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Issue No.12 - December (2011 vol.23)

pp: 1903-1917

Jeffrey Jestes , Florida State University, Tallahassee

Graham Cormode , AT&T Labs-Research, Florham Park

Feifei Li , Florida State University, Tallahassee

Ke Yi , Hong Kong University of Science and Technology, Hong Kong

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.192

ABSTRACT

Recently, there have been several attempts to propose definitions and algorithms for ranking queries on probabilistic data. However, these lack many intuitive properties of a top-k over deterministic data. We define several fundamental properties, including exact-k, containment, unique rank, value invariance, and stability, which are satisfied by ranking queries on certain data. We argue that these properties should also be carefully studied in defining ranking queries in probabilistic data, and fulfilled by definition for ranking uncertain data for most applications. We propose an intuitive new ranking definition based on the observation that the ranks of a tuple across all possible worlds represent a well-founded rank distribution. We studied the ranking definitions based on the expectation, the median, and other statistics of this rank distribution for a tuple and derived the expected rank, median rank, and quantile rank correspondingly. We are able to prove that the expected rank, median rank, and quantile rank satisfy all these properties for a ranking query. We provide efficient solutions to compute such rankings across the major models of uncertain data, such as attribute-level and tuple-level uncertainty. Finally, a comprehensive experimental study confirms the effectiveness of our approach.

INDEX TERMS

Probabilistic data, ranking queries, top-k queries, uncertain database.

CITATION

Jeffrey Jestes, Graham Cormode, Feifei Li, Ke Yi, "Semantics of Ranking Queries for Probabilistic Data",

*IEEE Transactions on Knowledge & Data Engineering*, vol.23, no. 12, pp. 1903-1917, December 2011, doi:10.1109/TKDE.2010.192REFERENCES