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

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Algebraic Query Optimization for Distributed Top-k Queries

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

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

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

Michel,  Sebastian
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

Neumann, T., & Michel, S. (2007). Algebraic Query Optimization for Distributed Top-k Queries. In A. Kemper, H. Schöning, T. Rose, M. Jarke, T. Seidl, C. Quix, et al. (Eds.), Datenbanksysteme in Business, Technologie und Web (BTW): 12. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (pp. 324-343). Bonn, Germany: Gesellschaft für Informatik.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1DFE-D
Zusammenfassung
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.