Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations

  • Authors:

  • Ke Yi;Feifei Li;George Kollios;Divesh Srivastava

  • Affiliations:

  • Hongkong University of Science and Technology, Hong Kong;Florida State University, Tallahassee;Boston University, Boston;AT&T Labs-Research, Florham Park

  • Venue:
  • IEEE Transactions on Knowledge and Data Engineering
  • Year:
  • 2008

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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.