Robust Module-Based Data Management

The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs-a module-,...

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Vydané v:IEEE transactions on knowledge and data engineering Ročník 25; číslo 3; s. 648 - 661
Hlavní autori: Goasdoue, Francois, Rousset, M-C
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.03.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Edícia:Transactions on Knowledge and Data Engineering
Predmet:
algorithms for data and knowledge management
Artificial Intelligence
Computer Science
Data management
Data models
database management
Design engineering
Foundations
intelligent web services
Knowledge management
Models and principles
Ontology
personalization
Personalizing
Reference systems
Resource description framework
Robustness
Semantic Web
Studies
Trends
ISSN:1041-4347, 1558-2191
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Popis
Shrnutí:The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs-a module-, possibly personalizing it with extra constraints w.r.t. the application under construction, and then managing a data set using the resulting schema. In this paper, we extend the existing definitions of modules and we introduce novel properties of robustness that provide means for checking easily that a robust module-based DMS evolves safely w.r.t. both the schema and the data of the reference DMS. We carry out our investigations in the setting of description logics which underlie modern ontology languages, like RDFS, OWL, and OWL2 from W3C. Notably, we focus on the DL-lite A dialect of the DL-lite family, which encompasses the foundations of the QL profile of OWL2 (i.e., DL-lite R ): the W3C recommendation for efficiently managing large data sets.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2011.255