Efficiently Answering Probabilistic Threshold Top-k Queries on Uncertain Data

on Uncertain Data (Extended Abstract) Ming Hua Jian Pei Wenjie Zhang Xuemin Lin Simon Fraser University, Canada The University of New South Wales & NICTA {mhua, jpei}@cs.sfu.ca {zhangw, lxue}@cse.unsw.edu.au Abstract-- In this paper, we propose a novel type of probabilistic threshold top-k queries on uncertain data, and give an exact algorithm. More details can be found in [4]. I. PROBABILISTIC THRESHOLD TOP-k QUERIES We consider uncertain data in the possible worlds semantics model [1], [5], [7], which is also adopted by some recent studies on uncertain data processing, such as [8], [2], [6]. Generally, an uncertain table T contains a set of (uncertain) tuples, where each tuple t T is associated with a membership probability value Pr(t) > 0. When there is no confusion, we also call an uncertain table simply a table. A generation rule on a table T specifies a set of exclusive tuples in the form of R : tr1 ? ? ? trm where tri T (1 i m) and m

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Ming Hua, Jian Pei, Wenjie Zhang, Xuemin Lin
• Keywords: Database, ICDE 2008, Uncertain Data, Uncertain Data Processing, Uncertain Table