Discovering the most influential sites over uncertain data: A rank based approach

Zheng, Kai, Huang, Zi, Zhou, Aoying and Zhou, Xiaofang (2012) Discovering the most influential sites over uncertain data: A rank based approach. IEEE Transaction on Knowledge and Data Engineering, 24 12: 2156-2169. doi:10.1109/TKDE.2011.121

Author Zheng, Kai
Huang, Zi
Zhou, Aoying
Zhou, Xiaofang
Title Discovering the most influential sites over uncertain data: A rank based approach
Language of Title eng
Journal name IEEE Transaction on Knowledge and Data Engineering   Check publisher's open access policy
Language of Journal Name eng
ISSN 1041-4347
Publication date 2012-01-01
Year available 2011
Sub-type Article (original research)
DOI 10.1109/TKDE.2011.121
Open Access Status Not Open Access
Volume 24
Issue 12
Start page 2156
End page 2169
Total pages 15
Place of publication Piscataway, NJ, United States
Publisher IEEE
Language eng
Abstract With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring and biological information management systems, uncertainty-aware query processing has received a significant amount of research effort from the database community in recent years. In this paper, we investigate a new type of query in the context of uncertain databases, namely uncertain top-k influential sites query (UTkIS query for short), which can be applied in a wide range of application areas such as marketing analysis and mobile services. Since it is not so straightforward to precisely define the semantics of topk query with uncertain data, in this paper we introduce a novel and more intuitive formulation of the query on the basis of expected rank semantics. To address the efficiency issue caused by possible worlds exploration, we propose effective pruning rules and a divide-and-conquer paradigm such that the number of candidates as well as the number of possible worlds to be considered can be significantly reduced. Finally we conduct extensive experiments on real datasets to verify the effectiveness and efficiency of the new methods proposed in this paper.
Keyword "Fuzzy"
Spatial databases
Probabilistic reasoning
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 60925008
Institutional Status UQ
Additional Notes IEEE Transactions on Knowledge and Data Engineering Published online 9 June 2011.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 09 Mar 2012, 20:09:17 EST by Mr Kevin Zheng on behalf of School of Information Technol and Elec Engineering