de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Search for the Best but Expect the Worst - Distributed Top-k Queries over Decreasing Aggregated Scores

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45041

Michel,  Sebastian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons127842

Neumann,  Thomas
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Michel, S., & Neumann, T. (2007). Search for the Best but Expect the Worst - Distributed Top-k Queries over Decreasing Aggregated Scores. In Tenth International Workshop on the Web and Databases (WebDB 2007) (pp. 1-6). Orsay, France: INRIA Saclay.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-20A2-2
Zusammenfassung
We consider distributed top-k queries in wide-area networks where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers. In contrast to existing work, we exclusively consider distributed top-k queries over decreasing aggregated values. State-of-the-art distributed top-k algorithms usually depend on threshold propagation to reduce expensive data access across the network, but fail to compute tight thresholds if the aggregation function is decreasing. Decreasing aggregation functions, however, occur naturally, for example when considering conjunctive queries. Our proposed algorithms allow for efficient execution of these kind of queries, using a combination of threshold propagation and semijoin techniques. We demonstrate these techniques for the problem of top-k peer selection in a Peer-To-Peer Web search engine. Our experimental results on real-world data shows the superiority of our approach over pure thresholding.