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Issue No.12 - December (2008 vol.20)
pp: 1669-1682
Ke Yi , Hongkong University of Science and Technology, Hong Kong
Feifei Li , Florida State University, Tallahassee
George Kollios , Boston University, Boston
Divesh Srivastava , AT&T Labs-Research, Florham Park
ABSTRACT
This work introduces new algorithms for processing top-$k$ queries in uncertain databases, under the generally adopted model of x-relations. An x-relation consists of a number of x-tuples, and each x-tuple randomly instantiates into one tuple from one or more alternatives. Soliman et al.~\cite{soliman07} first introduced the problem of top-$k$ query processing in uncertain databases and proposed various algorithms to answer such queries. Under the x-relation model, our new results significantly improve the state of the art, in terms of both running time and memory usage. In the single-alternative case, our new algorithms are 2 to 3 orders of magnitude faster than the previous algorithms. In the multi-alternative case, the improvement is even more dramatic: while the previous algorithms have exponential complexity in both time and space, our algorithms run in near linear or low polynomial time. Our study covers both types of top-$k$ queries proposed in \cite{soliman07}. We provide both the theoretical analysis and an extensive experimental evaluation to demonstrate the superiority of the new approaches over existing solutions.
INDEX TERMS
Database Management, Information Technology and Systems, Database design, modeling and management, Query design and implementation languages, Analysis of Algorithms and Problem Complexity, Theory of Computation, Uncertain Database, Top-k Query, x-Relation Model
CITATION
Ke Yi, Feifei Li, George Kollios, Divesh Srivastava, "Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 12, pp. 1669-1682, December 2008, doi:10.1109/TKDE.2008.90
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