de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Optimizing Distributed Top-k Queries

MPS-Authors
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/persons44113

Bender,  Matthias
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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45636

Triantafillou,  Peter
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Neumann, T., Bender, M., Michel, S., Schenkel, R., Triantafillou, P., & Weikum, G. (2008). Optimizing Distributed Top-k Queries. In J. Bailey, D. Maier, K.-D. Schewe, B. Thalheim, & X. S. Wang (Eds.), Web Information Systems Engineering – WISE 2008: 9th International Conference (pp. 337-349). Berlin: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1C8C-8
Abstract
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments that can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, and 2) computing data-adaptive scan depths for different input sources. The paper presents comprehensive experiments with two different real-life datasets, using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.