An efficient query matching algorithm for relational data semantic cache

Data access latency can be reduced for databases by using caching. Semantic caching enhances the performance of normal caching by locally answering the partially overlapped queries. Query processing (generation of probe and remainder query from the incoming queries) and cache management need to be addressed in its totality to really enjoy these benefits. That is, there is a need of correct, complete and efficient algorithms to process incoming queries and to manage semantic and data cache. In this paper, we address this issue in the context of query processing. We have observed that the algorithm proposed by Q. Ren and his colleagues has some inefficiencies and redundancies. To overcome these inefficiencies and redundancies, we have proposed an algorithm for query matching with hierarchal stored query semantics. Proposed algorithm performs matching of stored semantics in cache with semantics of incoming query. It also has capability to generate amending query efficiently and rejects incorrect queries at initial level. Comparison of proposed algorithm is made with existing algorithm. Complexity of proposed query matching algorithm is O(n) which is smaller then the existing which have O(2n-1), n is number of attributes in a relation. Also, the proposed algorithm is capable to stop the useless processing as was done in the previous algorithms.