Towards Distributed Convoy Pattern Mining

被引：3
|
作者：
Orakzai, Faisal [1 ]
Devogele, Thomas [2 ]
Calders, Toon [1 ]
机构：
[1] Univ Libre Bruxelles, Dept Comp & Decis Engn CoDE, B-1050 Brussels, Belgium
[2] Univ Tours, Dept Informat, F-41000 Blois, France
来源：
23RD ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2015) | 2015年
关键词：
Spatial; temporal; movement patterns; big data; data mining; distributed processing; parallel algorithms; MR-DBSCAN; DISCOVERY; ALGORITHM;
D O I：
10.1145/2820783.2820840
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Mining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however do not scale to real-life dataset sizes. Therefore a distributed algorithm for convoy mining is inevitable. In this paper, we discuss the problem of convoy mining and analyze different data partitioning strategies to pave the way for a generic distributed convoy pattern mining algorithm.
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收藏
页数：4
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