Title
Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index
Abstract
Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach (parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques.
Year
DOI
Venue
2014
10.1007/s10115-013-0725-8
Knowledge and Information Systems
Keywords
DocType
Volume
data streams,grid index,parallel queries,probabilistic skyline,skyline queries,uncertain data
Journal
41
Issue
ISSN
Citations 
2
0219-3116
10
PageRank 
References 
Authors
0.54
52
4
Name
Order
Citations
PageRank
Xiaoyong Li116413.24
Yijie Wang223942.22
Xiaoling Li3808.02
Yuan Wang4634.31