Query batching optimization in database systems

•We study the query batching problem in database systems.•We develop a mixed binary quadratic program for the problem.•Our model relies on a novel regression-based processing-time prediction function.•We prove the proposed mixed binary quadratic program is an NP-hard problem.•We develop two heuristi...

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Vydané v:Computers & operations research Ročník 121; s. 104983
Hlavní autori: Eslami, Mehrad, Mahmoodian, Vahid, Dayarian, Iman, Charkhgard, Hadi, Tu, Yicheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 01.09.2020
Pergamon Press Inc
Predmet:
Algorithms
Batching problem
Database systems
Heuristic methods
Linear regression
Mixed binary quadratic programming
Operations research
Optimization
Optimization techniques
Queries
Query processing
Restricted-cardinality search method
System effectiveness
Database systems
Batching problem
Restricted-cardinality search method
Linear regression
Mixed binary quadratic programming
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:•We study the query batching problem in database systems.•We develop a mixed binary quadratic program for the problem.•Our model relies on a novel regression-based processing-time prediction function.•We prove the proposed mixed binary quadratic program is an NP-hard problem.•We develop two heuristics and show their efficacy on three database benchmarks. Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong resemblance to order batching in the warehousing context. While the latter is a well-studied problem, the literature on optimization techniques for query batching problem is quite scarce/nonexistent. In this study, we develop a Mixed Binary Quadratic Program (MBQP) to optimize query-batching in a database system. This model uses the coefficients of a linear regression on the query retrieval time, trained by a large set of randomly generated query sets. We also propose two heuristics, the so-called restricted-cardinality search methods I and II, for solving the proposed MBQP. To demonstrate the effectiveness of our proposed techniques, we conduct a comprehensive computational study over randomly generated instances of three well-known database benchmarks. The computational results show that when the proposed MBQP is solved using the designed heuristics, an improvement of up to 61.8% in retrieving time is achieved.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2020.104983