By Topic

Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data

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

2 Author(s)
Xiaofeng Ding ; Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Hai Jin

The skyline operator has received considerable attention from the database community, due to its importance in many applications including multicriteria decision making, preference answering, and so forth. In many applications where uncertain data are inherently exist, i.e., data collected from different sources in distributed locations are usually with imprecise measurements, and thus exhibit kind of uncertainty. Taking into account the network delay and economic cost associated with sharing and communicating large amounts of distributed data over an internet, an important problem in this scenario is to retrieve the global skyline tuples from all the distributed local sites with minimum communication cost. Based on the well-known notation of the probabilistic skyline query over centralized uncertain data, in this paper, we propose the notation of distributed skyline queries over uncertain data. Furthermore, two communication- and computation-efficient algorithms are proposed to retrieve the qualified skylines from distributed local sites. Extensive experiments have been conducted to verify the efficiency, the effectiveness and the progressiveness of our algorithms with both the synthetic and real data sets.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:24 ,  Issue: 8 )