Department of Computer Science
k-selection query over uncertain data
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. © Springer-Verlag Berlin Heidelberg 2010.
Source Publication Title
Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I
Liu, Xingjie, Mao Ye, Jianliang Xu, Yuan Tian, and Wang-Chien Lee. "k-selection query over uncertain data." Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I (2010): 444-459.