By Topic

Representing and querying uncertain data with complex correlations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Guiping Xu ; Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Yu Liu ; Guowei Zeng

Many applications such as sensor networks, RFID, scientific experimental measurements, stock market prediction, information extraction, etc., need to manage uncertain data and process complex correlations among uncertain data. In probabilistic database systems, uncertain data are represented through attaching probability value to tuples, maybe attributes. Some probabilistic data models assume that tuples are independent of each other and cannot express data correlations effectively. Although others based on probabilistic graph model can capture the representation of uncertainty and complex correlations, the scalability of query and probabilistic inference cannot satisfy the needs of the applications well. In this paper, a novel probabilistic data model RTx-PDM is proposed. RTx-PDM can not only handle arbitrary uncertain data natively at the attribute or tuple level but also represent the correlations among uncertain data with the intuitive BLOCK structure. Especially, RTx-PDM can effectively express shared and schema-level correlations in a compact way through using BLOCK. Traditional relation operators are extended to support manipulating BLOCKs and representing correlations in the operation results. Experimental results validate our approach and demonstrate the effectiveness of exploiting data correlations during query processing.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on  (Volume:2 )

Date of Conference:

26-28 July 2011