Search results for "Database"

showing 10 items of 2136 documents

Extending SPARQL with Temporal Logic

2009

The data integration and sharing activities carried on in the framework of the Semantic Web lead to large knowledge bases that must be queried, analyzed, and exploited efficiently. Many of the knowledge representation languages of the Semantic Web, starting with RDF, are based on directed, labeled graphs, which can be also manipulated using graph algorithms and tools coming from other domains. In this paper, we propose an analysis approach of RDF graphs by reusing the verification technology developed for concurrent systems. To this purpose, we define a translation from the SPARQL query language into XTL, a general-purpose graph manipulation language implemented in the CADP verification too…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebInformationSystems_DATABASEMANAGEMENTlabeled transition system[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languagesSPARQL[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodel checkingRDFACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]temporal logicACM : D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languages[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationverification
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A new approach based on NμSMV Model to query semantic graph

2011

International audience; The language most frequently used to represent the semantic graphs is the RDF (W3C standard for meta-modeling). The construction of semantic graphs is a source of numerous errors of interpretation. Processing of large semantic graphs can be a limit to use semantics in modern information systems. The work presented in this paper is part of a new research at the border between two areas: the semantic web and the model checking. For this, we developed a tool, RDF2NμSMV, which converts RDF graphs into NμSMV language. This conversion aims checking the semantic graphs with the model checker NμSMV in order to verify the consistency of the data. The data integration and shar…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/WebNμSMVTemporal logic02 engineering and technologycomputer.software_genreQuery languageSPARQLtemporal logic queryRDFModel CheckingSemantic similarity020204 information systemsSemantic computing0202 electrical engineering electronic engineering information engineeringSPARQLRDFSemantic WebGraph databaseInformation retrieval[INFO.INFO-WB]Computer Science [cs]/Webcomputer.file_format[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationAbstract semantic graphSemantic graphQuery checking020201 artificial intelligence & image processing[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationcomputer
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The Hierarchical Agglomerative Clustering with Gower index: a methodology for automatic design of OLAP cube in ecological data processing context

2015

In Press, Corrected Proof; International audience; The OLAP systems can be an improvement for ecological studies. In fact, ecology studies, follows and analyzes phenomenon across space and time and according to several parameters. OLAP systems can provide to ecologists browsing in a large dataset. One focus of the current research on OLAP system is the automatic design of OLAP cubes and of data warehouse schemas. This kind of works makes accessible OLAP technology to non information technology experts. But to be efficient, the automatic OLAP building must take into account various cases. Moreover the OLAP technology is based on the concept of hierarchy. Thereby the hierarchical clustering m…

[ INFO.INFO-NA ] Computer Science [cs]/Numerical Analysis [cs.NA]Computer scienceContext (language use)02 engineering and technologycomputer.software_genre020204 information systems0202 electrical engineering electronic engineering information engineeringDimension (data warehouse)Cluster analysisEcology Evolution Behavior and Systematics[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]OLAPEcologyAutomatic designApplied MathematicsEcological ModelingOnline analytical processing[ STAT.AP ] Statistics [stat]/Applications [stat.AP]InformationSystems_DATABASEMANAGEMENTHierarchical agglomerative clustering[INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA]Missing dataData warehouseComputer Science ApplicationsHierarchical clustering[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Computational Theory and MathematicsModeling and SimulationOLAP cube020201 artificial intelligence & image processingData mining[SDE.BE]Environmental Sciences/Biodiversity and EcologyBird populationcomputer
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Filtering and emission area identification in the Time Resolved Imaging data

2012

Abstract Time Resolved Imaging (TRI) acquisitions allow precise timing analysis of emission spots. Up to date technologies deeply challenge their isolation by hiding the weak ones, under sizing or over sizing visually detectable emission spots and finally by jeopardizing timing resolution. We report on an algorithm based on 1 and 2D signal processing tools which automates the identification of emission sites and optimizes separation between noise and useful signal, even for weak spots surrounding strong emission areas. The application of the algorithm on several sets of data from different types of devices and their results are also discussed.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciencesImaging dataSignal[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringIsolation (database systems)[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing010302 applied physicsSignal processingNoise (signal processing)business.industryPhoto EmissionStatic timing analysisPattern recognitionSizingIdentification (information)IC Failure AnalysisImage Thresholding[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Cluster matching in time resolved imaging for VLSI analysis

2014

International audience; If scaling has the benefit of enabling manufacturers to design tomorrow's integrated circuits, from the failure analyst point of view it also has the drawback of making devices more complex. The test sequence for modern VLSI can be quite long, with thousands of vector. Dynamic photon emission databases can contain millions of photons representing thousands of state changes in the region of interest. Finding a candidate location where to perform physical analysis is quite challenging, especially if the fault occurs on a single vector. In this paper, we suggest a new methodology to find single vector fault in dynamic photon emission database. The process is applied at …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyIntegrated circuitFault (power engineering)computer.software_genre01 natural sciencesk-nearest neighbors algorithmlaw.invention[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinglaw0103 physical sciences0202 electrical engineering electronic engineering information engineeringPoint (geometry)[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/MicroelectronicsCluster analysisComputer Science::Databases[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing010302 applied physicsVery-large-scale integrationProcess (computing)Computer engineering[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics020201 artificial intelligence & image processingData mining[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerProceedings of the 21th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)
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Cross-Media Color Reproduction and Display Characterization

2012

International audience; In this chapter, we present the problem of cross-media color reproduction, that is, how to achieve consistent reproduction of images in different media with different technologies. Of particular relevance for the color image processing community is displays, whose color properties have not been extensively covered in previous literature. Therefore, we go more in depth concerning how to model displays in order to achieve colorimetric consistency. The structure of this chapter is as follows: After a short introduction, we introduce the field of cross-media color reproduction, including a brief description of current standards for color management, the concept of colori…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor reproductionCross media[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyBiologyColor management01 natural sciencesCharacterization (materials science)law.invention010309 opticsConsistency (database systems)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinglaw0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingColor image processingRelevance (information retrieval)Computer visionArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Enhancing scientific information systems with semantic annotations

2013

International audience; Scientific Information Systems aim to produce or improve knowledge on a subject through activities of research and development. The management of scientific dat a requires some essential properties. We propose SemLab an architecture that sup ports interoperability, data quality and extensibility through a unique paradigm: semantic annotation. We present two app lications that validate our architecture.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceInteroperability[INFO.INFO-WB]Computer Science [cs]/Web[ INFO.INFO-WB ] Computer Science [cs]/WebSubject (documents)02 engineering and technologyOntology (information science)Semantic interoperabilityData science[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]020204 information systemsData qualitySemantic computing0202 electrical engineering electronic engineering information engineeringInformation system[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]020201 artificial intelligence & image processing[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Automatic ontology-based User Profile Learning from heterogeneous Web Resources in a Big Data Context

2013

International audience; The Web has developed to the biggest source of information and entertainment in the world. By its size, its adaptability and flexibility, it challenged our current paradigms on information sharing in several areas. By offering everybody the opportunity to release own contents in a fast and cheap way, the Web already led to a revolution of the traditional publishing world and just now, it commences to change the perspective on advertisements. With the possibility to adapt the contents displayed on a page dynamically based on the viewer's context, campaigns launched to target rough customer groups will become an element of the past. However, this new ecosystem, that re…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS][INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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ArchaeoKM: Managing Archaeological data through Archaeological Knowledge

2010

International audience; Nowadays, the use of ontologies in the field of the archaeology is a new direction of research that is not fully tested. Some results are very encouraging and challenge the vision of archaeological sites which cannot be modeled semantically. Most of the re-searches based on ontology technologies in the arc-haeology domain are based on the research of finding on a common ground for the interoperability and the integration of data. Papers like (LANG 2009), (KOLLIAS 2008) cover these research. The project ArchaeoKM is a shift from such researches. It focus-es on using the knowledge possessed by the archaeo-logists to model the data of an archaeological project, and to d…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][SHS.ARCHEO] Humanities and Social Sciences/Archaeology and Prehistory[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory[ SHS.ARCHEO ] Humanities and Social Sciences/Archaeology and Prehistoryrulesindustrial archaeology[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]ontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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