Title
Cost-Based Predictive Spatiotemporal Join
Abstract
A predictive spatiotemporal join finds all pairs of moving objects satisfying a join condition on future time and space. In this paper, we present CoPST, the first and foremost algorithm for such a join using two spatiotemporal indexes. In a predictive spatiotemporal join, the bounding boxes of the outer index are used to perform window searches on the inner index, and these bounding boxes enclose objects with increasing laxity over time. CoPST constructs globally tightened bounding boxes "on the fly" to perform window searches during join processing, thus significantly minimizing overlap and improving the join performance. CoPST adapts gracefully to large-scale databases, by dynamically switching between main-memory buffering and disk-based buffering, through a novel probabilistic cost model. Our extensive experiments validate the cost model and show its accuracy for realistic data sets. We also showcase the superiority of CoPST over algorithms adapted from state-of-the-art spatial join algorithms, by a speedup of up to an order of magnitude.
Year
DOI
Venue
2009
10.1109/TKDE.2008.159
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
inner index,spatiotemporal index,temporal databases,visual databases,spatial databases,novel probabilistic cost model,disk-based buffering,spatial join algorithms,boxes enclose object,probabilistic cost model,cost model,temporal databases.,cost-based predictive spatiotemporal join,moving objects,join processing,predictive spatiotemporal,future time,spatiotemporal indexes,window search,main-memory buffering,prediction algorithms,environmental management,satisfiability,telematics,database systems,temporal database,indexing terms,spatial database,indexation,air traffic control
Data mining,Data set,Air traffic control,Computer science,On the fly,Temporal database,Probabilistic logic,Telematics,Speedup,Bounding overwatch
Journal
Volume
Issue
ISSN
21
2
1041-4347
Citations 
PageRank 
References 
3
0.42
23
Authors
6
Name
Order
Citations
PageRank
Wook-Shin Han180557.85
Jaehwa Kim282.81
Byung Suk Lee329368.57
Yufei Tao47231316.71
Ralf Rantzau518815.97
Volker Markl62245182.37