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  Counting at Large: Efficient Cardinality Estimation in Internet-Scale Data Networks

Ntarmos, N., Triantafillou, P., & Weikum, G. (2006). Counting at Large: Efficient Cardinality Estimation in Internet-Scale Data Networks. In Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006) (pp. 1-10). Los Alamitos, USA: IEEE.

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 Urheber:
Ntarmos, Nikos, Autor
Triantafillou, Peter1, Autor           
Weikum, Gerhard1, Autor           
Liu, Ling, Herausgeber
Reuter, Andreas, Herausgeber
Whang, Kyu-Young, Herausgeber
Zhang, Jianjun, Herausgeber
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 Zusammenfassung: Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for the efficient deployment and access of emerging internetscale information systems. Examples of such applications range from optimizing query access plans in internet-scale databases, to evaluating the significance (rank/score) of various data items in information retrieval applications. The key constraints that any acceptable solution must satisfy are: (i) efficiency: the number of nodes that need be contacted for counting purposes must be small in order to enjoy small latency and bandwidth requirements; (ii) scalability, seemingly contradicting the efficiency goal: arbitrarily large numbers of nodes nay need to add elements to a (multi-) set, which dictates the need for a highly distributed solution, avoiding server-based scalability, bottleneck, and availability problems; (iii) access and storage load balancing: counting and related overhead chores should be distributed fairly to the nodes of the network; (iv) accuracy: tunable, robust (in the presence of dynamics and failures) and highly accurate cardinality estimation; (v) simplicity and ease of integration: special, solution-specific indexing structures should be avoided. In this paper, first we contribute a highly-distributed, scalable, efficient, and accurate (multi-) set cardinality estimator. Subsequently, we show how to use our solution to build and maintain histograms, which have been a basic building block for query optimization for centralized databases, facilitating their porting into the realm of internet-scale data networks.

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Sprache(n): eng - English
 Datum: 2007-02-132006
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: Los Alamitos, USA : IEEE
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 Identifikatoren: eDoc: 314378
Anderer: Local-ID: C1256DBF005F876D-D37106F9C337BCC3C12571550046667A-NtarmosTW06
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Titel: Untitled Event
Veranstaltungsort: Atlanta, GA, USA
Start-/Enddatum: 2006-04-03

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Titel: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006)
Genre der Quelle: Konferenzband
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Ort, Verlag, Ausgabe: Los Alamitos, USA : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1 - 10 Identifikator: ISBN: 0-7695-2570-9