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Improving Collection Selection with Overlap-Awareness

MPG-Autoren
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/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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45808

Zimmer,  Christian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Bender, M., Michel, S., Triantafillou, P., Weikum, G., & Zimmer, C. (2005). Improving Collection Selection with Overlap-Awareness. In SIGIR 2005: Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '05) (pp. 67-74). New York, USA: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-26C3-4
Zusammenfassung
Collection selection has been a research issue for years. Most of the existing literature estimates the expected result quality of a collection, typically using precomputed statistics, and ranks the collections accordingly. We believe that this is insufficient if the collections overlap, e.g., in the scenario of autonomous peers crawling the web. We argue for the extension of existing quality measures using estimators of mutual overlap among collections and present experiments in which this combination outperforms CORI, a popular approach based on quality estimation. In our experiments, we use a prototype implementation of a P2P web search engine that allows handling large amounts of data in a distributed and self-organizing manner. Taking overlap into account during collection selection in this scenario can drastically decrease the number of collections that have to be contacted in order to reach a satisfactory level of recall, which is a great step towards the feasibility of distributed web search.