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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| Vydáno v: | Computers & operations research Ročník 121; s. 104983 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.09.2020
Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | •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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| AbstractList | •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. 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. |
| ArticleNumber | 104983 |
| Author | Dayarian, Iman Mahmoodian, Vahid Eslami, Mehrad Tu, Yicheng Charkhgard, Hadi |
| Author_xml | – sequence: 1 givenname: Mehrad orcidid: 0000-0001-9415-0541 surname: Eslami fullname: Eslami, Mehrad organization: Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA – sequence: 2 givenname: Vahid surname: Mahmoodian fullname: Mahmoodian, Vahid organization: Department of Industrial and Management Systems Engineering, University of South Florida, FL 33620, USA – sequence: 3 givenname: Iman orcidid: 0000-0002-9179-1973 surname: Dayarian fullname: Dayarian, Iman organization: Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL 35487, USA – sequence: 4 givenname: Hadi orcidid: 0000-0001-5416-6960 surname: Charkhgard fullname: Charkhgard, Hadi email: hcharkhgard@usf.edu organization: Department of Industrial and Management Systems Engineering, University of South Florida, FL 33620, USA – sequence: 5 givenname: Yicheng surname: Tu fullname: Tu, Yicheng organization: Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA |
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| Keywords | Database systems Batching problem Restricted-cardinality search method Linear regression Mixed binary quadratic programming |
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| SubjectTerms | 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 |
| Title | Query batching optimization in database systems |
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