Optimize Scale Independent Queries with Invariant Computation

Abstract

Big data deals with the prodigiously and sizably voluminous volume of data engendered at high speed and it is arduous to process and manage with the subsisting database management tools. Query processing in astronomically immense data is a challenging task and frequently encounters problem. To overcome the complexity involved in processing the larger dataset, query optimization is the promising solution. Performance is a bottleneck, when complicated queries access an unbounded amount of data, resulting in high response time using the existing query optimization technique. In proposed work, to surmount this issue, scale independence is identified with access schema and query execution is optimized with invariant data. With astronomically immense precomputation and incremental computation dataset is used for querying. In precomputation approach, the invariant data is computed afore execution and thus resulting in lesser computation time during query processing. Incremental computation technique is applied to optimize the query for the streaming data. Thus, the invariant data is computed incrementally with the incipiently inserted data along with the precomputed data and then utilized for the query processing. By applying these approaches for optimizing scale independent queries, the performance can be ameliorated with tolerable response time.