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 explo...

• Type: Conference
• Year: 2010
• Where: DASFAA
• Authors: Xingjie Liu, Mao Ye, Jianliang Xu, Yuan Tian, Wang-Chien Lee
• Keywords: DASFAA 2010, Database, K-selection Queries, K-selection Query, Optimal Selection