Abstract
Bottleneck issues handled in the field of information retrieval are analysis of query and management of data storage. Hadoop is a large scale environment that is supported with larger storage and faster processing. Even though, it suffers from these challenging issues while the number of information requesters is higher. This paper addresses these two bottleneck issues in Hadoop by retrieving the information with the design of Query Analysis and Ontology-based Clustering (QAOC) architecture. In QAOC architecture, the components involved are query manager, scheduler and data management. Initially the query manager consolidates the query if they are similar; hereby the searching time is effectively minimized. Then the user query is scheduled in neuro-fuzzy by computing query arrival time, query length and query expiry time. The data management in the back-end is operated by weighted ontology-based clustering method to cluster the data based on their relevancy. The scheduled user query is searched in the ontology based balanced binary tree and lastly the relevant results are ranked using Okapi BM25 and delivered to user. This QAOC architecture is experimented on Hadoop 2.7 and the results are compared in terms of execution time, processing speed and memory consumption.
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