Abstract
In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.
Highlights
The penetration of smart technologies has made it increasingly convenient to capture and store the data of day to day business transactions
The experiments have been conducted to ascertain the comparative performance of MVSVEGA and most fundamental view selection algorithm HRUA (MVSHRUA) in terms of the quality of the Top-K views selected by them
Experiments were carried out to determine the appropriate value of the probability of crossover and the probability of mutation for which MVSVEGA is able to select the Top-10 views with least Total View Evaluation Cost (TVEC) for dimensions 5, 6, 7, 8, 9 and 10
Summary
The penetration of smart technologies has made it increasingly convenient to capture and store the data of day to day business transactions. Heuristics like randomized (Vijay Kumar & Kumar, 2012a, 2012c, 2015), greedy (Harinarayan et al, 1996; Shukla et al, 1998; Haider & Vijay Kumar, 2011, 2017; Vijay Kumar, 2013; Vijay Kumar & Ghoshal, 2009; Vijay Kumar & Haider, 2010, 2011a, 2011b, 2012, 2015, Vijay Kumar et al, 2010b, 2011), evolutionary (Vijay Kumar & Kumar, 2012b, 2013, 2014, 2018b) and swarm (Arun & Vijay Kumar, 2015a, 2015b, 2017a, 2017b; Vijay Kumar & Arun, 2016, 2017, Vijay Kumar et al, 2017, Kumar & Vijay Kumar, 2017, 2018a) have addressed this limitation with the HRUA Their objective was to select Top-K views from the multidimensional lattice that minimized the total cost of evaluating all the views, this cost is referred to as the Total View Evaluation Cost (TVEC) and is defined in (Vijay Kumar & Kumar, 2012a, 2012b, 2012c, 2013, 2014, 2015) as: N ( ) ∑ ∑ TVEC VTop−K =. Experiment-based comparison of the proposed view selection algorithm with HRUA is carried out in terms of the quality of the Top-K views selected by them
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