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106 results on '"ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT"'

1. DiscoPG

Author: Angela Bonifati, Stefania Dumbrava, Emile Martinez, Fatemeh Ghasemi, Malo Jaffré, Pacôme Luton, Thomas Pickles, Université Claude Bernard Lyon 1 - Faculté des sciences (UCBL FS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Institut Polytechnique de Paris (IP Paris), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), and École normale supérieure de Lyon (ENS de Lyon)
Subjects: Graph databases, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Noeuds, General Engineering, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Property graphs, Graph applications
Abstract: Property graphs are becoming pervasive in a variety of graph processing applications using interconnected data. They allow to encode multi-labeled nodes and edges, as well as their properties, represented as key/value pairs. Although property graphs are widely used in several open-source and commercial graph databases, they lack a schema definition, unlike their relational counterparts. The property graph schema discovery problem consists of extracting the underlying schema concepts and types from such graph datasets. We showcase DiscoPG, a system for efficiently and accurately discovering and exploring property graph schemas. To this end, it leverages hierarchical clustering using a Gaussian Mixture Model, which accounts for both node labels and properties. DiscoPG allows users to perform schema discovery for both static and dynamic graph datasets. Suitable visualization layouts and dedicated dashboards enable the user perception of the static and dynamic inferred schema on the node clusters, as well as the differences in runtimes and clustering quality. To the best of our knowledge, DiscoPG is the first system to tackle the property graph schema discovery problem. As such, it supports the insightful exploration of the graph schema components and their evolving behavior, while revealing the underpinnings of the clustering-based discovery process.
Published: 2022
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2. Special section on Data processing in modern distributed architectures

Author: Darmont, Jérôme, Novikov, Boris, Wrembel, Robert, Bellatreche, Ladjel, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire d'Informatique et d'Automatique pour les Systèmes (LIAS), and Université de Poitiers-ENSMA
Subjects: Databases, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, GeneralLiterature_MISCELLANEOUS
Abstract: International audience; This editorial paper accompanies the best papers from the 24thEuropean Conference on Advances in Databases and Information Systems (ADBIS 2020), which was held virtually in September 2020 (originally planned to be held in Lyon, France).
Published: 2022
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3. Proceedings of the conference BDA'2021

Author: Pacitti, Esther, Miklos, Zoltan, GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2021

4. Towards Fully-fledged Archiving for RDF Datasets

Author: Olivier Pelgrin, Katja Hose, Luis Galárraga, Aalborg University [Denmark] (AAU), Large Scale Collaborative Data Mining (LACODAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Subjects: Information retrieval, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Knowledge bases, Computer Networks and Communications, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, [INFO.INFO-WB]Computer Science [cs]/Web, InformationSystems_DATABASEMANAGEMENT, 02 engineering and technology, computer.file_format, Computer Science Applications, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, RDF, RDF archiving, computer, Information Systems
Abstract: International audience; The dynamicity of RDF data has motivated the development of solutions for archiving, i.e., the task of storing and querying previous versions of an RDF dataset. Querying the history of a dataset finds applications in data maintenance and analytics. Notwithstanding the value of RDF archiving, the state of the art in this field is underdeveloped: (i) most existing systems are neither scalable nor easy to use, (ii) there is no standard way to query RDF archives, and (iii) solutions do not exploit the evolution patterns of real RDF data. On these grounds, this paper surveys the existing works in RDF archiving in order to characterize the gap between the state of the art and a fully-fledged solution. It also provides RDFev, a framework to study the dynamicity of RDF data. We use RDFev to study the evolution of YAGO, DBpedia, and Wikidata, three dynamic and prominent datasets on the Semantic Web. These insights set the ground for the sketch of a fully-fledged archiving solution for RDF data.
Published: 2021
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5. Prédire la présence de larves d'anophèles grâce à des algorithmes de classification et au réseaux de capteurs IoT

Author: Parkoo, Kodzo, Sarr, Cheikh, Gueye, Bamba, Université de Thiès, and Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)
Subjects: First-name-2 Family-name-2, Data prediction, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, IoT Sensors Network, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Data analysis, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Anti-larvae fight, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], First-name-k Family-name-k.. Classification algorithms, First-name-1 Family-name-1, Python
Abstract: Soumission à Episciences; The reduction of the malaria prevalence rate in Senegal, in addition to all the efforts made in the treatment and prevention of malaria, also involves an Anti-larvae program (ALP). In order to make ALP more effective, data from mosquito breeding sites and the environment of these sites have been collected. All of the stored data must be processed to produce predictive information. In order to obtain reliable results from the predictions, the choice of algorithms is crucial. The expected result is to find algorithms with the highest prediction reliability and to determine which of the parameters collected by the sensors are decisive for the presence of larvae.; La réduction du taux de prévalence du paludisme au Sénégal, au-delà de tous les efforts engagés dans le traitement et la prévention du paludisme, passe aussi par la lutte anti-larvaire (LAL). Dans l’optique de rendre la LAL plus efficace, des données issues des gîtes de moustiques, de l’environnement de ses différents gîtes ont été recueillies. L’ensemble des données stockées doivent être soumises à des traitements afin d’en sortir des informations prédictives. Afin d’obtenir des résultats fiables issues des prédictions, le choix des algorithmes est déterminant. Le résultat espéré est de parvenir à trouver des algorithmes avec des taux de fiabilité en prédiction les plus élevés et de déterminer parmi les paramètres recueillis par les capteurs, ceux qui sont déterminants pour la présence de larves.
Published: 2021

6. Découverte de motifs fréquents limités en espace-temps

Author: Borges, Heraldo, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CEFET-RJ, Rio de Janeiro, Brazil, Eduardo Ogasawara, Esther Pacitti, and Equipe associée Inria HPDASC
Subjects: motifs, Restricted Sequences, Spatial-Time Series, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Séries spatio-temporelles, séquences limitées
Abstract: Many phenomena can be observed and organized as a sequence of observations on a timeline that can be modeled as a time series. A relevant area that is being explored in time series analysis is pattern discovery. Patterns are subsequences of time series related to some special properties or behaviors. A particular pattern that occurs a significant number of times in time series is called motif. Several important phenomena of a time series present different behaviors when observed at points in space (for example, series collected by sensors and IoT) and are best modeled as space-time series. Each time series is associated with a position in space. A space-time pattern is a sequence of events that are limited in space and time. Finding patterns that are frequent and restricted in space and time can allow us to understand how a phenomenon occurs. Several works have been developed to identify motifs in time series. However, studies that address spatiotemporal data techniques have not been identified. In this thesis, we compare different approaches to identifying motifs in time series with their main differences. We propose two methods for automatically identifying space-time restricted motifs in space-time series, the CSA and CSTMP. We experimentally compare the pro- posed methods with two other alternative methods: the Matrix Profile technique and the ensemble of the Matrix Profile and DBScan techniques. Our results show that CSA and CSTMP are innovative and obtain results that outperform the state-of-the-art techniques for time series and their adaptations for space-time series, being evaluated in two datasets.; Tout phénomène peut être observé et organisé comme une séquence d'observations sur une ligne de temps qui peut être modélisée comme une série temporelle. Un domaine pertinent exploré dans l'analyse des séries temporelles est la découverte de modèles. Les modèles sont des sous-séquences de séries temporelles liées à des propriétés ou à des comportements particuliers. Un modèle particulier qui apparaît un nombre significatif de fois dans une série temporelle est appelé motif. Plusieurs phénomènes importants d'une série temporelle présentent des comportements différents lorsqu'ils sont observés à des points dans l'espace (par exemple, les séries collectées par des capteurs et l'IoT) et sont mieux modélisés en tant que séries spatio-temporelles. Chaque série temporelle est associée à une position dans l'espace. Un modèle spatio-temporel est une séquence d'événements limitée dans l'espace et le temps. Trouver des motifs fréquents et limités dans l'espace et le temps peut nous permettre de comprendre comment un phénomène se produit. Plusieurs travaux ont été développés pour identifier des motifs dans les séries temporelles. Cependant, les études portant sur les techniques de données spatio-temporelles n'ont pas été identifiées. Dans cette thèse, nous comparons différentes approches d'identification de motifs dans les séries temporelles avec leurs principales différences. Nous proposons deux méthodes pour identifier automatiquement les motifs restreints à l'espace-temps dans les séries spatio-temporelles, le CSA et le CSTMP. Nous comparons expérimentalement les méthodes proposées avec deux autres méthodes alternatives : la technique du profil matriciel et l'ensemble des techniques du profil matriciel et du DBScan. Nos résultats montrent que la CSA et la CSTMP sont innovantes et obtiennent des résultats qui surpassent les techniques de pointe pour les séries temporelles et leurs adaptations pour les séries spatio-temporelles, évaluées dans deux ensembles de données.
Published: 2021

7. GRASS GIS for topographic and geophysical mapping of the Peru-Chile Trench

Author: Polina Lemenkova, Schmidt United Institute of Physics of the Earth [Moscow] (IPE), and Russian Academy of Sciences [Moscow] (RAS)
Subjects: ACM: K.: Computing Milieux/K.7: THE COMPUTING PROFESSION, Geography, Planning and Development, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], ACM: D.: Software, ACM: K.: Computing Milieux, Seafloor spreading, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, EGM96, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDE]Environmental Sciences, Geology, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [SDU.STU.PE]Sciences of the Universe [physics]/Earth Sciences/Petrography, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Shuttle Radar Topography Mission, Volcanism, geoid, topography, ACM: H.: Information Systems, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Geoid, ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION, cartography, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Geomorphology, [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, Earth-Surface Processes, ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION/K.3.2: Computer and Information Science Education, [SDU.STU.TE]Sciences of the Universe [physics]/Earth Sciences/Tectonics, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], ACM: D.: Software/D.3: PROGRAMMING LANGUAGES, Elevation, 15. Life on land, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, gravity, Tectonics, ACM: D.: Software/D.1: PROGRAMMING TECHNIQUES, ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION/K.3.1: Computer Uses in Education, Thematic map, 13. Climate action, [SDU]Sciences of the Universe [physics], ACM: D.: Software/D.4: OPERATING SYSTEMS, GRASS GIS
Abstract: International audience; The study area is located along the western continental margins of South America, Peru-Chile Trench southeast Pacific Ocean, geographically encompasses 90° to 60°W longitude and 55°S to 0° latitude. The study aims to perform a spatial analysis using GRASS GIS approach applied for processing and visualizing topographic and geophysical data on the study area. Data include following raster grids: topographic SRTM_15PLUS raster grid with 15 arc-second resolution, geoid model (EGM96), geophysical fields and gravity maps (marine free-air gravity and vertically corrected free-air gravity). The thematic grids were mapped by GRASS GIS modules and visualized for comparative analysis. Spatial analysis included plotting slope aspect, profile curvature, terrain elevation and modeled topographic classes based on the neighborhood analysis. The results include visualized geophysical and topographic maps of the study area showing correlation between the geophysical fields and topographic elevation. The variety of forms of the submarine relief of the Pacific Ocean seafloor was formed as a result of complex factors: tectonic movements, dislocations, active volcanism, geologic variations in rock density, which is reflected in visualized gravity and geoid maps. The actuality of the study is explained by high potential commercial interests in deep-water mineral resource deposits, oil and gas, which make ocean seafloor studies one of the most promising topics in geoscience. At the same time, the need for programming applications for big data analysis in geosciences requires testing advanced scripting approaches in cartography. Therefore, the current paper presents a multidisciplinary approach combining geological analysis with technical cartographic aspects of data analysis and visualization.; Zona de studiu este localizată în marjele continentale vestice ale Americii de Sud, Groapa Peru-Chile în sud-estul Oceanului Pacific, cuprinde geografic 90° - 60° V longitudine și 55° S până la 0° latitudine. Scopul studiului este realizarea unei analize spațiale folosind abordarea GRASS GIS aplicată pentru prelucrarea și vizualizarea datelor topografice și geofizice din zona de studiu. Datele includ următoarele griduri raster: raster SRTM_15PLUS topografic cu rezoluție de 15 secunde în arc, model geoid (EGM96), câmpuri geofizice și hărți gravitaționale (gravitație aer liberă marină și gravitație aer liberă corectată vertical). Rețelele tematice au fost reprezentate cu GRASS GIS și vizualizate pentru analiză comparativă. Analiza spațială a inclus aspectul de pantă, curbura profilului, altitudinea terenului și clase topografice modelate pe baza analizei de vecinătate. Rezultatele includ hărți geofizice și topografice ale zonei de studiu care prezintă corelația între câmpurile geofizice și creșterea topografică. Varietatea formelor reliefului submarin al oceanului Pacific a fost configurată ca urmare a unor factori complexi: mișcări tectonice, discolații, vulcanism activ, variații geologice ale densității rocilor, care se reflectă în hărțile vizualizate ale gravitației și geoidelor. Actualitatea studiului este explicată de interesele comerciale potențiale ridicate în depozitele de la adâncime, petrol și gaze, ceea ce face ca studiile de pe malul oceanului să fie unul dintre cele mai promițătoare în direcțiile de geoștiință. În același timp, nevoia de programare a aplicațiilor pentru analiza datelor în geoștiințe necesită testarea abordărilor avansate de script în cartografie. Prin urmare, lucrarea actuală prezintă o abordare multidisciplinară, combinând analiza geologică cu aspecte cartografice tehnice ale analizei și vizualizării datelor.
Published: 2020
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8. Analysis of the difference in depths and variation in slope steepness of the Sunda Trench, Indonesia, east Indian Ocean

Author: Lemenkova, Polina, Schmidt United Institute of Physics of the Earth [Moscow] (IPE), and Russian Academy of Sciences [Moscow] (RAS)
Subjects: ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS, Bathymetry, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, mapping, Indian Ocean, computer.programming_language, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.1: Simulation Theory, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, GMT, geography.geographical_feature_category, Subduction, ACM: K.: Computing Milieux, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Trench, [SDE]Environmental Sciences, submarine geomorphology, Geology, ACM: I.: Computing Methodologies, geology, Java, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDE.MCG]Environmental Sciences/Global Changes, lcsh:G1-922, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Environmental Science (miscellaneous), Structural basin, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS, Paleontology, ACM: H.: Information Systems, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.5: Model Development, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, General Bathymetric Chart of the Oceans, cartography, [INFO]Computer Science [cs], 14. Life underwater, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, Computers in Earth Sciences, Transect, Oceanic trench, [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, Earth-Surface Processes, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.8: Types of Simulation, [SDU.STU.TE]Sciences of the Universe [physics]/Earth Sciences/Tectonics, geography, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], geomorphology, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, Sunda Trench, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, 13. Climate action, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], computer, lcsh:Geography (General)
Abstract: The paper discusses geomorphology of the Sunda Trench, an oceanic trench located in eastern Indian Ocean along the Sumatra and Java Islands of the Indonesian archipelago. In particular, it analysis the difference in depths and variation in slope steepness between the two segments of the trench: the southern Java transect (coordinates 108.8°E 10.10°S to 113.0°E 10.75°S) and the northern Sumatra transect (97.5°E 1.1°S to 101.0°E 5.5°S). The thematic maps and geomorphological modelling were plotted using Generic Mapping Tools (GMT). The materials include high-resolution data on topography, geology and geophysics: GEBCO 15 arc-minute resolution grid, EGM2008 2.5 minute Earth Gravitation Model of 2008, GlobSed global 5‐arc‐minute total sediment thickness and vector geological datasets. In addition to the GEBCO-based bathymetric data, geological, topographic and geophysical maps, the results include enlarged transects for the Java and Sumatra segments, their slope gradients and cross-section profiles, derived from the bathymetric GEBCO dataset. The geomorphology framework of the Sunda Trench is largely controlled by the subduction of the Australian plate underneath the Sunda microplate. The geological processes take place in basin of the Indian Ocean at different stages of its evolution and influence the nature of the submarine geomorphology and geometric shape of the trench. Sunda Trench is seismically active part of the Pacific Ring of Fire. A large number of the catastrophic earthquakes are recorded around the trench. The histograms shows variation in depths along the segments of the Sumatra and Java. The Java segment has a bell-shaped data distribution in contrast to the Sumatra with bimodal pattern. The Java segment has the most repetitive depths at -2,500 to -5,200 m. The Sumatra transect has two peaks: 1) a classic bell-shaped peak at depths -4,500 m to -5,500 m; 2) shelf area with a peak from 0 to -1,750 m. The data at middle depths (-1,750 to -4,500 m) have a frequency -6,000 m, there are only 138 samples for the Sumatra while 547 samples for Java. Furthermore, Java segment has more symmetrical geometric shape while Sumatra segment is asymmetric, one-sided. The Sumatra segment has a steepness of 57.86° on its eastern side (facing Sumatra Island) and a contrasting 14.58° on the western part. The Java segment has a steepness of 64.34° on its northern side (facing Java Island) and 24.95° on the southern part (facing Indian Ocean). The paper contributes to the studies of the submarine geomorphology in Indonesia.
Published: 2020
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9. GRASS GIS Modules for Topographic and Geophysical Analysis of the ETOPO1 DEM and Raster Data: North Fiji Basin, Pacific Ocean

Author: Lemenkova, Polina, Schmidt United Institute of Physics of the Earth [Moscow] (IPE), and Russian Academy of Sciences [Moscow] (RAS)
Subjects: ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities/I.3.4.0: Application packages, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, bathymetry, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.3: Picture/Image Generation, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.6: Methodology and Techniques/I.3.6.2: Graphics data structures and data types, topography, ACM: H.: Information Systems, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.6: Methodology and Techniques, Fiji, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling/I.3.5.2: Curve, surface, solid, and object representations, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities, [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology, profile, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES/H.1.2: User/Machine Systems, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.STU.TE]Sciences of the Universe [physics]/Earth Sciences/Tectonics, Pacific Ocean, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities/I.3.4.1: Device drivers, [SDE.IE]Environmental Sciences/Environmental Engineering, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities/I.3.4.3: Graphics packages, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling/I.3.5.5: Modeling packages, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.2: Graphics Systems, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.6: Methodology and Techniques/I.3.6.4: Languages, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities/I.3.4.5: Paint systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.6: Methodology and Techniques/I.3.6.3: Interaction techniques, [SDE]Environmental Sciences, GRASS GIS, transect, ACM: I.: Computing Methodologies
Abstract: International audience; GRASS GIS Modules for Topographic and Geophysical Analysis of the ETOPO1 DEM and Raster Data: North Fiji Basin, Pacific Ocean The paper presents topographic analysis based on the raster data (NetCDF and grid formats) and visualization of the geophysical datasets that resembles the topographic surface. Geographically, the research focuses on the region of North Fiji Basin, South Pacific Ocean. North Fiji Basin is one of the marginal basins located at the converging boundary between the Pacific and Australian tectonic plates and is notable for complex geological settings. Methodology is based on GRASS GIS and includes scripting and cartographic visualization. Data include ETOPO1, GEBCO, EGM96 gravity and geoid raster grids imported to GRASS GIS via GDAL library ('r.in.gdal' module) from NetCDF and GRD formats, evaluated and visualized. Several GRASS modules were used as a sequential scripting for displaying and modelling data. Geomorphometric analysis (slopes, curvature, aspect, elevation) was performed by module 'r.slope.aspect' based on ETOPO1 grid. Statistical data analysis (histograms, polar diagram) for topographic and geoid gravitational fields were visualized by combination of GRASS modules d.rast, g.region, d.polar, d.histogram, d.grid, d.legend, d.text. Topographic analysis reveal that free-air gravity anomaly mark the continental shelf-slope border showing that generally, geophysical settings are dominated by the topographic effect. These observations are consistent with the inferred prominent role of the geophysical settings. The histograms reveal that topography (trenches, abyssal plains, shelf areas) has values between-5,000 and-1,000 m, and a clear peak with major values between 100 and 350 m above sea level. This distribution indicates that dominating depths of the ocean floor in North Fiji Basin are between-1,500 to-3,000 m. The aspect map of the ETOPO1 initial raw raster grid show that the surface has mostly 50°-110° oriented slope followed by data range in 250°-300° oriented slopes which is well correlating with submarine topography and general relief forms. The steepening of the gravity values between 50 and 55 mGal and 58 to 63 mGal indicates geoid undulations suggesting that bathymetry is controlled by the geological settings in the study area. Consolebased scripts of GRASS GIS are presented together with cartographic outputs showing its effectiveness for the geographic spatial analysis.
Published: 2020
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10. Actes de la conférence BDA 2020

Author: Amann, Bernd, Goasdoué, François, Bases de Données (BD), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), A Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2020

11. Data lakes for digital humanities

Author: Jérôme Darmont, Cécile Favre, Camille Noûs, Sabine Loudcher, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, and Laboratoire Cogitamus
Subjects: FOS: Computer and information sciences, Computer science, Process (engineering), Big data, Database administrator, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Digital Humanities, Computer Science - Databases, Digital humanities, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Database administration, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Data lakes, Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Lemmatisation, Databases (cs.DB), Data science, Variety (cybernetics), Information integration, 010201 computation theory & mathematics, • Information systems → Data management systems, • Digital Humanities, business, • Applied computing
Abstract: Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully master this process, we propose the use of data lakes as a solution to data siloing and big data variety problems. We describe data lake projects we currently run in close collaboration with researchers in humanities and social sciences and discuss the lessons learned running these projects., Comment: Data and Digital Humanities Track
Published: 2020
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12. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium: International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020

Author: Bellatreche, Ladjel, Bieliková, Mária, Boussaïd, Omar, Catania, Barbara, Darmont, Jérôme, Demidova, Elena, Duchateau, Fabien, Hall, Mark, Merčun, Tanja, Novikov, Boris, Papatheodorou, Christos, Risse, Thomas, Romero, Oscar, Sautot, Lucile, Talens, Guilaine, Wrembel, Robert, Žumer, Maja, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Database, Big Data, Business Intelligence, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, Digital Libraries, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.7: Digital Libraries, Information Systems
Abstract: International audience; This volume contains a selection of the papers presented at all Workshops and DoctoralConsortium of the 24th East-European Conference on Advances in Databases andInformation Systems (ADBIS 2020), the 24th International Conference on Theory andPractice of Digital Libraries (TPDL 2020), and the 16th Workshop on BusinessIntelligence and Big Data (EDA 2020), held during August 25–27, 2020, in Lyon,France.
Published: 2020
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13. New Trends in Databases and Information Systems

Author: Darmont, Jérôme, Novikov, Boris, Wrembel, Robert, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon
Subjects: Databases, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, Informaton Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT
Abstract: International audience; The 24th European Conference on Advances in Databases and Information Systems(ADBIS 2020) was set to be held in Lyon, France, during August 25–28, 2020, inconjunction with the 24th International Conference on Theory and Practice of DigitalLibraries (TPDL 2020) and the 16th EDA days on Business Intelligence & Big Data(EDA 2020). However, because of the worldwide COVID-19 crisis, ADBIS, TPDL,and EDA had to take place online during August 25–27, 2020. Yet, the three con-ferences joined their forces to propose common keynotes, workshops, and a DoctoralConsortium.
Published: 2020
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14. 24th European Conference on Advances in Databases and Information Systems (ADBIS 2020)

Author: Darmont, Jérôme, Novikov, Boris, Wrembel, Robert, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, National Research University, HigherSchool of Economics, and Poznan University of Technology (PUT)
Subjects: Databases, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Information Systems
Abstract: International audience; The 24th European Conference on Advances in Databases and Information Systems(ADBIS 2020) was set to be held in Lyon, France, during August 25–28, 2020, inconjunction with the 24th International Conference on Theory and Practice of DigitalLibraries (TPDL 2020) and the 16th EDA days on Business Intelligence & Big Data(EDA 2020). However, because of the worldwide COVID-19 crisis, ADBIS, TPDL,and EDA had to take place online during August 25–27, 2020. Yet, the three con-ferences joined their forces to propose common keynotes, workshops, and a DoctoralConsortium.
Published: 2020
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15. Metadata model for an archeological data lake

Author: Liu, Pengfei, Loudcher, Sabine, Darmont, Jérôme, Perrin, Emmanuelle, Girard, Jean-Pierre, Rousset, Marie-Odile, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, ARCHEORIENT - Environnements et sociétés de l'Orient ancien (Archéorient), Université Lumière - Lyon 2 (UL2)-Centre National de la Recherche Scientifique (CNRS), LabEx IMU Lyon, Alliance of Digital Humanities Organizations (ADHO), and HyperTheseau
Subjects: Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Archaeology, Thesaurus, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Data lake, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases
Abstract: References- Alrehamy H. et Walker C. Personal Data Lake With Data Gravity Pull. 5th International Conference on Big Data and Cloud Computing(BDCloud 2015), Dalian, China, Volume 88 of IEEE Computer Society Washington, pp. 160–167. 2015.- De Luca L. et al., Patrimonialisation du numérique et numérisation du patrimoine, regards croisés. MICNRS, 2016. Consultable à l’adresse : www.cnrs.fr/mi/IMG/pdf/synthesepatrimoine_numeriquevf.pdf- Dixon J. Pentaho, Hadoop, and Data Lakes. 2010. Consultable à l’adresse :https ://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/ - Hai R., Geisler S. et Quix C. Constance : An Intelligent Data Lake System. International Conference on Management of Data (SIGMOD). San Francisco, USA, ACM Digital Library, pp. 2097–2100. 2016.- Sawadogo P., Scholly E., Favre C., Ferey E., Loudcher S., Darmont J. Metadata Systems for Data Lakes: Models and Features. 1st International Workshop on BI and Big Data Applications (BBIGAP@ADBIS 2019), Sept 2019, Bled, Slovenia. pp.440-451. 2019.; International audience; The HyperThesau project was initiated by a multidisciplinary team consisting of two research laboratories of archaeology and computer science, respectively, a digital library, two archeological museums and a private company. This project has two main objectives: 1) the design and implementation of an integrated platform to host, search, share and analyze archaeological data; 2) the design of a domain-specific thesaurus taking the whole archaeological data lifecycle into account, from data creation to publication.Archeological data may bear many different types, e.g., textual documents, images (photos, drawings...), sensor data, etc. Moreover, similar documents, e.g., excavation reports, are often created by various software tools that are not compatible with each other. The description of an archaeological object also differs with respect to users, usages and time (De Luca et al., 2016). Such variety of archeological data induces many scientific challenges related to storing heterogeneous data in a centralized repository, guaranteeing data quality, cleaning and transforming the data to make them interoperable, finding and accessing data efficiently and cross-analyzing the data by taking their spatial and temporal dimensions into account. To overcome all these challenges, we exploit the concept of data lake (Dixon, 2010). A data lake is a centralized repository that helps store all types of data without having any predefined structure (Hai et al., 2016). To avoid a data lake turning into an inoperable data swamp, an efficient metadata management system is essential to catalogue, search and access the data (Alrehamy & Walker, 2015; Hai et al., 2016). Note that we do not propose a new archaeological data model nor new data formats for data archiving here. Our approach aims to collect all types of archaeological data, save them inside a data lake and propose metadata for better organizing data and for allowing users to easily find data for analysis purposes. In this article, we first present a data lake prototype that is architectured in nine layers such as data ingestion, data storage, data application, data governance, data security, etc. Each layer is implemented with one or more frameworks of the Hadoop ecosystem, e.g., Atlas, HDFS, HIVE, OpenLdap, Spark, etc. This prototype is operational and currently hosts the data of two archeological research facilities. Secondly, we present our metadata management system and the metadata model we use to manage archeological data inside the data lake. We actually instantiate the MEtadata model for Data Lakes (MEDAL), which adopts a graph model (Sawadogo et al., 2019). Eventually, our metadata management system is implemented with Apache Atlas, which can host not only descriptive metadata, but also several thesauruses. With the help of a search engine, i.e., Solr, users can find data through descriptive metadata, a thesaurus or the data lineage.
Published: 2020

16. Oraclised Data Schemas: Improving contractual Certainty in uncertain Times

Author: Roche, Niall, P Moore, Alastair, UCL Centre for Blockchain Technologies, and London University
Subjects: ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published: 2020

17. The Glycine Receptor Allosteric Ligands Library (GRALL)

Author: Adrien Henri Cerdan, Marion Sisquellas, Diego Enry Barreto Gomes, Gilberto P Pereira, Jean-Pierre Changeux, Marco Cecchini, Institut de Chimie de Strasbourg, Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO), Kavli Institute for Brain and Mind [San Diego], University of California [San Diego] (UC San Diego), University of California-University of California, Collège de France (CdF (institution)), This work has received funding from the French National Research Agency (ANR-18-CE11-0015) and the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). G.P. received support from the Ecole Doctorale des Sciences Chimiques ED-222., ANR-18-CE11-0015,PENTA_CONTROL,Étude de la dynamique structurale d'un canal ionique pentamérique pour la conception rationnelle des médicaments(2018), European Project: 785907,H2020,HBP SGA2(2018), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Neurobiologie intégrative des Systèmes cholinergiques, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), and University of California (UC)-University of California (UC)
Subjects: Statistics and Probability, In silico, Allosteric regulation, Cooperativity, Computational biology, Ligands, Biochemistry, Synaptic Transmission, 03 medical and health sciences, 0302 clinical medicine, Receptors, Glycine, Allosteric Regulation, Radioligand, Homomeric, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Binding site, Molecular Biology, Glycine receptor, 030304 developmental biology, Gene Library, 0303 health sciences, Binding Sites, Drug discovery, Chemistry, Original Papers, Structural Bioinformatics, 3. Good health, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery
Abstract: Motivation Glycine receptors (GlyRs) mediate fast inhibitory neurotransmission in the brain and have been recognized as key pharmacological targets for pain. A large number of chemically diverse compounds that are able to modulate GlyR function both positively and negatively have been reported, which provides useful information for the development of pharmacological strategies and models for the allosteric modulation of these ion channels. Results Based on existing literature, we have collected 218 unique chemical entities with documented modulatory activities at homomeric GlyR-α1 and -α3 and built a database named GRALL. This collection includes agonists, antagonists, positive and negative allosteric modulators and a number of experimentally inactive compounds. Most importantly, for a large fraction of them a structural annotation based on their putative binding site on the receptor is provided. This type of annotation, which is currently missing in other drug banks, along with the availability of cooperativity factors from radioligand displacement experiments are expected to improve the predictivity of in silico methodologies for allosteric drug discovery and boost the development of conformation-based pharmacological approaches. Availability and implementation The GRALL library is distributed as a web-accessible database at the following link: https://ifm.chimie.unistra.fr/grall. For each molecular entry, it provides information on the chemical structure, the ligand-binding site, the direction of modulation, the potency, the 3D molecular structure and quantum-mechanical charges as determined by our in-house pipeline. Contact mcecchini@unistra.fr Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2020
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18. Post-actes BDA 2019 -Gestion de Données Principes Technologies et Applications

Author: Zeitouni, Karine, Lamarre, Philippe, Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

19. Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud

Author: Luis Pineda, Alexandru Costan, Ji Liu, Patrick Valduriez, Gabriel Antoniu, Marta Mattoso, Esther Pacitti, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Scalable Storage for Clouds and Beyond (KerData), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro (UFRJ), MUSIC Inria Associated Team, Z-Cloudflow MSR - Inria Project, ANR OverFlow project, MCTI/RNP-Brazil, CNPq, FAPERJ, Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), COPPE - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro [Rio de Janeiro] (UFRJ), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Pierre Senellart, Amedeo Napoli
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Distributed database, Computer science, business.industry, Distributed computing, Cloud computing, 02 engineering and technology, Computer Science Applications, Scheduling (computing), Metadata, Workflow, Computational Theory and Mathematics, 020204 information systems, Server, Metadata management, 0202 electrical engineering, electronic engineering, information engineering, Task analysis, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Workflow management system, Information Systems
Abstract: National audience; Large-scale scientific applications are often expressed as scientificworkflows (SWfs) that help defining data processing jobs and dependenciesbetween jobs’ activities. Several SWfs have huge storageand computation requirements, and so they need to be processedin multiple (cloud-federated) datacenters. It has been shown thatefficient metadata handling plays a key role in the performance ofcomputing systems. However, most of this evidence concern onlysingle-site, HPC systems to date. In addition, the efficient schedulingof tasks among different data centers is critical to the SWfexecution. In this paper, we present a hybrid distributed model andarchitecture, using hot metadata (frequently accessed metadata)for efficient SWf scheduling in a multisite cloud. We couple ourmodel with a scientific workflow management system (SWfMS)to validate and tune its applicability to different real-life scientificworkflows with different scheduling algorithms. We show that thecombination of efficient management of hot metadata and schedulingalgorithms improves the performance of SWfMS, reducing theexecution time of highly parallel jobs up to 64.1% and that of thewhole scientific workflows up to 37.5%, by avoiding unnecessarycold metadata operations.; Les applications scientifiques à grande échelle sont souvent exprimées sous forme de workflows scientifiques(SWfs) qui aident à définir les jobs de traitement des données et les dépendances entre les activités des jobs.Certains SWfs nécessitent une très grande quantité de stockage et de calcul, ce qui peut être obtenu en exploitantplusieurs data centers dans un cloud. Dans ce contexte, la gestion des métadonnées et l’ordonnancement destâches entre différents data centers deviennent critiques pour l’exécution efficace de SWf. Dans cet article, nousproposons une architecture et un modèle distribués hybrides, en utilisant les métadonnées chaudes (fréquemmentconsultées) pour l’ordonnancement efficace de SWf dans un cloud multisite. Nous utilisons notre modèle dansun système de gestion de workflows scientifiques (SWfMS) pour valider et régler son applicabilité à différentsworkflows scientifiques réels avec différents algorithmes d’ordonnancement. Nous montrons que la combinaisond'une gestion efficace des métadonnées chaudes et des algorithmes d’ordonnancement améliore les performancesdu SWfMS. En évitant les opérations inutiles de métadonnées froides, le temps d'exécution des jobs quis’exécutent en parallèle est réduit jusqu'à 64,1% et celui de l'ensemble des workflows scientifiques jusqu'à37,5%.
Published: 2019
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20. The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes

Author: Torben Bach Pedersen, Elena Apostol, Ciprian-Octavian Truica, Jérôme Darmont, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon
Subjects: FOS: Computer and information sciences, Document-Oriented Database Management Systems, Information Systems and Management, XML Database Management Systems, computer.internet_protocol, Relational database, Computer science, H.2, 02 engineering and technology, Benchmark, computer.software_genre, NoSQL, Management Information Systems, Computer Science - Databases, 020204 information systems, Schema (psychology), 0202 electrical engineering, electronic engineering, information engineering, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, XPath, computer.programming_language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Information retrieval, Document-oriented database, Databases (cs.DB), Data structure, JSON, Computer Science Applications, XQuery, XML database, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, computer, XML, JSON Database Management Systems, Information Systems
Abstract: In the current context of Big Data, a multitude of new NoSQL solutions for storing, managing, and extracting information and patterns from semi-structured data have been proposed and implemented. These solutions were developed to relieve the issue of rigid data structures present in relational databases, by introducing semi-structured and flexible schema design. As current data generated by different sources and devices, especially from IoT sensors and actuators, use either XML or JSON format, depending on the application, database technologies that store and query semi-structured data in XML format are needed. Thus, Native XML Databases, which were initially designed to manipulate XML data using standardized querying languages, i.e., XQuery and XPath, were rebranded as NoSQL Document-Oriented Databases Systems. Currently, the majority of these solutions have been replaced with the more modern JSON based Database Management Systems. However, we believe that XML-based solutions can still deliver performance in executing complex queries on heterogeneous collections. Unfortunately nowadays, research lacks a clear comparison of the scalability and performance for database technologies that store and query documents in XML versus the more modern JSON format. Moreover, to the best of our knowledge, there are no Big Data-compliant benchmarks for such database technologies. In this paper, we present a comparison for selected Document-Oriented Database Systems that either use the XML format to encode documents, i.e., BaseX, eXist-db, and Sedna, or the JSON format, i.e., MongoDB, CouchDB, and Couchbase. To underline the performance differences we also propose a benchmark that uses a heterogeneous complex schema on a large DBLP corpus., Comment: 28 pages, 6 figures, 7 tables
Published: 2021
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21. Actes de la conférence BDA 2018

Author: Florea, Adina Magda, Vodislav, Dan, Université Polytechnique de Bucarest (UPB), Centre PREMINV, Université de Cergy Pontoise (UCP), and Université Paris-Seine
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

22. Answering Provenance-Aware Queries on RDF Data Cubes under Memory Budgets

Author: Galárraga, Luis, Ahlstrøm, Kim, Hose, Katja, Pedersen, Torben, Large Scale Collaborative Data Mining (LACODAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Aalborg University [Denmark] (AAU), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-WB]Computer Science [cs]/Web, InformationSystems_DATABASEMANAGEMENT, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT
Abstract: International audience; The steadily-growing popularity of semantic data on the Web and the support for aggregation queries in SPARQL 1.1 have propelled the interest in Online Analytical Processing (OLAP) and data cubes in RDF. Query processing in such settings is challenging because SPARQL OLAP queries usually contain many triple patterns with grouping and aggregation. Moreover, one important factor of query answering on Web data is its provenance, i.e., metadata about its origin. Some applications in data analytics and access control require to augment the data with provenance metadata and run queries that impose constraints on this provenance. This task is called provenance-aware query answering. In this paper, we investigate the benefit of caching some parts of an RDF cube augmented with provenance information when answering provenance-aware SPARQL queries. We propose provenance-aware caching (PAC), a caching approach based on a provenance-aware partitioning of RDF graphs, and a benefit model for RDF cubes and SPARQL queries with aggregation. Our results on real and synthetic data show that PAC outperforms significantly the LRU strategy (least recently used) and the Jena TDB native caching in terms of hit-rate and response time.; La croissante popularité des données sémantiques sur le Web et la prise en charge des requêtes d'agrégation dans SPARQL 1.1 ont suscité l'intérêt pour le traitement analytique en ligne (OLAP) et les cubes de données en RDF. Le traitement des requêtes dans de tels scenarios est difficile, car les requêtes OLAP SPARQL contiennent généralement de nombreux modèles triples avec regroupement et agrégation. De plus, un facteur important de réponse à une requête sur des données Web est sa provenance, c’est-à-dire des métadonnées sur son origine. Certaines applications d’analyse de données et de contrôle d’accès nécessitent d’augmenter les données avec des métadonnées de provenance et d’exécuter des requêtes qui imposent des contraintes à cette provenance. Cette tâche est appelée réponse à une requête sensible à la provenance. Dans cet article, nous étudions les avantages de la mise en cache de certaines parties d’un cube RDF complétées par des informations de provenance lors de la réponse à des requêtes SPARQL tenant compte de la provenance. Nous proposons une mise en cache basée sur la provenance (PAC), une approche de mise en cache basée sur un partitionnement des graphes RDF tenant compte de la provenance, ainsi qu'un modèle d'avantages pour les cubes RDF et les requêtes SPARQL avec agrégation. Nos résultats sur des données réelles et synthétiques montrent que PAC dépasse de manière significative la stratégie LRU (la moins récemment utilisée) et la mise en cache native Jena TDB en termes de taux d’accès et de temps de réponse.
Published: 2018
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23. Handling Uncertainty in Relational Databases with Possibility Theory - A Survey of Different Modelings

Author: Henri Prade, Olivier Pivert, GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Davide Ciucci, Gabriella Pasi, Barbara Vantaggi, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche en Informatique et en Automatique - INRIA (FRANCE), Institut National des Sciences Appliquées de Rennes - INSA (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Institut Mines-Télécom (FRANCE), Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université de Bretagne Sud - UBS (FRANCE), Université de Rennes 1 (FRANCE), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Subjects: Possibility theory, Theoretical computer science, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Relational database, Computer science, Probabilistic logic, Uncertainty, Duality (optimization), 02 engineering and technology, Intelligence artificielle, Inconsistency, Database design, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data cleaning, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Uncertainty modeling, 020201 artificial intelligence & image processing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Tuple, Uncertainty quantification, Relational databases, ComputingMilieux_MISCELLANEOUS
Abstract: International audience; Mainstream approaches to uncertainty modeling in relational databases are probabilistic. Still some researchers persist in proposing representations based on possibility theory. They are motivated by the ability of this latter setting for modeling epistemic uncertainty and by its qualitative nature. Interestingly enough, several possibilistic models have been proposed over time, and have been motivated by different application needs ranging from database querying, to database design and to data cleaning. Thus, one may distinguish between four different frameworks ordered here according to an increasing representation power: databases with (i) layered tuples; (ii) certainty-qualified attribute values; (iii) attribute values restricted by general possibility distributions; (iv) possibilistic c-tables. In each case, we discuss the role of the possibility-necessity duality, the limitations and the benefit of the representation settings, and their suitability with respect to different tasks.
Published: 2018
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24. Computing query sets for better exploring raw data collections

Author: Vargas-Solar, Genoveva, Zechinelli-Martini, José-Luis, Espinosa-Oviedo, Javier-Alfonso, Heterogeneous and Adaptive distributed DAta management Systems (HADAS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Franco-Mexicain d'Informatique et d'Automatique (LAFMIA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV)-Université de Technologie de Compiègne (UTC)-Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Universidad de las Américas [Puebla] (UDLAP)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], big data, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ontologies, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases, query rewriting, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.2: Query formulation
Abstract: International audience; This paper proposes an approach for helping data scientists express queries that can help them understand the content of raw data collections. The proposal is based on query rewriting techniques that given an initial query they can provide sets of queries that can help data scientists to better exploit data collections. We apply our approach in the context of the project Exploring the History of Climate Change in Latin America through Newspaper Articles funded by the Panamerican Institute of Geography and History (PAIGH) where data scientists explore newspapers to track articles reporting climatologic events.
Published: 2018

25. Computation of PDFs on Big Spatial Data: Problem & Architecture

Author: Liu, J., Lemus, N. M., Pacitti, E., Fabio Porto, Valduriez, P., Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), SciDISC Inria associated team with Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; Big spatial data can be produced by observation or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area, which can be very time consuming. In this paper, we analyze the problem and discuss the use of Spark to efficiently compute PDFs.
Published: 2018

26. Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project

Author: Valduriez, P., Mattoso, M., Akbarinia, R., Borges, H., Camata, J., Coutinho, A., Gaspar, D., Lemus, N., Liu, J., Lustosa, H., Masseglia, F., Silva, F. N. D., Silva, V., Souza, R., Ocaña, K., Eduardo Ogasawara, Oliveira, D. D., Pacitti, E., Porto, F., Shasha, D., Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro (UFRJ), Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), SciDISC Inria associated team with Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), COPPE - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro [Rio de Janeiro] (UFRJ), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Big data, Scalable Data-Intensive Computing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], HPC, Scientific data, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC), as exemplified by frameworks such as Hadoop and Spark. In this context, the SciDISC project addresses the grand challenge of scientific data analysis using DISC, by developing architectures and methods to combine simulation and data analysis. SciDISC is an ongoing project between Inria, several research institutions in Rio de Janeiro and NYU. This paper introduces the motivations and objectives of the project, and reports on the first results achieved so far.
Published: 2018

27. Constellation Queries over Big Data

Author: Porto, Fábio, Khatibi, Amir, Rittmeyer, Joao, Ogasawara, Eduardo, Valduriez, Patrick, Shasha, Dennis, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), SBC, SciDISC Inria associated team with Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: FOS: Computer and information sciences, Big data, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer Science - Databases, H.2.4, H.2.8, H.3.1, Databases (cs.DB), ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Pattern search
Abstract: International audience; A geometrical pattern is a set of points with all pairwise distances (or, more generally, relative distances) specified. Finding matches to such patterns has applications to spatial data in seismic, astronomical, and transportation contexts. Finding geometric patterns is a challenging problem as the potential number of sets of elements that compose shapes is exponentially large in the size of the dataset and the pattern. In this paper, we propose algorithms to find patterns in large data applications. Our methods combine quadtrees, matrix multiplication, and bucket join processing to discover sets of points that match a geometric pattern within some additive factor on the pairwise distances. Our distributed experiments show that the choice of composition algorithm (matrix multiplication or nested loops) depends on the freedom introduced in the query geometry through the distance additive factor. Three clearly identified blocks of threshold values guide the choice of the best composition algorithm.; Um padra ̃o geome ́trico e ́ definido por um conjunto de pontos e todos os pares de distaˆncias entre estes pontos. Encontrar casamentos de padro ̃es geome ́tricos em datasets tem aplicac ̧o ̃es na astronomia, na pesquisa s ́ısmica e no desenho de a ́reas urbanas. A soluc ̧a ̃o do problema impo ̃e um grande desafio, considerando-se o nu ́mero exponencial de candidatos, potencialmente func ̧a ̃o do nu ́mero de elementos no dataset e nu ́mero de pontos na forma geome ́trica. O me ́todo aqui apresentado inclui: quadtrees,multiplicac ̧a ̃o de matrizes e junc ̧o ̃es espaciais para encontrar conjuntos de pontos que se aproximem do padra ̃o fornecido, com um erro admiss ́ıvel. Apresentamos uma implementac ̧a ̃o dis- tribu ́ıda reveladora de que a escolha do algoritmo (multiplicac ̧a ̃o de matrizes ou junc ̧o ̃es espaciais) depende da liberdade introduzida por um fator de erro adi- tivo na geometria do padra ̃o. Identificamos treˆs regio ̃es baseadas nos valores de erro tolerados que determinam a escolha do algoritmo.
Published: 2018

28. DfAnalyzer: Runtime Dataflow Analysis of Scientific Applications using Provenance

Author: Vítor Silva, Daniel de Oliveira, Patrick Valduriez, Marta Mattoso, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro (UFRJ), Fluminense Federal University [Niterói], Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), SciDISC Inria associated team with Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, business.industry, Dataflow, media_common.quotation_subject, General Engineering, 02 engineering and technology, computer.software_genre, Domain (software engineering), Visualization, SPARK (programming language), Debugging, Scripting language, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), 020201 artificial intelligence & image processing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Software engineering, business, computer, computer.programming_language, media_common
Abstract: International audience; We present DfAnalyzer, a tool that enables monitoring, debugging, steering, and analysis of dataflows while being generated by scientific applications. It works by capturing strategic domain data, registering provenance and execution data to enable queries at runtime. DfAnalyzer provides lightweight dataflow monitoring components to be invoked by high performance applications. It can be plugged in scripts, or Spark applications, in the same way users already plug visualization library components. During this demo, we will show how DfAnalyzer captures the dataflow, provenance, as well as how it provides runtime data analyses of applications. We will also encourage attendees to use DfAnalyzer for their own applications. PVLDB Reference Format: Vítor Silva, Daniel de Oliveira, Patrick Valduriez, Marta Mattoso. Word Template for VLDB Conferences. PVLDB, 11(5): xxxx-2018, 2018. DOI: https://doi.org/TBD
Published: 2018
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29. Fuzzy Preference Queries to NoSQL Graph Databases

Author: Virginie Thion, Arnaud Castelltort, Olfa Slama, Anne Laurent, Olivier Pivert, WEB-CUBE, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Symbolic and Human-centric view of dAta MANagement (SHAMAN), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Olivier Pivert, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), WEB Architecture x Semantic WEB x WEB of Data (WEB3), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cypher query language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Theoretical computer science, Graph database, Computer science, Fuzzy set, InformationSystems_DATABASEMANAGEMENT, 02 engineering and technology, Scientific literature, Query language, NoSQL, computer.software_genre, Fuzzy logic, 020204 information systems, Management system, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Fuzzy set theory, computer, Implementation challenges, NoSQL graph databases
Abstract: International audience; This chapter presents a framework that makes it possible to introduce fuzzy preferences in queries over graph databases. It discusses implementation issues for the implementation of such a framework. The chapter introduces the notion of graph databases and preliminary notions concerning fuzzy set theory. It also presents the contributions of the scientific literature that allow for a flexible querying of crisp and fuzzy graph databases. These contributions choose the same approach which consists in extending a query language in order to introduce fuzzy preferences in graph pattern queries. Both these approaches consider the extension of the Cypher query language implemented in the Neo4j graph database management system. The chapter then presents a pioneer work that extends the Cypher language in order to express fuzzy preference queries over attributes, nodes and relationships in order to query a crisp graph database.
Published: 2018
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30. Análise de dados científicos sobre múltiplas fontes de dados ao longo da execução de simulações computacionais

Author: Silva, Vitor, Universidade Federal do Rio de Janeiro (UFRJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universidade Federal de Rio de Janeiro, Marta Mattoso, Daniel de Oliveira, and Patrick Valduriez(Patrick.Valduriez@inria.fr)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Gestion de données scientifiques, Scientific workflow management, Data analysis, Scientific data management, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT
Abstract: Large-scale computational simulations are characterized by the chaining of programs that execute increasingly complex computational models. Much of the data produced by these programs need to be analyzed by scientific domain users to validate their scientific hypotheses. However, it is not trivial since other programs must be developed to access and to capture these scientific data. In many cases, users also need to relate data produced by different simulation programs. This thesis proposes an approach that monitors, debugs, and analyzes the data element flow produced by different simulation programs. We also propose a component-based architecture, named as ARMFUL, to extract and relate scientific data generated in these several simulation steps considering a dataflow abstraction and techniques for scientific data capture. ARMFUL’s components can be instantiated on a scientific workflow system (e.g., A-Chiron) or a library of components (e.g., DfAnalyzer). We evaluate these instances using simulations in high performance computing environments. In our experimental results, our approach introduced a negligible overhead of the simulation execution time, and we perform complex queries to the scientific data.; Les simulations numériques à grande échelle se caractérisent par l'enchaînement de programmes qui exécutent des modèles informatiques de plus en plus complexes. Une grande partie des données produites par ces programmes doivent être analysées par les utilisateurs du domaine scientifique pour valider leurs hypothèses scientifiques. Cependant, ce n'est pas anodin puisque d'autres programmes doivent être développés pour accéder à ces données scientifiques et les saisir. Dans de nombreux cas, les utilisateurs ont également besoin de relier les données produites par différents programmes de simulation. Cette thèse propose une approche qui surveille, débogue et analyse le flux d'éléments de données produits par différents programmes de simulation. Nous proposons également une architecture basée sur les composants, appelée ARMFUL, pour extraire et relier les données scientifiques générées dans ces différentes étapes de simulation, en tenant compte d'un flux de données abstraites et des techniques de capture de données scientifiques. Les composants de ARMFUL peuvent être instanciés sur un système de flux de travail scientifique (par exemple, A-Chiron) ou une bibliothèque de composants (par exemple, DfAnalyzer). Nous évaluons ces cas à l'aide de simulations dans des environnements informatiques à haute performance. Dans nos résultats expérimentaux, notre approche a introduit une surcharge négligeable du temps d'exécution de la simulation, et nous effectuons des requêtes complexes aux données scientifiques.; Simulações computacionais em larga escala são caracterizadas pelo encadeamento de programas que executam modelos computacionais cada vez mais complexos. Muitos dos dados produzidos por esses programas precisam ser analisados pelos usuários do domínio científico a fim de validar as suas hipóteses científicas. Entretanto, esta não é uma tarefa trivial, pois outros programas precisam ser desenvolvidos para acessar e capturar esses dados científicos. Em muitos casos, os usuários também precisam relacionar dados produzidos por diferentes programas de simulação. Esta tese propõe uma abordagem capaz de monitorar, depurar e analisar o fluxo de elementos de dados produzido pelos diferentes programas de simulação. Propomos também uma arquitetura baseada em componentes, nomeada como ARMFUL, que permite extrair e relacionar dados científicos produzidos nessas diversas etapas por meio da abstração de fluxo de dados e de técnicas de captura de dados científicos. Os seus componentes podem ser instanciados em um sistema de workflows científicos (A-Chiron) ou uma biblioteca de componentes (DfAnalyzer). Avaliamos essas instâncias utilizando simulações em ambientes de processamento de alto desempenho. Os resultados experimentais mostram que a nossa abordagem introduz uma sobrecarga negligenciável em relação ao tempo de execução da simulação, além de permitir o processamento de consultas aos dados científicos.
Published: 2018

31. Analysis of raw data from multiple data sources during the execution of computational simulations

Author: Silva, Vitor, Universidade Federal do Rio de Janeiro (UFRJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universidade Federal de Rio de Janeiro, Marta Mattoso, Daniel de Oliveira, Patrick Valduriez(Patrick.Valduriez@inria.fr), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Gestion de données scientifiques, Scientific workflow management, Data analysis, Scientific data management, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT
Abstract: Large-scale computational simulations are characterized by the chaining of programs that execute increasingly complex computational models. Much of the data produced by these programs need to be analyzed by scientific domain users to validate their scientific hypotheses. However, it is not trivial since other programs must be developed to access and to capture these scientific data. In many cases, users also need to relate data produced by different simulation programs. This thesis proposes an approach that monitors, debugs, and analyzes the data element flow produced by different simulation programs. We also propose a component-based architecture, named as ARMFUL, to extract and relate scientific data generated in these several simulation steps considering a dataflow abstraction and techniques for scientific data capture. ARMFUL’s components can be instantiated on a scientific workflow system (e.g., A-Chiron) or a library of components (e.g., DfAnalyzer). We evaluate these instances using simulations in high performance computing environments. In our experimental results, our approach introduced a negligible overhead of the simulation execution time, and we perform complex queries to the scientific data.; Les simulations numériques à grande échelle se caractérisent par l'enchaînement de programmes qui exécutent des modèles informatiques de plus en plus complexes. Une grande partie des données produites par ces programmes doivent être analysées par les utilisateurs du domaine scientifique pour valider leurs hypothèses scientifiques. Cependant, ce n'est pas anodin puisque d'autres programmes doivent être développés pour accéder à ces données scientifiques et les saisir. Dans de nombreux cas, les utilisateurs ont également besoin de relier les données produites par différents programmes de simulation. Cette thèse propose une approche qui surveille, débogue et analyse le flux d'éléments de données produits par différents programmes de simulation. Nous proposons également une architecture basée sur les composants, appelée ARMFUL, pour extraire et relier les données scientifiques générées dans ces différentes étapes de simulation, en tenant compte d'un flux de données abstraites et des techniques de capture de données scientifiques. Les composants de ARMFUL peuvent être instanciés sur un système de flux de travail scientifique (par exemple, A-Chiron) ou une bibliothèque de composants (par exemple, DfAnalyzer). Nous évaluons ces cas à l'aide de simulations dans des environnements informatiques à haute performance. Dans nos résultats expérimentaux, notre approche a introduit une surcharge négligeable du temps d'exécution de la simulation, et nous effectuons des requêtes complexes aux données scientifiques.; Simulações computacionais em larga escala são caracterizadas pelo encadeamento de programas que executam modelos computacionais cada vez mais complexos. Muitos dos dados produzidos por esses programas precisam ser analisados pelos usuários do domínio científico a fim de validar as suas hipóteses científicas. Entretanto, esta não é uma tarefa trivial, pois outros programas precisam ser desenvolvidos para acessar e capturar esses dados científicos. Em muitos casos, os usuários também precisam relacionar dados produzidos por diferentes programas de simulação. Esta tese propõe uma abordagem capaz de monitorar, depurar e analisar o fluxo de elementos de dados produzido pelos diferentes programas de simulação. Propomos também uma arquitetura baseada em componentes, nomeada como ARMFUL, que permite extrair e relacionar dados científicos produzidos nessas diversas etapas por meio da abstração de fluxo de dados e de técnicas de captura de dados científicos. Os seus componentes podem ser instanciados em um sistema de workflows científicos (A-Chiron) ou uma biblioteca de componentes (DfAnalyzer). Avaliamos essas instâncias utilizando simulações em ambientes de processamento de alto desempenho. Os resultados experimentais mostram que a nossa abordagem introduz uma sobrecarga negligenciável em relação ao tempo de execução da simulação, além de permitir o processamento de consultas aos dados científicos.
Published: 2018

32. Summary-based optimization in semantic graph databases

Author: Czeresnia Etinger, Isak, Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France
Subjects: ACM: H.: Information Systems, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, InformationSystems_DATABASEMANAGEMENT, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES
Abstract: RDF is the data model of choice for the semantic Web. SPARQL is the standard of the W3C for querying RDF graph. As the structure of RDF graph is heterogeneous and they lack a schema, evaluating queries over such graphs may be hard. Toward making it more efficient, in this project we study the problem of optimizing SPARQL queries based on RDF quotient summaries.
Published: 2018

33. Connecting Width and Structure in Knowledge Compilation

Author: Amarilli, Antoine, Monet, Mikaël, Senellart, Pierre, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Value from Data (VALDA ), Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: Probabilistic databases, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Circuits, Treewidth, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Knowledge compilation
Abstract: International audience; Several query evaluation tasks can be done via knowledge compilation: the query result is compiledas a lineage circuit from which the answer can be determined. For such tasks, it is importantto leverage some width parameters of the circuit, such as bounded treewidth or pathwidth, to convertthe circuit to structured classes, e.g., deterministic structured NNFs (d-SDNNFs) or OBDDs.In this work, we show how to connect the width of circuits to the size of their structured representation,through upper and lower bounds. For the upper bound, we show how bounded-treewidthcircuits can be converted to a d-SDNNF, in time linear in the circuit size. Our bound, unlikeexisting results, is constructive and only singly exponential in the treewidth. We show a relatedlower bound on monotone DNF or CNF formulas, assuming a constant bound on the arity (size ofclauses) and degree (number of occurrences of each variable). Specifically, any d-SDNNF (resp.,SDNNF) for such a DNF (resp., CNF) must be of exponential size in its treewidth; and thesame holds for pathwidth when compiling to OBDDs. Our lower bounds, in contrast with mostprevious work, apply to any formula of this class, not just a well-chosen family. Hence, for ourlanguage of DNF and CNF, pathwidth and treewidth respectively characterize the efficiency ofcompiling to OBDDs and (d-)SDNNFs, that is, compilation is singly exponential in the widthparameter. We conclude by applying our lower bound results to the task of query evaluation.
Published: 2018
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34. A Semantic Approach to Manage Ideas in an Innovation Process

Author: Monticolo, Davy, Sidhom, Sahbi, Chávez Barrios, Pedro, Equipe de Recherche sur les Processus Innovatifs (ERPI), Université de Lorraine (UL), Knowledge Information and Web Intelligence (KIWI), Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Sidhom, Sahbi
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, ACM: H.: Information Systems, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; For almost one decade, the organization " 48 hours generating ideas " (48H) makes every year a creativity workshop (CWS) to generate thousands of idea cards (IdC) by mean of creative methods with the purpose of solving industrial problems; inside of this organization, creativity depends on solver participants, creative experts, organizators, technical expert and industrial managers like Acar states in [1] " Organizational creativity is influenced by many thing. Some are social other are brought to the organization by individuals who comprise it. To certain degree, the organizational creativity depends on the individuals inside it " ; the huge quantity of these IdC's creates a problem " how to manage, to select and to compare these ideas that contain several and different fields " as Khemiri wrote in [2] " Text representations in a multimedia corpus with heterogeneous ideas " ; the 48H has several complexities such as people from different continents and cultures, but also, different industries and educative centers are participating during this event; We want to implement a semantic approach to manage ideas for the same industrial problem. This project started in september 2016, the proposal has five stages: to understand creative methods, to analyze a creativity workshop, to identify knowledge by mean of Organizational Model, to propose a Model to compare and evaluate ideas, and to develop an annotation system. We have observed that more than 1200 idea cards were generated during the last 48H creativity workshop. Each idea card describes an idea which is our main source of data. An example of an IdC: the 48H's idea card called Multifunction flying tractor (figure central), it is well structured according to nine components: Title of the idea (Text informative), Team, Description, Schema (draw),The topic (industrial problem), Priority clients, Scenario of use, Advantages, Risks, Competences; We will take in account the fields Title and Description. The proposal contain three principal steps: a) to design a model to highlights knowledge, b) to propose a semantic approach to annotate idea cards, and c) to implement an Intelligent system. The design of the model which highlights knowledge from the organizational creativity workshop 48H uses the meta-model KROM (Knowledge Reuse Organizational Meta-Model) by Girondon in [3] which facilitates the understanding of the collaboration among roles and highlight knowledge by collaborative activities; This organizational model has three domains: description of organizational structure (presented on the poster), description of expertise management, and description of knowledge management. The description of organizational structure shows missions, goals to identify the 48H and procedures inside the organization.
Published: 2018

35. Evaluating Queries and Updates on Big XML Documents

Author: Nicole Bidoit, Dario Colazzo, Noor Malla, Carlo Sartiani, Données et Connaissances Massives et Hétérogènes (LRI) (LaHDAK - LRI), Laboratoire de Recherche en Informatique (LRI), and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Subjects: XML Encryption, Computer Networks and Communications, Computer science, Efficient XML Interchange, 02 engineering and technology, computer.software_genre, Theoretical Computer Science, Simple API for XML, XML Schema Editor, 020204 information systems, Streaming XML, 0202 electrical engineering, electronic engineering, information engineering, Cloud computing, XML schema, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, computer.programming_language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Database, XML validation, computer.file_format, XML, XML database, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Map/Reduce, 020201 artificial intelligence & image processing, computer, Software, Information Systems
Abstract: International audience; In this paper we present Andromeda, a system for processing queries and updates on large XML documents. The system is based on the idea of statically and dynamically partitioning the input document, so as to distribute the computing load among the machines of a MapReduce cluster.
Published: 2018
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36. NoSQL Data Models – Trends and Challenges

Author: Pivert, Olivier, GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

37. πSOD-M: Construction d'applications à base de services en présence de besoins non-functionnels

Author: Khalid Belhajjame, Valeria de Castro, Javier Espinosa-Oviedo, Martin Musicante, Umberto Souza da Costa, Plácido Souza Neto, Genoveva Vargas Solar, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Universidad Rey Juan Carlos [Madrid] (URJC), Laboratoire Franco-Mexicain d'Informatique et d'Automatique (LAFMIA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV)-Université de Technologie de Compiègne (UTC)-Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Universidade Federal do Rio Grande do Norte [Natal] (UFRN), Heterogeneous and Adaptive distributed DAta management Systems (HADAS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and UFRN
Subjects: Non-Functional Requirements, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Service-based software process, MDA, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES
Abstract: International audience; Specifying non-functional requirements (NFRs) is a complex task, being usually addressed during the latter phases of the software development process. The late inclusion of NFRs during the software development may compromise the quality of the resulting application. This paper presents πSOD- M, a method and associated tools that (i) allow the early specification of NFRs allowing users to them in an abstract way without having to care about low level details; (ii) embraces the MDA philosophy, generating models (code) whenever possible for reducing the specification and programming effort of the business application logic and its associated NFRs. Our solution has been used in the context of an industrial and real case study.
Published: 2018

38. Evaluating visual data analysis systems: A discussion report

Author: Marco Angelini, Carsten Binnig, Leilani Battle, Tiziana Catarci, Giuseppe Santucci, Wesley Willett, Michael Sedlmair, Philipp Eichmann, Jean-Daniel Fekete, University of Washington [Seattle], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Darmstadt University of Technology [Darmstadt], Brown University, Analysis and Visualization (AVIZ), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Jacobs University [Bremen], University of Calgary, and Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, Intersection (set theory), 020207 software engineering, 02 engineering and technology, Data science, Visualization, Data set, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), Computational Theory and Mathematics, Information Systems, Computer Science Applications1707 Computer Vision and Pattern Recognition, Data system, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Grand Challenges, TRACE (psycholinguistics)
Abstract: International audience; Visual data analysis is a key tool for helping people to make sense of and interact with massive data sets. However, existing evaluation methods (e.g., database benchmarks, individual user studies) fail to capture the key points that make systems for visual data analysis (or visual data systems) challenging to design. In November 2017, members of both the Database and Visualization communities came together in a Dagstuhl seminar to discuss the grand challenges in the intersection of data analysis and interactive visualization. In this paper, we report on the discussions of the working group on the evaluation of visual data systems, which addressed questions centered around developing better evaluation methods, such as " How do the different communities evaluate visual data systems? " and " What we could learn from each other to develop evaluation techniques that cut across areas? ". In their discussions, the group brainstormed initial steps towards new joint evaluation methods and developed a first concrete initiative — a trace repository of various real-world workloads and visual data systems — that enables researchers to derive evaluation setups (e.g., performance benchmarks, user studies) under more realistic assumptions, and enables new evaluation perspectives (e.g., broader meta analysis across analysis contexts, reproducibility and comparability across systems).
Published: 2018
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39. Proceedings of the BDA 2017 conference

Author: Senellart, Pierre, Napoli, Amedeo, Value from Data (VALDA ), Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2017

40. Le Document ?, Coordination des « Actes de 20e Colloque International sur le Document Electronique (CiDE.20) »

Author: Larouk, Omar, Hassoun, Mohamed, Zreik, Khaldoun, BESACIER, Guillaume, Equipe de recherche de Lyon en sciences de l'information et de la communication (ELICO), Université Lumière - Lyon 2 (UL2)-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), Université de Lyon-Université de Lyon-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Mohamed HASSOUN & Omar LAROUK, Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-École nationale supérieure des sciences de l'information et des bibliothèques (ENSSIB), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.1: Information filtering, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.2: User Interfaces, [INFO.INFO-WB]Computer Science [cs]/Web, [SCCO.LING]Cognitive science/Linguistics, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.2: Query formulation, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.4: Retrieval models, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and Indexing, ComputingMilieux_MISCELLANEOUS, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.0: Clustering
Abstract: International audience
Published: 2017

41. Data Discovery in RDF Graphs

Author: Manolescu, Ioana, Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2017

42. Combined Tractability of Query Evaluation via Tree Automata and Cycluits

Author: Amarilli, Antoine, Bourhis, Pierre, Monet, Mikaël, Senellart, Pierre, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Linking Dynamic Data (LINKS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Value from Data (VALDA ), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer Science::Logic in Computer Science, circuits, provenance, treewidth, InformationSystems_DATABASEMANAGEMENT, Computer Science::Programming Languages, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Computer Science::Databases, query evaluation, tree automata
Abstract: International audience; We investigate parameterizations of both database instances and queries that make query evaluation fixed-parameter tractable in combined complexity. We introduce a new Datalog fragment with stratified negation, intensional-clique-guarded Datalog (ICG-Datalog), with linear-time evaluation on structures of bounded treewidth for programs of bounded rule size. Such programs capture in particular conjunctive queries with simplicial decompositions of bounded width, guarded negation fragment queries of bounded CQ-rank, or two-way regular path queries. Our result is shown by compiling to alternating two-way automata, whose semantics is defined via cyclic provenance circuits (cycluits) that can be tractably evaluated. Last, we prove that probabilistic query evaluation remains intractable in combined complexity under this parameterization.
Published: 2017
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43. Top-k Querying of Unknown Values under Order Constraints

Author: Amarilli, Antoine, Amsterdamer, Yael, Milo, Tova, Senellart, Pierre, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [Bar Ilan], Bar-Ilan University [Israël], School of Computer Science (TAU-CS), Tel Aviv University (TAU), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Value from Data (VALDA ), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Tel Aviv University [Tel Aviv], Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], partial order, unknown values, crowdsourcing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, uncertainty, interpolation
Abstract: International audience; Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data.
Published: 2017
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44. Robust Module-Based Data Management

Author: François Goasdoué, M-C Rousset, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Database optimizations and architectures for complex large data (OAK), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Heterogeneous and Adaptive distributed DAta management Systems (HADAS), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Computer science, Data management, RDF Schema, 02 engineering and technology, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Personalization, Data modeling, Description logic, Schema (psychology), 0202 electrical engineering, electronic engineering, information engineering, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.4: Knowledge Representation Formalisms and Methods, RDF, Semantic Web, computer.programming_language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Information retrieval, business.industry, 020207 software engineering, Web Ontology Language, computer.file_format, Ontology language, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, Computer Science Applications, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, Computational Theory and Mathematics, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Ontology, 020201 artificial intelligence & image processing, Data mining, business, computer, Information Systems
Abstract: International audience; The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs -- a module --, possibly personalizing it with extra-constraints wrt the application under construction, and then managing a dataset using the resulting schema. In this paper, we extend the existing definitions of modules and we introduce novel properties of robustness that provide means for checking easily that a robust module-based DMS evolves safely wrt both the schema and the data of the reference DMS. We carry out our investigations in the setting of description logics which underlie modern ontology languages, like RDFS, OWL, and OWL2 from W3C. Notably, we focus on the DL-lite_A dialect of the DL-lite family, which encompasses the foundations of the QL profile of OWL2 (ie DL-lite_R): the W3C recommendation for efficiently managing large datasets.
Published: 2013
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45. Secret Sharing for Cloud Data Security

Author: Varunya Attasena, Jérôme Darmont, Nouria Harbi, Kasetsart University - KU (THAILAND), Kasetsart University (KU), Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon
Subjects: FOS: Computer and information sciences, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer Science - Cryptography and Security, Databases (cs.DB), Data availability, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Computer Science - Databases, Data integrity, Cloud computing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Data access, Secret sharing, Data privacy, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.0: General/H.2.0.0: Security, integrity, and protection, Cryptography and Security (cs.CR)
Abstract: International audience; Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm.
Published: 2017
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46. Updating RDF/S Databases Under Constraints

Author: Mirian Halfeld-Ferrari, Dominique Laurent, Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), and Halfeld Ferrari, Mirian
Subjects: Chase, Computer science, RDF Schema, 02 engineering and technology, [INFO] Computer Science [cs], computer.software_genre, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], SPARQL, [INFO]Computer Science [cs], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, RDF, Polynomial time complexity, Cwm, ComputingMilieux_MISCELLANEOUS, computer.programming_language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Database, InformationSystems_DATABASEMANAGEMENT, computer.file_format, First-order logic, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages, 020201 artificial intelligence & image processing, computer, RDF query language
Abstract: We address the issue of database updating in the presence of constraints, using the first order logic formalism. Based on a chasing technique, we propose a deterministic update strategy. While generalizing key-foreign key constraints, our approach satisfies the consistency and minimal change requirements, with a polynomial time complexity. Our custom version of the chase allows improvement in updating non-null RDF/S databases with constraints.
Published: 2017

47. RDF Updates with Constraints

Author: Halfeld Ferrari, Mirian, Hara, Carmem, Uber, Flávio, Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO), and Halfeld Ferrari, Mirian
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, ComputingMilieux_MISCELLANEOUS, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages
Abstract: International audience
Published: 2017

48. Big Data Clustering using Data Streams Approach

Author: Ibrahim Louhi, Lydia Boudjeloud-Assala, Thomas Tamisier, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Data stream, Clustering high-dimensional data, Fuzzy clustering, Data stream mining, Computer science, Correlation clustering, Single-linkage clustering, 02 engineering and technology, computer.software_genre, Graph, Data stream clustering, CURE data clustering algorithm, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Canopy clustering algorithm, FLAME clustering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Data mining, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, Cluster analysis, computer, k-medians clustering, ComputingMilieux_MISCELLANEOUS
Abstract: In this paper we propose to process big data using a data streams approach. The data set is divided into subsets, each subsets is considered as a time window from a data stream. Our approach uses a neighborhood-based clustering. Instead of processing each new element one by one, we propose to process each group of new elements simultaneously. A clustering is applied on each new group using neighborhood graphs. The obtained clusters are then used to incrementally construct a representative graph of the data. The data graph is visualized in real time with specific visualizations that reflect the processing algorithm. To validate the approach, we apply it to different data streams and we compare it with known data stream clustering approaches.
Published: 2016

49. Data Exchange with MapReduce

Author: Khalid Belhajjame, Angela Bonifati, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Open science, Theoretical computer science, Chase, Computer science, 02 engineering and technology, computer.software_genre, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, 060201 languages & linguistics, Data Exchange, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Map-Reduce, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases/H.2.5.0: Data translation, 06 humanities and the arts, Linked data, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.5: Heterogeneous Databases, Data exchange, Dependencies, 0602 languages and literature, Scalability, Programming paradigm, 020201 artificial intelligence & image processing, Data mining, Tuple, computer
Abstract: International audience; Data exchange is one of the oldest database problems, being of both practical and theoretical interest. Given the pace at which heterogeneous data are published on the web, thanks to initiatives such as Linked Data and Open Science, scalability of data exchange becomes crucial. Pivotal to data exchange is the chase algorithm, which is a fixpoint algorithm to evaluate both source-to-target constraints and target constraints in the data exchange process. In this paper, we investigate how new programming models such as MapReduce can be used to implement the chase on large-scale data sources. To the best of our knowledge, how to exchange data at scale has not been investigated so far. We present an initial solution for chasing source-to-target tuple generating dependencies and target tuple-generating dependencies, and discuss open issues that need to be addressed to leverage MapReduce for the data exchange problem.
Published: 2016
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50. A Root of Trust for the Personal Cloud

Author: Anciaux, Nicolas, André, Benjamin, Pucheral, Philippe, Tran-Van, Paul, Secured and Mobile Information Systems (SMIS), Centre National de la Recherche Scientifique (CNRS)-Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Cozy Cloud, Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Personal cloud, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], access control, ACM: B.: Hardware, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, embedded database, secure hardware
Abstract: International audience; The Personal Cloud paradigm has recently emerged as a way to allow individuals to manage under their control the collection, usage and sharing of data in different contexts and for different types and sensitivities of data. While much research work is tackling the organisation and semantic exploitation of the user's workspace, far less attention has been paid to security issues. We promote a new approach, called Secure Personal Cloud, where the root of security is made of secure hardware and the access control policies are easily extracted from the personal cloud itself, with minimal user interaction. Our solution is based on the tight integration of a Personal Cloud platform (Cozy) with a trusted component (PlugDB).
Published: 2016

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