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Algebraic query optimization for distributed top-k queries.

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Title: Algebraic query optimization for distributed top-k queries.
Authors: Neumann, Thomas, Michel, Sebastian
Source: Informatik - Forschung und Entwicklung; Jun2007, Vol. 21 Issue 3, p197-211, 15p
Abstract (English): Distributed top-k query processing is increasingly becoming an essential functionality in a large number of emerging application classes. This paper addresses the efficient algebraic optimization of top-k queries in wide-area distributed data repositories where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers and the computational costs include network latency, bandwidth consumption, and local peer work. We use a dynamic programming approach to find the optimal execution plan using compact data synopses for selectivity estimation that is the basis for our cost model. The optimized query is executed in a hierarchical way involving a small and fixed number of communication phases. We have performed experiments on real web data that show the benefits of distributed top-k query optimization both in network resource consumption and query response time. [ABSTRACT FROM AUTHOR]
Abstract (German): In dieser Arbeit beschäftigen wir uns mit der Optimierung verteilter top-k Anfragen, bei denen die Daten auf verschiedene Rechner verteilt sind. Die Kosten, die es zu minimieren gilt, umfassen die Netzwerklast, den Verbrauch lokaler Rechenleistung und letztendlich die Zeit der Anfrageausführung. Wir benutzen dynamische Programmierung, um den optimalen Anfrageplan zu finden. Die Kostenschätzung basiert dabei auf kompakten Repräsentationen der eigentlichen Score-Verteilungen. Die optimierte Anfrage wird anschließend in einer hierachischen Weise ausgeführt, bei der nur eine kleine und fest vorgegebene Anzahl von Kommunikationsschritten angewendet wird. Umfassende Experimente mit Daten aus der realen Welt zeigen beachtliche Gewinne sowohl in der Reduktion der Netzwerklast als auch in der Reduktion der Anfragezeit. [ABSTRACT FROM AUTHOR]
Copyright of Informatik - Forschung und Entwicklung is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Algebraic query optimization for distributed top-k queries.
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  Data: <searchLink fieldCode="AR" term="%22Neumann%2C+Thomas%22">Neumann, Thomas</searchLink><br /><searchLink fieldCode="AR" term="%22Michel%2C+Sebastian%22">Michel, Sebastian</searchLink>
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  Data: Informatik - Forschung und Entwicklung; Jun2007, Vol. 21 Issue 3, p197-211, 15p
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  Label: Abstract (English)
  Group: Ab
  Data: Distributed top-k query processing is increasingly becoming an essential functionality in a large number of emerging application classes. This paper addresses the efficient algebraic optimization of top-k queries in wide-area distributed data repositories where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers and the computational costs include network latency, bandwidth consumption, and local peer work. We use a dynamic programming approach to find the optimal execution plan using compact data synopses for selectivity estimation that is the basis for our cost model. The optimized query is executed in a hierarchical way involving a small and fixed number of communication phases. We have performed experiments on real web data that show the benefits of distributed top-k query optimization both in network resource consumption and query response time. [ABSTRACT FROM AUTHOR]
– Name: AbstractNonEng
  Label: Abstract (German)
  Group: Ab
  Data: In dieser Arbeit beschäftigen wir uns mit der Optimierung verteilter top-k Anfragen, bei denen die Daten auf verschiedene Rechner verteilt sind. Die Kosten, die es zu minimieren gilt, umfassen die Netzwerklast, den Verbrauch lokaler Rechenleistung und letztendlich die Zeit der Anfrageausführung. Wir benutzen dynamische Programmierung, um den optimalen Anfrageplan zu finden. Die Kostenschätzung basiert dabei auf kompakten Repräsentationen der eigentlichen Score-Verteilungen. Die optimierte Anfrage wird anschließend in einer hierachischen Weise ausgeführt, bei der nur eine kleine und fest vorgegebene Anzahl von Kommunikationsschritten angewendet wird. Umfassende Experimente mit Daten aus der realen Welt zeigen beachtliche Gewinne sowohl in der Reduktion der Netzwerklast als auch in der Reduktion der Anfragezeit. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Informatik - Forschung und Entwicklung is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2007
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