Sliding-window top-k queries on uncertain streams

Query processing on uncertain data streams has attracted a lot of attentions lately, due to the imprecise nature in the data generated from a variety of streaming applications, such as readings from a sensor network. However, all of the existing works on uncertain data streams study unbounded streams. This paper takes the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on arguably one of the most important types of queries--top-k queries. The challenge of answering sliding-window top-k queries on uncertain data streams stems from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, combined with the difficulty of coping with the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top-k queries on uncertain streams. We show that all the...

• Type: Journal
• Year: 2008
• Where: PVLDB
• Authors: Cheqing Jin, Ke Yi, Lei Chen 0002, Jeffrey Xu Yu, Xuemin Lin
• Keywords: PVLDB 2008, Sliding-window Top-k Queries, Top-k Queries, Uncertain Data Streams