Atul Thakare, Parag Deshpande


Selection of a proper set of views to materialize plays an important role in
database performance. There are many methods of view selection which uses different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient, scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. Tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. Query Cost model achieves the objective of maximizing the performance benefits from the final view set which is derived from the frequent view set given by tree mining algorithm. Performance benefit of a query is defined as a function of query
frequency, query creation cost, and query maintenance cost. The experimental results shows that the proposed method is successful in recommending a solution which is fairly close to optimal solution.


Database Management Systems , Data Warehousing and Data Mining , Query Optimization , Graph Mining , Algorithms for Parallel Computing , Evolutionary computations , Genetic Algorithms.

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DOI: https://doi.org/10.7494/csci.2018.19.4.3006


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