Swarm: Mining relaxed temporal moving object clusters

Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays

Research output: Contribution to journalArticle

244 Scopus citations

Abstract

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Motivated by this, we propose the concept of swarm which captures themoving objects that move within arbitrary shape of clusters for certain timestamps that are possibly nonconsecutive. The goal of our paper is to find all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-thefly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.

Original languageEnglish (US)
Pages (from-to)723-734
Number of pages12
JournalProceedings of the VLDB Endowment
Volume3
Issue number1
DOIs
StatePublished - Sep 2010

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this