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Increasing single instruction multiple data (SIMD) capabilities in modern hardware allows for the compilation of data-parallel query pipelines. This means GPU-alike challenges arise: control flow divergence causes the underutilization of vector-processing units. In this paper, we present efficient algorithms for the AVX-512 architecture to address this issue. These algorithms allow for the fine-grained assignment of new tuples to idle SIMD lanes. Furthermore, we present strategies for their integration with compiled query pipelines so that tuples are never evicted from registers. We evaluate our approach with three query types: (i) a table scan query based on TPC-H Query 1, that performs up to 34% faster when addressing underutilization, (ii) a hashjoin query, where we observe up to 25% higher performance, and (iii) an approximate geospatial join query, which shows performance improvements of up to 30%.

Machine and statistical learning is about constructing models from data. Data is usually understood as a set of records, a database. Nevertheless, databases are not static but change over time. We can understand this as follows: there is a space of possible databases and a database during its lifetime transits this space. Therefore, we may consider transitions between databases, and the database space. NoSQL databases also fit with this representation. In addition, when we learn models from databases, we can also consider the space of models. Naturally, there are relationships between the space of data and the space of models. Any transition in the space of data may correspond to a transition in the space of models. We argue that a better understanding of the space of data and the space of models, as well as the relationships between these two spaces is basic for machine and statistical learning. The relationship between these two spaces can be exploited in several contexts as, e.g., in model selection and data privacy. We consider that this relationship between spaces is also fundamental to understand generalization and overfitting. In this paper, we develop these ideas. Then, we consider a distance on the space of models based on a distance on the space of data. More particularly, we consider distance distribution functions and probabilistic metric spaces on the space of data and the space of models. Our modelization of changes in databases is based on Markov chains and transition matrices. This modelization is used in the definition of distances. We provide examples of our definitions. CC BY 4.0© 2021, The Author(s).Correspondence Address: Torra, V.; School of Informatics, Sweden; email: vtorra@ieee.orgPublished: 12 April 2021Acknowledgements: This study was partially funded by Vetenskapsrådet project “Disclosure risk and transparency in big data privacy” (VR 2016-03346, 2017-2020), Spanish project TIN2017-87211-R is gratefully acknowledged, and by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Commercial off-the-shelf DataBase Management Systems (DBMSes) are highly optimized to process a wide range of queries by means of carefully designed indexing and query planning. However, many aggregate range queries are usually performed by DBMSes using sequential scans, and certain needs, like storing Authenticated Data Structures (ADS), are not supported at all. Theoretically, these needs could be efficiently fulfilled adopting specific kinds of indexing, which however are normally ruled-out in DBMSes design. We introduce the concept of overlay index: an index that is meant to be stored in a standard database, alongside regular data and managed by regular software, to complement DBMS capabilities. We show a data structure, that we call DB-tree, that realizes an overlay index to support a wide range of custom aggregate range queries as well as ADSes, efficiently. All DB-trees operations can be performed by executing a small number of queries to the DBMS, that can be issued in parallel in one or two query rounds, and involves a logarithmic amount of data. We experimentally evaluate the efficiency of DB-trees showing that our approach is effective, especially if data updates are limited.

International audience; Non-Volatile Memory (NVM) is a promising development for Database Management Systems (DBMS), offering abundant and fast storage to complement traditional disk and main memory architectures. NVM introduces additional data migration possibilities to the traditional buffer pool (BP) managers used by the DBMS. Hence, the efficient use of this new technology requires a redesign of the BP manager. For the cloud Database-as-a-Service products, this need for a redesign is further complicated by the traditional cloud providers' goal to minimize the Service Level Agreement (SLA) violation penalties paid to their tenants. Unfortunately, current research in the area does not provide a comprehensive picture of the components constituting a multi-tenant persistent memory aware BP manager for a cloud database that makes use of NVM. Furthermore, researchers lack the software tools needed to quickly prototype and estimate the effectiveness of novel data management policies guiding those components. In this paper, we attempt to remedy both issues, first, by proposing a generalized framework that defines the purpose and the abstract interfaces of various multi-tenant persistent memory-aware BP manager components, and second, by developing and demonstrating a simulator algorithm that is shown to aid in quick testing of different implementations of those BP manager components.

This paper presents the stochastic-geometry analysis and designs a primary exclusive region (PER) for a spatial grid-based spectrum database system. The purpose of the spatial grid is to utilize information, such as the primary receiver (PR) antenna pattern, and the secondary transmitter (ST) density and transmission power, in each divided region. This paper introduces polar and square grids. For these spatial grids, the cumulants of the aggregate interference at a PR, from the STs, are derived, where the probability generating functional for the Poisson point process (PPP) is used on the assumption that the distribution of the STs in each divided region follows an inhomogeneous PPP. In addition, by introducing the allowable transmission probability of STs in each divided region, the PER optimization problem can be formulated as a continuous optimization problem. Numerical results demonstrate that a PER, corresponding to the information, is successfully designed and that more detailed information, due to a reduction in the area of each divided region, leads to a smaller and complex-shaped PER.

We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem. CC BY 4.0Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 12276)Partial support of the project Swedish Research Council (grant number VR 2016-03346) is acknowledged.DRIAT

International audience; The process-based life cycle assessments (LCA) of goods and services are calculated using a bottom-up approach related to a functional unit. However, this does not provide any information regarding the scale of the environmental impacts. Therefore, the normalization allows to relate the impacts to a reference system (specific countries, regions or even the whole world). These references are usually obtained from top-down approach. The different data sources introduce inconsistencies on results and raise doubts on their adequacy and representativity. This paper proposes a novel approach for determining the data for the reference in order to ensure consistency about boundaries, data sources and modelling hypotheses describing the system. For this purpose, normalization is applied as an expression of the result relative to the average component of the reference system, instead of the sum of all the components. The reference values are determined from the geometric means of the datasets of the inventory database, used for assessing the studied systems. The exemplary application to the ecoinvent databases provides normalization references for 878 versions of the impacts categories listed by ecoinvent and for the 2077 involved substances. For eight impact assessment methods, the results are compared with 16 normalization sets from the literature and point out highly significant correlations.

We are witnessing an enormous growth in social networks as well as in the volume of data generated by them. An important portion of this data is in the form of graphs. In recent years, several graph processing and management systems emerged to handle large-scale graphs. The primary goal of these systems is to run graph algorithms and queries in an efficient and scalable manner. Unlike relational data, graphs are semi-structured in nature. Thus, storing and accessing graph data using secondary storage requires new solutions that can provide locality of access for graph processing workloads. In this work, we propose a scalable block formation and layout technique for graphs, which aims at reducing the I/O cost of disk-based graph processing algorithms. To achieve this, we designed a scalable MapReduce-style method called ICBL, which can divide the graph into a series of disk blocks that contain sub-graphs with high locality. Furthermore, ICBL can order the resulting blocks on disk to further reduce non-local accesses. We experimentally evaluated ICBL to showcase its scalability, layout quality, as well as the effectiveness of automatic parameter tuning for ICBL. We deployed the graph layouts generated by ICBL on the Neo4j open source graph database, http://www.neo4j.org/ (2015) graph database management system. Our results show that the layout generated by ICBL reduces the query running times over Neo4j more than 2 × compared to the default layout. © 2017, Springer Science+Business Media New York.

This survey paper discusses the feasibility of sharing the spectrum between satellite telecommunication networks and terrestrial and other satellite networks on the basis of a comprehensive study carried out as part of the European Space Agency's (ESA) Advanced Research in Telecommunications Systems (ARTES) programme. The main area of investigation is the use of spectrum databases to enable a controlled sharing environment. Future satellite systems can largely benefit from the ability to access spectrum bands other than the dedicated licensed spectrum band. Potential spectrum sharing scenarios are classified as: a) secondary use of the satellite spectrum by terrestrial systems, b) satellite system as a secondary user of spectrum, c) extension of a terrestrial network by using the satellite network, and d) two satellite systems sharing the same spectrum. We define practical use cases for each scenario and identify suitable techniques. The proposed scenarios and use cases cover several frequency bands and satellite orbits. Out of all the scenarios reviewed, owing to the announcement of many different mega-constellation satellite networks, we focus on analysing the feasibility of spectrum sharing between geostationary orbit (GSO) and non-geostationary orbit (NGSO) satellite systems. The performance is primarily analysed on the basis of widely accepted recommendations of the Radiocommunications Sector of the International Telecommunications Union (ITU-R). Finally, future research directions are identified.

This paper presents a new database consisting of concurrent articulatory and acoustic speech data. The articulatory data correspond to ultrasound videos of the vocal tract dynamics, which allow the visualization of the tongue upper contour during the speech production process. Acoustic data is composed of 30 short sentences that were acquired by a directional cardioid microphone. This database includes data from 17 young subjects (8 male and 9 female) from the Santander region in Colombia, who reported not having any speech pathology. © 2019 IEEE. IEEE Colombia Section;IEEE Signal Processing Society Colombia Chapter;Universidad Industrial de Santander

International audience; In this paper, a new instance selection algorithm is proposed in the context of classification to manage non-trivial database sizes. The algorithm is hybrid and runs with only a few parameters that directly control the balance between the three objectives of classification, i.e. errors, storage requirements and runtime. It comprises different mechanisms involving neighborhood and stratification algorithms that specifically speed up the runtime without significantly degrading efficiency. Instead of applying an IS (Instance Selection) algorithm to the whole database, IS is applied to strata deriving from the regions, each region representing a set of patterns selected from the original training set. The application of IS is conditioned by the purity of each region (i.e. the extent to which different categories of patterns are mixed in the region) and the stratification strategy is adapted to the region components. For each region, the number of delivered instances is firstly limited via the use of an iterative process that takes into account the boundary complexity, and secondly optimized by removing the superfluous ones. The sets of instances determined from all the regions are put together to provide an intermediate instance set that undergoes a dedicated filtering process to deliver the final set. Experiments performed with various synthetic and real data sets demonstrate the advantages of the proposed approach.

The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification. Landesamt für Geoinformation und Landesvermessung Niedersachsen (LGLN) Landesamt für Vermessung und Geoinformation Schleswig Holstein (LVermGeo)

Graphics processing units have proved its capability for general purpose computing in many research areas. In this paper, we propose the mechanism and implementation of a database system that encrypts and decrypts data by using GPU. The proposed mechanism is mainly designed for a database system that requires data encryption and decryption to support high security level or ODBS. By exploiting the computation capability of GPU, we achieve not only a fast encryption and decryption time per operation but also a higher overall performance of a database system by offioading computation to GPU. Moreover, the proposed system includes a mechanism which can decide whether to offload computation to GPU or not for more performance gain. We implemented the AES algorithm based on CUDA framework and integrate with MySQL, a commodity database system. Our evaluation demonstrates that the encryption and decryption on GPU shows eight times better performance compared that on CPU when the data size is 16 MB. We also show that the proposed system alleviates the utilization of CPU and the overall performance of database system is improved by offioading heavy encrypting and decrypting computation to GPU.

Date of Conference: 12-15 Dec. 2016 Conference name: IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 While research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from vehicles, we develop a time-varying road network data set where edge weights differ over time. We present our methodology and share this dataset, along with a graph manipulation tool. We estimate the traffic conditions using the sparse GPS data available by characterizing the sparsity issues and assessing the properties of travel sequence data frequency domain. We develop interpolation methods to complete the sparse data into a complete graph dataset with realistic time-varying edge values. We evaluate the performance of time-varying and static shortest path solutions over the generated dynamic road network. The shortest paths using the dynamic graph produce very different results than the static version. We provide an independent Java API and a graph database to analyze and manipulate the generated time-varying graph data easily, not requiring any knowledge about the inners of the graph database system. We expect our solution to support researchers to pursue problems of time-varying graphs in terms of theoretical, algorithmic, and systems aspects. The data and Java API are available at: http://elif.eser.bilkent.edu.tr/roadnetwork. © 2016 IEEE.

Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively. The research leading to these results has received funding from the European Union’s 7th Framework Programme under grant agreement number 318633, the Ministry of Science and Technology of Spain under contract TIN2015-65316-P, and a HiPEAC collaboration grant awarded to Naveed Ul Mustafa.

Conference of 4th ACM International Workshop on Security in Cloud Computing, SCC 2016 ; Conference Date: 30 May 2016; Conference Code:121977; International audience; The wide use of cloud computing and of data outsourcing rises important concerns with regards to data security resulting thus in the necessity of protection mechanisms such as encryption of sensitive data. The recent major theoretical breakthrough of finding the Holy Grail of encryption, i.e. fully homomorphic encryption guarantees the privacy of queries and their results on encrypted data. However, there are only a few studies proposing a practical performance evaluation of the use of homomorphic encryption schemes in order to perform database queries. In this paper, we propose and analyse in the context of a secure framework for a generic database query interpreter two different methods in which client requests are dynamically executed on homomorphically encrypted data. Dynamic compilation of the requests allows to take advantage of the different optimizations performed during an off-line step on an intermediate code representation, taking the form of boolean circuits, and, moreover, to specialize the execution using runtime information. Also, for the returned encrypted results, we assess the complexity and the efficiency of the different protocols proposed in the literature in terms of overall execution time, accuracy and communication overhead.

Decision support systems are computer based programs which assists decision makers in effective and efficient decision making. Outliers are the data sets which are highly irrelevant with respect to main data sets. Spatial outliers are the spatial objects with distinct features from their surrounding neighbours. In this paper we have proposed a decision support system for strategic decision makers to establish an industry. We have shown how spatial outlier detection techniques may be used to aid decision makers. Each site is evaluated based upon multiple decision variables. Here, we use two different outlier detection algorithms to detect a site which is most unsuitable to establish an industry. We also perform an experiment on a synthetic dataset of fifteen different sites to locate ambiguous sites. We found that decision making process for site selection may be improved by using spatial outlier detection techniques.

In this paper we present a formal model of semantics for newly introduced operators of cisets, ciset relations. The notion of alternate worlds is used to formalize the information content of a ciset and ciset relations. A ciset represents a collection of (regular) sets. Similarly, a ciset relation represents a collection of (regular) relations. Once this collection has been identified, any ciset relational operator can be applied on the collection of (regular) relations represented by ciset relations involved. This approach is computationally inefficient and is introduced solely to fully explain in a formal way, the semantics of newly introduced operators.

[EN] This paper presents an alternative to cluster mixed databases. The main idea is to propose a general method to cluster mixed data sets, which is not very complex and still can reach similar levels of performance of some good algorithms. The proposed approach is based on codifying the categorical attributes and use a numerical clustering algorithm on the resulting database. The codification proposed is based on polar or spherical coordinates, it is easy to understand and to apply, the increment in the length of the input matrix is not excessively large, and the codification error can be determined for each case. The proposed codification combined with the well known k-means algorithm showed a very good performance in different benchmarks and has been compared with both, other codifications and other mixed clustering algorithms, showing a better or comparable performance in all cases., The authors acknowledge the partial funding of this work by the National projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V., The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the FEDER in the framework of the Project CGL2009-14220 and CGL2010-19591/BTE, the Spanish Institut Geografico Nacional (IGN), Institut Cartografico Valenciano (ICV), Institut Murciano de Investigacion y Desarrollo Agrario y Alimentario (IMIDA) and Banco de Terras de Galicia (Bantegal).

Enhancing the performance of large database systems depends heavily on the cost of performing join operations. When two very large tables are joined, optimizing such operation is considered one of the interesting research topics to many researchers, especially when both tables, to be joined, are very large to fit in main memory. In such case, join is usually performed by any other method than hash Join algorithms. In this paper, a novel join algorithm that is based on the use of quadtrees, is introduced. Applying the proposed algorithm on two very large tables, that are too large to fit in main memory, is proven to be fast and efficient. In the proposed new algorithm, both tables are represented by a storage efficient quadtree that is designed to handle one-dimensional arrays (1-D arrays). The algorithm works on the two 1-D arrays of the two tables to perform join operations. For the new algorithm, time and space complexities are studied. Experimental studies show the efficiency and superiority of this algorithm. The proposed join algorithm requires minimum number of I/O operations and operates in main memory with O(n log (n/k)) time complexity, where k is number of key groups with same first letter, and (n/k) is much smaller than n.

There is a growing realization that modern database management systems (DBMSs) must be able to manage data that contains uncertainties that are represented in the form of probabilistic relations. Consequently, the design of each core DBMS component must be revisited in the presence of uncertain and probabilistic information. In this paper, we study how to build histogram synopses for probabilistic relations, for the purposes of enabling both DBMS-internal decisions (such as indexing and query planning), and (possibly, user-facing) approximate query processing tools. In contrast to initial work in this area, our probabilistic histograms retain the key possible-worlds semantics of probabilistic data, allowing for more accurate, yet concise, representation of the uncertainty characteristics of data and query results. We present a variety of techniques for building optimal probabilistic histograms, each one tuned to a different choice of approximation-error metric. We show that these can be incorporated into a general Dynamic Programming (DP) framework, which generalizes that used for existing histogram constructions. The end result is a histogram where each "bucket" is approximately represented by a compact probability distribution function (PDF), which can be used as the basis for query planning and approximate query answering. We present novel, polynomial-time algorithms to find optimal probabilistic histograms for a variety of PDF-error metrics (including variation distance, sum squared error, max error and EMD 1 ). Our experimental study shows that our probabilistic histogram synopses can accurately capture the key statistical properties of uncertain data, while being much more compact to store and work with than the original uncertain relations.

Carbon dioxide Capture and Storage (CCS) is a promising contribution to reduce further increase of atmospheric CO2 emissions from fossil fuels. The CCS concept anticipates that large amounts of CO2 are going to be stored in the subsurface for the long term. Since CCS is a rather new technology, uncertainties related with the future evolution of a CO2 storage system should be addressed in a systematic framework. Performance and risk assessment frameworks are being created for the evaluation of the safeness and effectiveness of a CCS project. The recommended first step of this framework is a qualitative analysis, which encompasses the scenario analysis. The main goal of this step is to identify CO2 release scenarios, which can be supported by using a database of Features, Events and Processes (FEP), i.e. the elements that describe the characteristics and behaviour of the geological system. A qualitative FEP database tool assists to describe the storage concept and the factors which might influence the future behaviour of the storage reservoir and its surroundings. Scenario analysis using a FEP database is a crucial part of the risk assessment of a CCS project and has been applied to define release scenarios for a number of storage sites. However, further improvement is needed to facilitate a faster and more coherent analysis. For this purpose, a novel scenario analysis framework has been created, called Carbon Storage Scenario Identification Framework (CASSIF) (Yavuz et al., 2008 [1]). It is based on the three major CO2 release scenarios (well, fault and seal) from where the relevant events and processes are identified and modelled. Expert opinion is a key value within this framework. A questionnaire to be filled in by experts has been designed for the initial screening and ranking of events and processes. Features (static entities) have been separated from the events and process (the dynamic characteristics) so the focus in the expert elicitation will be on the latter. The new interface facilitates the online use of the FEP database. Together with real-time scenario formation, these improvements will greatly enhance the speed, transparency and comprehe nsiveness in the creation of subsurface CO2 release scenarios. © 2009 Elsevier Ltd. All rights reserved.

The product generation from hyperspectral sensor data has high requirements on the processing infrastructure, both hardware and software. The Airborne Prism Experiment (APEX) processing and archiving facility has been set up to provide for the automated generation of level-1 calibrated data and user-configurable on-demand product generation for higher processing levels. The system offers full reproducibility of user orders and processing parameters by employing a relational database. The flexible workflow software allows for the quick integration of novel algorithms or the definition of new processing sequences. Reprocessing of data is supported by the archiving approach. Configuration management based on the database enables the control over different versions of processing modules to be applied. The system is described with a focus on the APEX instrument; however, its generic design allows adaptation to other sensor systems.

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10 Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.

We formalise a specialised database management system model for time series using a multiresolution approach. These special purpose database systems store time series lossy compressed in a space-bounded storage. Time series can be stored at multiple resolutions, using distinct attribute aggregations and keeping its temporal attribute managed in a consistent way.The model exhibits a generic approach that facilitates its customisation to suit better the actual application requirements in a given context. The elements, the meaning of which depends on a real application, are of generic nature.Furthermore, we consider some specific time series properties that are a challenge in the multiresolution approach. We also describe a reference implementation of the model and introduce a use case based on real data. HighlightsA multiresolution database stores time series lossy compressed by data summaries.The multiresolution schema is customisable by aggregation and regularisation.The model considers the time dimension and sampling irregularities of time series.The formalism is based on set theory and is inspired by relational database theory.

In the present paper, we investigate the validity and reliability of de-facto evaluation standards, defined for measuring or predicting the quality of the interaction with spoken dialogue systems. Two experiments have been carried out with a dialogue system for controlling domestic devices. During these experiments, subjective judgments of quality have been collected by two questionnaire methods (ITU-T Rec. P.851 and SASSI), and parameters describing the interaction have been logged and annotated. Both metrics served the derivation of prediction models according to the PARADISE approach. Although the limited database allows only tentative conclusions to be drawn, the results suggest that both questionnaire methods provide valid measurements of a large number of different quality aspects; most of the perceptive dimensions underlying the subjective judgments can also be measured with a high reliability. The extracted parameters mainly describe quality aspects which are directly linked to the system, environmental and task characteristics. Used as an input to prediction models, the parameters provide helpful information for system design and optimization, but not general predictions of system usability and acceptability. © 2005 Elsevier Ltd. All rights reserved.

Effective use of labor, providing system flexibility, increasing productivity, decreasing lead times and costs are some of the most important factors influencing selection of material handling equipment. In this study, a decision support system (FUMAHES: fuzzy multi-attribute material handling equipment selection) considering these factors for material handling equipment selection is developed. FUMAHES consists of a database, a rule-based system and multi-attribute decision making modules. This database includes detailed data about equipment types and their properties. The rule-based system module provides rules, which are utilized by inference engine for determining the most proper material handling equipment type. Ultimately, a final decision is made for the most proper equipment among the alternatives of the same type using the information axiom of axiomatic design principles. Evaluation of alternatives is made for the cases of both complete and incomplete information. This paper also introduces a fuzzy information axiom approach and uses it in the selection of material handling equipment in a real case. © 2005 Elsevier Ltd. All rights reserved.

This report summarizes the viewpoints and insights gathered in the Dagstuhl Seminar on Atomicity in System Design and Execution, which was attended by 32 people from four different scientific communities: database and transaction processing systems, fault tolerance and dependable systems, formal methods for system design and correctness reasoning, and hardware architecture and programming languages. Each community presents its position in interpreting the notion of atomicity and the existing state of the art, and each community identifies scientific challenges that should be addressed in future work. In addition, the report discusses common themes across communities and strategic research problems that require multiple communities to team up for a viable solution. The general theme of how to specify, implement, compose, and reason about extended and relaxed notions of atomicity is viewed as a key piece in coping with the pressing issue of building and maintaining highly dependable systems that comprise many components with complex interaction patterns.

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.

With the growth in the popularity of the World Wide Web (WWW), there is a need to provide education and hands-on training to students in preparing materials for presentation on the Web, developing client and server-side software, and building Web-based applications. Employers seek candidates who are proficient in computer languages and tools used on the WWW. Courses on Web programming are in great demand. This paper presents the details of a course the author has developed on Web languages and Web-based applications. Key features of the course are 1) teaching of diverse Web languages and technologies using a program completion strategy, 2) discussion of Web-based applications involving database access and electrical/mechanical measuring instruments, and 3) long individual student projects and seminars.

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).

Cataloged from PDF version of article. Using one's hands in human-computer interaction increases both the effectiveness of computer usage and the speed of interaction. One way of accomplishing this goal is to utilize computer vision techniques to develop hand-gesture-based interfaces. A video database system is one application where a hand-gesture-based interface is useful, because it provides a way to specify certain queries more easily. We present a hand-gesture-based interface for a video database system to specify motion and spatiotemporal object queries. We use a regular, low-cost camera to monitor the movements and configurations of the user's hands and translate them to video queries. We conducted a user study to compare our gesture-based interface with a mouse-based interface on various types of video queries. The users evaluated the two interfaces in terms of different usability parameters, including the ease of learning, ease of use, ease of remembering (memory), naturalness, comfortable use, satisfaction, and enjoyment. The user study showed that querying video databases is a promising application area for hand-gesture-based interfaces, especially for queries involving motion and spatiotemporal relations. © 2014 Springer-Verlag London.

We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods., Comment: 7 pages, 2 figures, Paper at IJCNN 2015 (International Joint Conference on Neural Networks, 2015)

Cataloged from PDF version of article. Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. In this paper, we propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients.

The use of biological weapons represents a great concern both from a military and civilian point of view. The early detection of biological warfare agents (BWAs) in atmosphere is a huge challenge that could be addressed through UV-LIF (Ultra Violet Laser Induced Fluorescence) techniques. Fluorescence measurements of aerosol particles can provide gross discrimination between bio-agents and atmospheric background particles, In this work we intend to investigate the capability of discriminating among different biological warfare agents (BWA) through the analysis of the optical emission spectra. To accomplish this task, a deep knowledge of fluorescence features with different boundary conditions is required, in order to create a database of comparable spectral fingerprints. Preliminary results, obtained through a laboratory setup with a standard UV lamp source, showed that significant differences can be appreciated among BWAs simulants' spectra. This represents a first step towards the implementation of a spectral database and a laser-based biological stand-off identification technique.

Given the undeniable popularity of the Web, providing efficient and secure access to remote databases using a Web browser is crucial for the emerging cooperative information systems and applications. In this paper, we evaluate all currently available Java-based approaches that support persistent connections between Web clients and database servers. These approaches include Java applets, Java Sockets, Servlets, Remote Method Invocation, CORBA, and mobile agents technology. Our comparison is along the dimensions of performance and programmability. Our findings point out that best performance is not always achievable with high programmability and low resource requirements. Moreover, the mobile agent technology needs to improve its programmability while giving particular emphasis on its infrastructure. 10 4 401 422 Cited By :5

In this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features.

This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references. (C) 2000 Elsevier Science Ltd. All rights reserved.

Index selection for relational databases is an important issue which has been researched quite extensively [1–5]. In the literature, in index selection algorithms for relational databases, at most one index is considered as a candidate for each attribute of a relation. However, it is possible that more than one different type of indexes with different storage space requirements may be present as candidates for an attribute. Also, it may not be possible to eliminate locally all but one of the candidate indexes for an attribute due to different benefits and storage space requirements associated with the candidates. Thus, the algorithms available in the literature for optimal index selection may not be used when there are multiple candidates for each attribute and there is a need for a global optimization algorithm in which at most one index can be selected from a set of candidate indexes for an attribute. The problem of index selection in the presence of multiple candidate indexes for each attribute (which we call the multiple choice index selection problem) has not been addressed in the literature. In this paper, we present the multiple choice index selection problem, show that it is NP-hard, and present an algorithm which gives an approximately optimal solution within a user specified error bound in a logarithmic time order.

For signature files we propose a new false drop estimation method for databases with varying record lengths. Our approach provides more accurate estimation of the number of false drops by considering the lengths of individual records instead of using the average number of terms per record. In signature file processing, accurate estimation of the number of false drops is essential to obtain a more accurate signature file and therefore to obtain a better (query) response time. With a formal proof we show that under certain conditions the number of false drops estimated by considering the average record length is less than or equal to the precise 'expected' estimation which is based on the individual record lengths. The experiments with real data show that the proposed method accurately estimates the number of false drops and the actual response time. Depending on the space overhead, our approach obtains up to 33% and 20% response time improvements for the conventional sequential and new efficient multiframe signature file methods, respectively.

A compiler-based meta-level system for MetaProlog language is presented. Since MetaProlog is a meta-level extension of Prolog, the Warren Abstract Machine (WAM) is extended to get an efficient implementation of meta-level facilities; this extension is called the Abstract MetaProlog Engine (AMPE). Since theories and proofs are main meta-level objects in MetaProlog, we discuss their representations and implementations in detail. First, we describe how to efficiently represent theories and derivability relations. At the same time, we present the core part of the AMPE, which supports multiple theories and a fast context switching among theories in the MetaProlog system. Then we describe how to compute proofs, how to shrink the search space of a goal using partially instantiated proofs, and how to represent other control knowledge in a WAM-based system. In addition to computing proofs that are just success branches of search trees, fail branches can also be computed and used in the reasoning process. © Elsevier Science Inc., 1998.

16th International Conference on Extending Database Technology, EDBT 2013 -- 18 March 2013 through 22 March 2013 -- Genoa -- 96730 PubMed ID: 24500681 The integration of information dispersed among multiple repositories is a crucial step for accurate data analysis in various domains. In support of this goal, it is critical to devise procedures for identifying similar records across distinct data sources. At the same time, to adhere to privacy regulations and policies, such procedures should protect the confidentiality of the individuals to whom the information corresponds. Various private record linkage (PRL) protocols have been proposed to achieve this goal, involving secure multi-party computation (SMC) and similarity preserving data transformation techniques. SMC methods provide secure and accurate solutions to the PRL problem, but are prohibitively expensive in practice, mainly due to excessive computational requirements. Data transformation techniques offer more practical solutions, but incur the cost of information leakage and false matches. In this paper, we introduce a novel model for practical PRL, which 1) affords controlled and limited information leakage, 2) avoids false matches resulting from data transformation. Initially, we partition the data sources into blocks to eliminate comparisons for records that are unlikely to match. Then, to identify matches, we apply an efficient SMC technique between the candidate record pairs. To enable efficiency and privacy, our model leaks a controlled amount of obfuscated data prior to the secure computations. Applied obfuscation relies on differential privacy which provides strong privacy guarantees against adversaries with arbitrary background knowledge. In addition, we illustrate the practical nature of our approach through an empirical analysis with data derived from public voter records. Publisher's Version

Visual query systems (VQSs) are query systems for databases that use visual representations to depict the domain of interest and express related requests. VQSs can be seen as an evolution of query languages adopted into database management systems; they are designed to improve the effectiveness of the human?computer communication. Thus, their most important features are those that determine the nature of the human?computer dialogue. In order to survey and compare existing VQSs used for querying traditional databases, we first introduce a classification based on such features, namely the adopted visual representations and the interaction strategies. We then identify several user types and match the VQS classes against them, in order to understand which kind of system may be suitable for each kind of user. We also report usability experiments which support our claims. Finally, some of the most important open problems in the VQS area are described.

The paper is dedicated to issues concerning simulation and analysis of hierarchical multiprocessor systems oriented to database applications. Requirements for a parallel database system model are given. A survey and comparative analysis of known parallel database system models are presented. A new multiprocessor database system model is introduced. This model allows us to simulate and evaluate arbitrary hierarchical multiprocessor configurations in the context of the OLTP class database applications. Examples of using the database multiprocessor model for simulation study of multiprocessor database systems are presented. © Pleiades Publishing, Ltd., 2013.

In the bottom-up evaluation of logic programs and recursively defined views on databases, all generated facts are usually assumed to be stored until the end of the evaluation. Discarding facts during the evaluation, however, can considerably improve the efficiency of the evaluation: the space needed to evaluate the program, the I/O costs, the costs of maintaining and accessing indices, and the cost of eliminating duplicates may all be reduced. Given an evaluation method that is sound, complete, and does not repeat derivation steps, we consider how facts can be discarded during the evaluation without compromising these properties. We show that every such space optimization method has certain components, the first to ensure soundness and completeness, the second to avoid redundancy (i.e., repetition of derivations), and the third to reduce “fact lifetimes” (i.e., the time period for which each fact must be retained during evaluation). We present new techniques based on providing bounds on the number of derivations and uses of facts, and using monotonicity constraints for each of the first two components, and provide novel synchronization techniques for the third component of a space optimization method. We describe how techniques for each of the three components can be combined in practice to obtain a space optimization method for a program. Our results are also of importance in applications such as sequence querying, and in active databases where triggers are defined over multiple “events.”, © ACM

16th International Database Engineering and Applications Symposium, IDEAS 2012; Prague; Czech Republic; 8 August 2012 through 10 August 2012, Grid systems become very popular during the last decade because of their rapidly increasing computational capabilities. On the other hand, the advances on different domains cause enormous increase in the scale of the manipulated data. This issue augments the importance of distributed query processing and causes researchers to port their underlying environment onto the grid systems. However the dynamicity, heterogeneity and large scale characteristics of grid systems pose new problems for the distributed query processing domain. Resource allocation for query processing in grid systems is one of these problems, which attracts many researchers' attention. In this paper, we propose a new resource allocation algorithm for one relational join operator in a query considering characteristics of the grid systems. We provide theoretical analyses of the proposed algorithm and we consolidate analyses with the simulations. Copyright © 2012 ACM.

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management. © 2011 IEEE. 21 3 1211 1222 Cited By :37

Gaussian mixture models are an important tool in Bayesian decision theory. In this study, we focus on building such models over statistical database protected under differential privacy. Our approach involves querying necessary statistics from a database and building a Bayesian classifier over the noise added responses generated according to differential privacy. We formally analyze the sensitivity of our query set. Since there are multiple methods to query a statistic, either directly or indirectly, we analyze the sensitivities for different querying methods. Furthermore we establish theoretical bounds for the Bayes error for the univariate (one dimensional) case. We study the Bayes error for the multivariate (high dimensional) case in experiments with both simulated data and real life data. We discover that adding Laplace noise to a statistic under certain constraint is problematic. For example variance-covariance matrix is no longer positive definite after noise addition. We propose a heuristic method to fix the noise added variance-covariance matrix. Publisher's Version

Information is valuable asset of any organization which is stored in databases. Data in such databases may contain credit card numbers, social security number or personal medical records etc. Failing to protect these databases from intrusions will result in loss of customer's confidence and might even result in lawsuits. Traditional database security mechanism does not design to detect anomalous behavior of database users. There are number of approaches to detect intrusions in network. But they cannot detect intrusions in database. There have been very few ID mechanisms specifically tailored to database systems. We propose transaction level approach to detect malicious behavior in database systems enabled with Role Based Access Control (RBAC) mechanism., by Udai Pratap Rao, G. J. Sahani and Dhiren R. Patel