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Probabilistic information retrieval approach for ranking of database query results

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44283

Das,  Gautam
Algorithms and Complexity, 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;

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Zitation

Chaudhuri, S., Das, G., Hristidis, V., & Weikum, G. (2006). Probabilistic information retrieval approach for ranking of database query results. ACM Transactions on Database Systems, 31, 1134-1168.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-23BF-A
Zusammenfassung
We investigate the problem of ranking the answers to a database query when many tuples are returned. In particular, we present methodologies to tackle the problem for conjunctive and range queries, by adapting and applying principles of probabilistic models from information retrieval for structured data. Our solution is domain independent and leverages data and workload statistics and correlations. We evaluate the quality of our approach with a user survey on a real database. Furthermore, we present and experimentally evaluate algorithms to efficiently retrieve the top ranked results, which demonstrate the feasibility of our ranking system.