Multi-Objective Parametric Query Optimization.
We propose a generalization of the classical database query optimization problem: multi-objective parametric query (MPQ) optimization. MPQ compares alternative processing plans according to multiple execution cost metrics. It also models missing pieces of information on which plan costs depend upon...
| Publicado en: | Communications of the ACM Vol. 60; no. 10; pp. 81 - 90 |
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| Autores principales: | , |
| Formato: | Artículo |
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Association for Computing Machinery
Oct2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=125351962&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 125351962 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Oct2017 vid: 60 iid: 10 pid: 68 pub: Association for Computing Machinery artinfo: ui: 125351962 10.1145/3068612 ppf: 81 ppct: 9 formats: tig: atl: Multi-Objective Parametric Query Optimization. aug: au: Trummer, Immanuel Koch, Christoph affil: Cornell University, Computer Science Department, Ithaca, NY. Ecole Polytechnique Fédérale de Lausanne, EPFL DATA Lab, Lausanne, Switzerland. su: Query languages (Computer science) Program transformation Data structures Databases Algorithms sug: subj: Query languages (Computer science) Program transformation Data structures Databases Algorithms ab: We propose a generalization of the classical database query optimization problem: multi-objective parametric query (MPQ) optimization. MPQ compares alternative processing plans according to multiple execution cost metrics. It also models missing pieces of information on which plan costs depend upon as parameters. Both features are crucial to model query processing on modern data processing platforms. MPQ generalizes previously proposed query optimization variants, such as multi-objective query optimization, parametric query optimization, and traditional query optimization. We show, however, that the MPQ problem has different properties than prior variants and solving it requires novel methods. We present an algorithm that solves the MPQ problem and finds, for a given query, the set of all relevant query plans. This set contains all plans that realize optimal execution cost tradeoffs for any combination of parameter values. Our algorithm is based on dynamic programming and recursively constructs relevant query plans by combining relevant plans for query parts. We assume that all plan execution cost functions are piecewise-linear in the parameters. We use linear programming to compare alternative plans and to identify plans that are not relevant. We present a complexity analysis of our algorithm and experimentally evaluate its performance. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2017 holdings: @attributes: islocal: N |
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