K-selection query over uncertain data

Xingjie Liu*, Mao Ye, Jianliang XU, Yuan Tian, Wang Chien Lee

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)


This paper studies a new query on uncertain data, called k-selection query. Given an uncertain dataset of N objects, where each object is associated with a preference score and a presence probability, a k-selection query returns k objects such that the expected score of the "best available" objects is maximized. This query is useful in many application domains such as entity web search and decision making. In evaluating k-selection queries, we need to compute the expected best score (EBS) for candidate k-selection sets and search for the optimal selection set with the highest EBS. Those operations are costly due to the extremely large search space. In this paper, we identify several important properties of k-selection queries, including EBS decomposition, query recursion, and EBS bounding. Based upon these properties, we first present a dynamic programming (DP) algorithm that answers the query in O(k · N) time. Further, we propose a Bounding-and-Pruning (BP) algorithm, that exploits effective search space pruning strategies to find the optimal selection without accessing all objects. We evaluate the DP and BP algorithms using both synthetic and real data. The results show that the proposed algorithms outperform the baseline approach by several orders of magnitude.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 15th International Conference, DASFAA 2010, Proceedings
Number of pages16
EditionPART 1
Publication statusPublished - 2010
Event15th International Conference on Database Systems for Advanced Applications, DASFAA 2010 - Tsukuba, Japan
Duration: 1 Apr 20104 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Database Systems for Advanced Applications, DASFAA 2010

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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