2:13 am - Saturday December 20, 2014

Robust module-based data management

Robust module-based data management

Technology Used: Java/J2EE

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 dataset using the resulting schema. The existing definitions of modules is extended and 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 from W3C.

EXISTING SYSTEM

 Recent work in description logics provides different solutions to achieve such a reuse of a reference ontology-based DMS.
 Existing definitions of modules in the literature basically resort to the notion of (deductive) conservative extension of a schema or of uniform interpolant of a schema.
 Previous work formalizes schemas written in DLs and discusses their connection.
 Up to now, conservative extension has been considered for defining a module as a subset of a schema.

Disadvantages
 Forgetting about non-interesting relations of a schema.
 Data replication is possible

PROPOSED SYSTEM

 The reuse of a reference ontology-based DMS is revisited in order to build a new DMS with specific needs.
 The system not only considers the design of a module-based DMS (i.e., how to extract a module from a ontological schema): it is also studied how a module-based DMS can benefit from the reference DMS to enhance its own data management skills.
 RDF is the W3C’s Semantic Web data model, which is rapidly spreading in more and more applications, and can be seen as a simple relational model restricted to unary and binary predicates.
 DL-lite comes with efficient inference algorithms for querying RDF data through (DL-lite) ontologies and for checking data consistency w.r.t. integrity constraints expressed in DL-lite.

Advantages
 Efficiently managing large RDF datasets
 Robust module-based DMS evolves safely w.r.t. both the schema and the data of the reference DMS

Download Ieee Projects

Filed in: Data Mining

No comments yet.

Leave a Reply