MoveMine: Mining moving object data for discovery of animal movement patterns

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining. For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

[1]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[2]  Hisashi Nakamura,et al.  Fast Spatio-Temporal Data Mining of Large Geophysical Datasets , 1995, KDD.

[3]  Jiawei Han,et al.  Mining periodic behaviors for moving objects , 2010, KDD.

[4]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[5]  Jian Pei,et al.  CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[6]  Jae-Gil Lee,et al.  Incremental Clustering for Trajectories , 2010, DASFAA.

[7]  Panos Kalnis,et al.  On Discovering Moving Clusters in Spatio-temporal Data , 2005, SSTD.

[8]  Dino Pedreschi,et al.  Advanced knowledge discovery on movement data with the GeoPKDD system , 2010, EDBT '10.

[9]  Padhraic Smyth,et al.  Probabilistic clustering of extratropical cyclones using regression mixture models , 2007 .

[10]  Wayne M Getz,et al.  Methods for assessing movement path recursion with application to African buffalo in South Africa. , 2009, Ecology.

[11]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[12]  Qing Liu,et al.  A Hybrid Prediction Model for Moving Objects , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[13]  Marianne Winslett,et al.  Differentially private data cubes: optimizing noise sources and consistency , 2011, SIGMOD '11.

[14]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[15]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[16]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[17]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[18]  B. Worton Kernel methods for estimating the utilization distribution in home-range studies , 1989 .

[19]  Jiawei Han,et al.  Swarm: Mining Relaxed Temporal Moving Object Clusters , 2010, Proc. VLDB Endow..

[20]  Jiawei Han,et al.  Summarizing itemset patterns: a profile-based approach , 2005, KDD '05.

[21]  Jae-Gil Lee,et al.  MoveMine: mining moving object databases , 2010, SIGMOD Conference.

[22]  Jiawei Han,et al.  Generalized Fisher Score for Feature Selection , 2011, UAI.

[23]  Jiawei Han,et al.  Keyword extraction for social snippets , 2010, WWW '10.

[24]  Joachim Gudmundsson,et al.  Reporting flock patterns , 2006, Comput. Geom..

[25]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[26]  George Kollios,et al.  Mining, indexing, and querying historical spatiotemporal data , 2004, KDD.

[27]  Srinivasan Parthasarathy,et al.  Summarizing itemset patterns using probabilistic models , 2006, KDD '06.

[28]  Patrick Laube,et al.  Analyzing Relative Motion within Groups of Trackable Moving Point Objects , 2002, GIScience.

[29]  Yida Wang,et al.  Efficient mining of group patterns from user movement data , 2006, Data Knowl. Eng..

[30]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[31]  Joachim Gudmundsson,et al.  Computing longest duration flocks in trajectory data , 2006, GIS '06.

[32]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[33]  Jiawei Han,et al.  Geo-Friends Recommendation in GPS-based Cyber-physical Social Network , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[34]  Bettina Speckmann,et al.  Efficient detection of motion patterns in spatio-temporal data sets , 2004, GIS '04.

[35]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[36]  Jiawei Han,et al.  Learning a Kernel for Multi-Task Clustering , 2011, AAAI.

[37]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[38]  Xifeng Yan,et al.  CloSpan: Mining Closed Sequential Patterns in Large Datasets , 2003, SDM.

[39]  Fei Wu,et al.  Mining Following Relationships in Movement Data , 2013, 2013 IEEE 13th International Conference on Data Mining.

[40]  Jiawei Han,et al.  Joint Feature Selection and Subspace Learning , 2011, IJCAI.

[41]  Joachim Gudmundsson,et al.  Dimensionality reduction for long duration and complex spatio-temporal queries , 2007, SAC '07.

[42]  Philip S. Yu,et al.  On Periodicity Detection and Structural Periodic Similarity , 2005, SDM.

[43]  Heng Tao Shen,et al.  Convoy Queries in Spatio-Temporal Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[44]  Jae-Gil Lee,et al.  Temporal Outlier Detection in Vehicle Traffic Data , 2009, 2009 IEEE 25th International Conference on Data Engineering.