By Topic

Optimizing Multi-Top-k Queries over Uncertain Data Streams

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Tao Chen ; State Key Lab. of Proteomics, Beijing Proteome Res. Center, Beijing, China ; Lei Chen ; Ozsu, M.T. ; Nong Xiao

Query processing over uncertain data streams, in particular top-κ query processing, has become increasingly important due to its wide application in many fields such as sensor network monitoring and internet traffic control. In many real applications, multiple top-κ queries are registered in the system. Sharing the results of these queries is a key factor in saving the computation cost and providing real-time response. However, due to the complex semantics of uncertain top-κ query processing, it is nontrivial to implement sharing among different top-κ queries and few works have addressed the sharing issue. In this paper, we formulate various types of sharing among multiple top-κ queries over uncertain data streams based on the frequency upper bound of each top-κ query. We present an optimal dynamic programming solution as well as a more efficient (in terms of time and space complexity) greedy algorithm to compute the execution plan of executing queries for saving the computation cost between them. Experiments have demonstrated that the greedy algorithm can find the optimal solution in most cases, and it can almost achieve the same performance (in terms of latency and throughput) as the dynamic programming approach.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:25 ,  Issue: 8 )