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The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation (NED) at the heart of designing accurate KG-driven systems such as query answering systems (QAS). According to the current research, most studies dealing with NED on KGs involve long texts, which is not the case of short text fragments, identified by their limited contexts. The accuracy of QASs strongly depends on the management of such short text. This limitation motivates this paper, which studies the NED problem on KGs, involving only short texts. First, we propose a NED approach including the following steps: (i) context expansion using WordNet to measure its similarity to the resource context. (ii) Exploiting coherence between entities in queries that contain more than one entity, such as “Is Michelle Obama the wife of Barack Obama?”. (iii) Taking into account the relations between words to calculate their similarity with the properties of a resource. (iv) the use of syntactic features. The NED solution approach is compared to state-of-the-art approaches using five datasets. The experimental results show that our approach outperforms these systems by 27% in the F-measure. A system called Welink, implementing our proposal, is available on GitHub, and it is also accessible via a REST API.

The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.

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).

Soumission à Episciences, Computers and electronic machines in businesses consume a significant amount of electricity, releasing carbon dioxide (CO2), which contributes to greenhouse gas emissions. Energy efficiency is a pressing concern in IT systems, ranging from mobile devices to large servers in data centers, in order to be more environmentally responsible. In order to meet the growing demands in the awareness of excessive energy consumption, many initiatives have been launched on energy efficiency for big data processing covering electronic components, software and applications. Query optimizers are one of the most power consuming components of a DBMS. They can be modified to take into account the energetical cost of query plans by using energy-based cost models with the aim of reducing the power consumption of computer systems. In this paper, we study, describe and evaluate the design of three energy cost models whose values of energy sensitive parameters are determined using the Nonlinear Regression and the Random Forests techniques. To this end, we study in depth the operating principle of the selected DBMS and present an analysis comparing the performance time and energy consumption of typical queries in the TPC benchmark. We perform extensive experiments on a physical testbed based on PostreSQL, MontetDB and Hyrise systems using workloads generatedusing our chosen benchmark to validate our proposal., Les ordinateurs et les machines électroniques des entreprises consomment une quantité importante d’électricité, libérant ainsi du dioxyde de carbone (CO2), qui contribue aux émissions de gaz à effet de serre. L’efficacité énergétique est une préoccupation urgente dans les systèmesinformatiques, partant des équipements mobiles aux grands serveurs dans les centres de données, afin d’être plus respectueux envers l’environnement. Afin de répondre aux exigences croissantes en matière de sensibilisation à l’utilisation excessive de l’énergie, de nombreuses initiatives ont été lancées sur l’efficacité énergétique pour le traitement des données massives couvrant les composantsélectroniques, les logiciels et les applications. Les optimiseurs de requêtes sont l’un des composants les plus énergivores d’un SGBD. Ils peuvent être modifiés pour prendre en compte le coût énergétique des plans des requêtes à l’aide des modèles de coût énergétiques intégrés dans l’optimiseur dans le but de réduire la consommation électrique des systèmes informatiques. Dans cet article, nousétudions, décrivons et évaluons la conception de trois modèles de coût énergétique dont les valeurs des paramètres sensibles à l’énergie sont définis en utilisant la technique de la Régression non linéaire et la technique des forêts aléatoires. Pour ce fait, nous menons une étude approfondie du principe de fonctionnement des SGBD choisis et présentons une analyse des performances en termes de temps et énergie sur des requêtes typiques du benchmarks TPC-H. Nous effectuons des expériences approfondies basées sur les systèmes PostgreSQL, MonetDB et Hyrise en utilisant un jeu de données généré à partir du benchmarks TPC-H afin de valider nos propositions.

The presentations during the BI&SemWeb event in 2020 (online) in Dakar, Senegal. Link to the speakers and slides:https://sites.google.com/view/bisemweb20

Research on database and information technologies has been rapidly evolving over the last couple of years. This evolution was lead by three major forces: Big Data, AI and Connected World that open the door to innovative research directions and challenges, yet exploiting four main areas: (i) computational and storage resource modeling and organization; (ii) new programming models, (iii) processing power and (iv) new applications that emerge related to health, environment, education, Cultural Heritage, Banking, etc. The 24th East-European Conference on Advances in Databases and Information Systems (ADBIS 2020), the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020) and the 16th Workshop on Business Intelligence and Big Data (EDA 2020), held during August 25–27, 2020, at Lyon, France, and associated satellite events aimed at covering some emerging issues related to database and information system research in these areas. The aim of this paper is to present such events, their motivations, and topics of interest, as well as briefly outline the papers selected for presentations. The selected papers will then be included in the remainder of this volume.

International audience

International audience