A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases
Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (R...
Ausführliche Beschreibung
Autor*in: |
Ayanso, Anteneh [verfasserIn] Goes, Paulo B. [author] Mehta, Kumar [author] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2009 |
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Online-Ressource |
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Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
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Übergeordnetes Werk: |
In: Journal of database management - Hershey, Pa : IGI Global, 2000, 20(2009), 4, Seite 1-25 |
Übergeordnetes Werk: |
volume:20 ; year:2009 ; number:4 ; pages:1-25 |
Links: |
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DOI / URN: |
10.4018/jdm.2009062501 |
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NLEJ244518653 |
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10.4018/jdm.2009062501 doi (DE-627)NLEJ244518653 (VZGNL)10.4018/jdm.2009062501 DE-627 ger DE-627 rakwb eng Ayanso, Anteneh verfasserin aut A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases 2009 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods IGI Global InfoSci Journals Archive 2000 - 2012 Relational Databases Top-K Query Query-Mapping Query Processing Cost Model Tradeoff Analysis Uncertainty Modeling Goes, Paulo B. author aut Mehta, Kumar author aut In Journal of database management Hershey, Pa : IGI Global, 2000 20(2009), 4, Seite 1-25 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:20 year:2009 number:4 pages:1-25 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2009062501 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2009062501&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 20 2009 4 1-25 |
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10.4018/jdm.2009062501 doi (DE-627)NLEJ244518653 (VZGNL)10.4018/jdm.2009062501 DE-627 ger DE-627 rakwb eng Ayanso, Anteneh verfasserin aut A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases 2009 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods IGI Global InfoSci Journals Archive 2000 - 2012 Relational Databases Top-K Query Query-Mapping Query Processing Cost Model Tradeoff Analysis Uncertainty Modeling Goes, Paulo B. author aut Mehta, Kumar author aut In Journal of database management Hershey, Pa : IGI Global, 2000 20(2009), 4, Seite 1-25 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:20 year:2009 number:4 pages:1-25 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2009062501 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2009062501&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 20 2009 4 1-25 |
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A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases |
abstract |
Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods |
abstractGer |
Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods |
abstract_unstemmed |
Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods |
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A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases |
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