Ontology-driven relevance reasoning architecture for data integration techniques

In order to execute a userpsilas query in a data integration system, the query execution process needs to be optimized. Before executing a query at real time, relevant and effective data sources must be identified. In this paper we propose an ontology-driven relevance reasoning architecture for future data integration techniques that will improve the response time for queries during the relevance reasoning process. Ontology has played a vital role to develop various component of the architecture. Source descriptions are plotted over the bitmap index in an intelligent and improved manner. Despite taking a lot of time in traversing local ontologies of source descriptions, bitmap index is exploited in relevance reasoning to identify the relevant and most effective data sources for userpsilas query. These identified data sources are ranked based on their relevance to the userpsilas query and then queried accordingly. A distinguished feature of the system is that it facilitates the user to write the query in terms of their local ontology concepts as well as global ontology concepts. A brief discussion is done on the results of the experimental study of proposed methodology for relevance reasoning and improvements are shown as compared to the previous systems.