Uncertain Queries Processing in Probabilistic Framework
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| Title | Uncertain Queries Processing in Probabilistic Framework |
| Authors | |
| Abstract | Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional Random Fields (CRFs)-in the setting of Probabilistic Databases that treat statistical models as first-class data objects. We investigate the problem of ranking the answers to Probabilistic Databases query. We present efficient algorithm for finding the best approximating parameters in such a framework to efficiently retrieve the top-k ranked results. An empirical study using real data sets demonstrates the effectiveness of probabilistic top-k queries and the efficiency of our method. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2010-11-01 |
| Source | Journal of Computers Vol 5, No 11 (2010) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |