Search results for "Data model"

showing 10 items of 162 documents

LinkedSaeima: A Linked Open Dataset of Latvia’s Parliamentary Debates

2019

This paper describes the LinkedSaeima dataset that contains structured data about Latvia’s parliamentary debates from 1993 until 2017. This information is published at http://dati.saeima.korpuss.lv as Linked Open Data. It is a part of the Corpus of Saeima (the Parliament of Latvia) released as open data for multidisciplinary research. The data model of LinkedSaeima follows the data structure of the LinkedEP dataset with a few modifications. The dataset is augmented with links to the Wikidata knowledge base that provide additional information about the speakers and named entities mentioned in the corpus.

Thesaurus (information retrieval)business.industryParliamentComputer sciencemedia_common.quotation_subject05 social sciences02 engineering and technologycomputer.file_formatLinked dataData structureWorld Wide WebOpen dataData modelKnowledge base020204 information systems0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesRDFbusinesscomputer050104 developmental & child psychologymedia_common
researchProduct

Framework for Evaluating the Version Management Capabilities of a Class of UML Modeling Tools from the Viewpoint of Multi-Site, Multi-Partner Product…

2010

UML models are widely used in software product line engineering for activities such as modeling the software product line reference architecture, detailed design, and automation of software code generation and testing. But in high-tech companies, modeling activities are typically distributed across multiple sites and involve multiple partners in different countries, thus complicating model management. Today's UML modeling tools support sophisticated version management for managing parallel and distributed modeling. However, the literature does not provide a comprehensive set of industrial-level criteria to evaluate the version management capabilities of UML tools. This article's contributio…

UML toolComputer sciencebusiness.industryApplications of UMLDiagramming softwarecomputer.software_genreData modelingSoftwareUnified Modeling LanguageNew product developmentCode generationReference architectureModel-driven architectureSoftware product lineSoftware architecturebusinessSoftware engineeringcomputercomputer.programming_language2010 43rd Hawaii International Conference on System Sciences
researchProduct

Interoperability between Distributed Systems and Web-Services Composition

2009

An information system is a multi-axis system characterized by a “data” axis, a “behavioral” axis, and a “communication” axis. The data axis corresponds to the structural and schematic technologies used to store data into the system. The behavioral axis represents management and production processes carried out by the system and corresponding technologies. The processes can interact with the data to extract, generate, and store data. The communication axis relates to the network used to exchange data and activate processes between geographically distant users or machines. Nowadays, technologies required for interoperability are extended to deal with the semantic aspect of the information sys…

World Wide WebInteroperationFlat file databaseComputer scienceSOAPcomputer.internet_protocolInteroperabilityInformation systemOntology (information science)Web servicecomputer.software_genrecomputerData modeling
researchProduct

Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context

2016

International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…

[ INFO ] Computer Science [cs]Computer scienceMaintenanceBig dataAdaptive learningContext (language use)Multi-label classification02 engineering and technologyOntology (information science)[INFO] Computer Science [cs]Machine learningcomputer.software_genreAdaptive LearningData modeling[SPI.AUTO]Engineering Sciences [physics]/AutomaticMachine LearningCold start020204 information systems[ SPI.AUTO ] Engineering Sciences [physics]/AutomaticMachine learning0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Multi-Label ClassificationMulti-label classificationbusiness.industryOntologyOntology-based data integration[SPI.AUTO] Engineering Sciences [physics]/Automatic020201 artificial intelligence & image processingAdaptive learningArtificial intelligencebusinesscomputer
researchProduct

Towards A Twitter Observatory: A Multi-Paradigm Framework For Collecting, Storing And Analysing Tweets

2016

International audience; In this article we show how a multi-paradigm framework can fulfil the requirements of tweets analysis and reduce the waiting time for researchers that use computational resources and storage systems to support large-scale data analysis. The originality of our approach is to combine concerns about data harvesting, data storage, data analysis and data visualisation into a framework that supports inductive reasoning in multidisciplinary scientific research. Our main contribution is a polyglot storage system with a generic data model to support logical data independence and a set of tools that can provide a suitable solution for mixing different types of algorithms in or…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][ INFO ] Computer Science [cs]Computer scienceknowledge discovery02 engineering and technology[INFO] Computer Science [cs][INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Data modelingmassive datasetsopen source softwareData visualization[ INFO.INFO-IT ] Computer Science [cs]/Information Theory [cs.IT]polyglot storage020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Twitter analysis . SystemsComputingMilieux_MISCELLANEOUS[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industryPolyglotInductive reasoningData science[SPI.TRON] Engineering Sciences [physics]/ElectronicsData independence[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsData model[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT]Data architecturebusinessSoftware architecture
researchProduct

Semantic oriented data structuration for MABS Application to BIM

2013

International audience; This paper presents a multiagent-based simulation approach to qualify the usage of buildings from the design phase. Our approach combines ontology and evolution process based on machine learning algorithms. The ontology relies on semantic data structures for the representation of environment components, agent knowledge and all data generated during the simulation.

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationComputer scienceProcess (engineering)0211 other engineering and technologies020101 civil engineering02 engineering and technologyOntology (information science)Semantic data modelcomputer.software_genre0201 civil engineering021105 building & constructionUpper ontologyRepresentation (mathematics)business.industryOntology-based data integration[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationDesign phaseBuilding information modeling[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA][ INFO.INFO-MA ] Computer Science [cs]/Multiagent Systems [cs.MA][INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA][INFO.INFO-MO] Computer Science [cs]/Modeling and SimulationData miningbusinessSoftware engineeringcomputer
researchProduct

A Neural Network Meta-Model and its Application for Manufacturing

2015

International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]0209 industrial biotechnology[SPI] Engineering Sciences [physics]Computer scienceneural networkBig dataContext (language use)02 engineering and technologycomputer.software_genreMachine learningCompetitive advantageData modeling[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]data analyticsArtificial neural networkbusiness.industrymeta-modelMetamodelingmanufacturingAnalyticsSustainabilityPredictive Model Markup LanguageData analysis020201 artificial intelligence & image processingData miningArtificial intelligencebusinesscomputer
researchProduct

Automated uncertainty quantification analysis using a system model and data

2015

International audience; Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]generic modeling environment[SPI] Engineering Sciences [physics]Computer scienceuncertainty quantificationMachine learningcomputer.software_genre01 natural sciencesData modelingSystem model[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]010104 statistics & probability03 medical and health sciences[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]Sensitivity analysis0101 mathematicsUncertainty quantification[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]030304 developmental biologyautomation0303 health sciencesMathematical modelbusiness.industryConditional probabilityBayesian networkmeta-modelMetamodelingBayesian networkProbability distributionData miningArtificial intelligencebusinesscomputer
researchProduct

Semantic Trajectory Modeling for Dynamic Built Environments

2017

This paper presents a data model to capture moving and changing objects in the context of dynamic built environment. Building elements are subject to change which represents semantic trajectories crossing trajectories of users. These semantic trajectories in dynamics built environment permit to capture fine-grained activities and behaviors of users and objects. The data model is based on ontology and description logics to capture logic constraints on semantic trajectories.

[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO]Computer science[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]0211 other engineering and technologies[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]Context (language use)[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]02 engineering and technologyOntology (information science)SemanticsData modelingData modelDescription logicHuman–computer interaction020204 information systems0202 electrical engineering electronic engineering information engineeringTrajectory[ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]Built environment[ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]ComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering
researchProduct

Semantic Issues About 3D Spatial Data Modelling Using CityGML

2015

International audience

[SHS.GEO] Humanities and Social Sciences/Geography[SHS.GEO]Humanities and Social Sciences/Geographyspatial data modellingComputingMilieux_MISCELLANEOUS[ SHS.GEO ] Humanities and Social Sciences/Geography
researchProduct