Bitmap Index in Ontology Mapping for Data Integration

Abstract

Selecting a relevant data source among the available ones in a data integration system plays vital role in optimizing query performance. The sources are heterogeneous and autonomous and can join and leave an integration system arbitrarily. Some sources may not contribute significantly to a user query because they are not relevant to it. Executing a user query against all available sources consumes resources unreasonably and makes the query processing expensive. The existing techniques for source selection take significant time in traversing source descriptions. Consequently, query response time degrades in coping with the growing number of available sources. Semantic heterogeneities of data add further complexity to source selection. As a first step, we employed ontologies for identifying the relevant data elements of individual sources for particular queries and semantic relationships among these data elements. Then, we mapped local ontologies with domain ontology through a bitmap index. In spite of traversing the local ontologies, our proposed system utilizes the bitmap index to perform relevance reasoning in order to optimize the user query response. A prototype system has been designed and implemented to validate the system. We evaluated the prototype system for query response time and it was improved due to the incorporation of a bitmap index.