MoveMine 2.0: Mining object relationships from movement data

Fei Wu, Tobias Kin Hou Lei, Zhenhui Li, Jiawei Han

Research output: Contribution to journalConference articlepeer-review

25 Scopus citations


The development in positioning technology has enabled us to collect a huge amount of movement data from moving objects, such as human, animals, and vehicles. The data embed rich information about the relationships among moving objects and have applications in many fields, e.g., in ecological study and human behavioral study. Previously, we have proposed a system MoveMine that integrates several start-of-art movement mining methods. However, it does not include recent methods on relationship pattern mining. Thus, we propose to extend MoveMine to MoveMine 2.0 by adding substantial new methods in mining dynamic relationship patterns. Newly added methods focus on two types of pairwise relationship patterns: (i) attraction/avoidance relationship, and (ii) following pattern. A user-friendly interface is designed to support interactive exploration of the result and provides exibility in tuning parameters. MoveMine 2.0 is tested on multiple types of real datasets to ensure its practical use. Our system provides useful tools for domain experts to gain insights on real dataset. Meanwhile, it will promote further research in relationship mining from moving objects.

Original languageEnglish (US)
Pages (from-to)1613-1616
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number13
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: Sep 1 2014Sep 5 2014

All Science Journal Classification (ASJC) codes

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


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