Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

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

Published: 15 July 2011 Publication History

Abstract

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.

References

[1]
Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94). Morgan Kaufmann Publishers Inc., San Francisco, CA, 487--499.
[2]
Al-Naymat, G., Chawla, S., and Gudmundsson, J. 2007. Dimensionality reduction for long duration and complex spatio-temporal queries. In Proceedings of the ACM Symposium on Applied Computing (SAC'07). ACM, New York, NY, 393--397.
[3]
Bar-David, I., Cross, P. C., Ryan, S. J., and Getz, W. M. 2009. Methods for assessing movement path recursion with application to african buffalo in south africa. Ecology. 90.
[4]
Benkert, M., Gudmundsson, J., Hübner, F., and Wolle, T. 2008. Reporting flock patterns. Comput. Geom. Theory Appl. 41, 3, 111--125.
[5]
Chen, L., Özsu, M. T., and Oria, V. 2005. Robust and fast similarity search for moving object trajectories. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'05). ACM, New York, NY, 491--502.
[6]
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases. In Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'96).
[7]
Gaffney, S., Robertson, A., Smyth, P., Camargo, S., and Ghil, M. 2006. Probabilistic clustering of extratropical cyclones using regression mixture models. Tech. rep. ICS 06-02. University of California, Irvino.
[8]
Giannotti, F., Nanni, M., Pinelli, F., and Pedreschi, D. 2007. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07). ACM, New York, NY, 330--339.
[9]
Gudmundsson, J., Laube, P., and Wolle, T. 2008. Movement patterns in spatio-temporal data. In Encyclopedia of GIS. 726--732.
[10]
Gudmundsson, J. and van Kreveld, M. 2006. Computing longest duration flocks in trajectory data. In Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems (GIS'06). ACM, New York, 35--42.
[11]
Gudmundsson, J., van Kreveld, M., and Speckmann, B. 2004. Efficient detection of motion patterns in spatio-temporal data sets. In Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems (GIS'04). ACM, New York, NY, 250--257.
[12]
Han, J., Pei, J., Yin, Y., and Mao, R. 2004. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8, 1, 53--87.
[13]
Jeung, H., Liu, Q., Shen, H. T., and Zhou, X. 2008a. A hybrid prediction model for moving objects. In Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE'08). IEEE Computer Society, Los Alamitos, CA, 70--79.
[14]
Jeung, H., Shen, H. T., and Zhou, X. 2008b. Convoy queries in spatio-temporal databases. In Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE'08). IEEE Computer Society, Los Alamitos, CA, 1457--1459.
[15]
Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., and Shen, H. T. 2008c. Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1, 1, 1068--1080.
[16]
Kalnis, P., Mamoulis, N., and Bakiras, S. 2005. On discovering moving clusters in spatio-temporal data. In Proceedings of 9th International Symposium on Spatial and Temporal Databases (SSTD'05). 364--381.
[17]
Laube, P. and Imfeld, S. 2002. Analyzing relative motion within groups of trackable moving point objects. In Proceedings of the 2nd International Conference on Geographic Information Science (GIScience'02). Springer-Verlag, Berlin, 132--144.
[18]
Lee, J.-G., Han, J., and Whang, K.-Y. 2007. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'07). ACM, New York, NY, 593--604.
[19]
Li, Z., Ding, B., Han, J., and Kays, R. 2010c. Swarm: Mining relaxed temporal moving object clusters. In Proceedings of the Internation Conference on Very Large Data Bases (VLDB'10).
[20]
Li, Z., Ding, B., Han, J., Kays, R., and Nye, P. 2010b. Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10). ACM, New York, NY, 1099--1108.
[21]
Li, Z., Ji, M., Lee, J.-G., Tang, L.-A., Yu, Y., Han, J., and Kays, R. 2010a. Movemine: Mining moving object databases. In Proceedings of the International Conference on Management of Data (SIGMOD'10). ACM, New York, NY, 1203--1206.
[22]
Liao, L., Fox, D., and Kautz, H. 2005. Location-based activity recognition using relational markov networks. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05). Morgan Kaufmann Publishers Inc., San Francisco, 773--778.
[23]
Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., and Cheung, D. W. 2004. Mining, indexing, and querying historical spatiotemporal data. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04). ACM, New York, NY, 236--245.
[24]
Nanni, M., Trasarti, R., Renso, C., Giannotti, F., and Pedreschi, D. 2010. Advanced knowledge discovery on movement data with the geopkdd system. In Proceedings of the 13th International Conference on Extending Database Technology (EDBT'10). ACM, New York, NY, 693--696.
[25]
Pei, J., Han, J., and Mao, R. 2000. Closet: An efficient algorithm for mining frequent closed itemsets. In Proceedings of the ACM-SIGMOD International Workshop Data Mining and Knowledge Discovery (DMKDÕ00). 11--20.
[26]
Stolorz, P., Nakamura, H., Mesrobian, E., Muntz, R. R., Santos, J. R., Yi, J., and Ng, K. 1995. Fast spatio-temporal data mining of large geophysical datasets. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining. AAAI Press, 300--305.
[27]
Vlachos, M., Gunopoulos, D., and Kollios, G. 2002. Discovering similar multidimensional trajectories. In Proceedings of the 18th International Conference on Data Engineering (ICDE'02). IEEE Computer Society, Los Alamitos, CA, 673.
[28]
Vlachos, M., Yu, P. S., and Castelli, V. 2005. On periodicity detection and structural periodic similarity. In Proceedings of the SIAM International Conference on Data Mining (SDM'05).
[29]
Wang, C. and Parthasarathy, S. 2006. Summarizing itemset patterns using probabilistic models. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). ACM, New York, NY, 730--735.
[30]
Wang, J., Han, J., and Pei, J. 2003. Closet+: Searching for the best strategies for mining frequent closed itemsets. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03). ACM, New York, NY, 236--245.
[31]
Wang, Y., Lim, E.-P., and Hwang, S.-Y. 2006. Efficient mining of group patterns from user movement data. Data Knowl. Engin. 57, 3, 240--282.
[32]
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology. 70.
[33]
Yan, X., Cheng, H., Han, J., and Xin, D. 2005. Summarizing itemset patterns: a profile-based approach. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD'05). ACM, New York, NY, 314--323.
[34]
Yan, X., Han, J., and Afshar, R. 2003. CloSpan: Mining closed sequential patterns in large datasets. In Proceedings of the SIAM International Conference on Data Mining (SDM'03). 166--177.
[35]
Zaki, M. J. and Hsiao, C. J. 2002. Charm: An efficient algorithm for closed itemset mining. In Proceedings of the SIAM International Conference on Data Mining (SDM'02).
[36]
Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q. 2010. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). ACM, New York, 1029--1038.

Cited By

View all
  • (2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 13-Apr-2024
  • (2024)MoReVis: A Visual Summary for Spatiotemporal Moving RegionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325016630:4(1927-1941)Online publication date: Apr-2024
  • (2024)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3323535(1-21)Online publication date: 2024
  • Show More Cited By

Index Terms

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

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 2, Issue 4
    July 2011
    272 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/1989734
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 July 2011
    Accepted: 01 August 2010
    Revised: 01 August 2010
    Received: 01 May 2010
    Published in TIST Volume 2, Issue 4

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Moving objects
    2. computational sustainability
    3. pattern mining
    4. periodic behavior
    5. swarm pattern

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Funding Sources

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)60
    • Downloads (Last 6 weeks)7
    Reflects downloads up to 30 Aug 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 13-Apr-2024
    • (2024)MoReVis: A Visual Summary for Spatiotemporal Moving RegionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325016630:4(1927-1941)Online publication date: Apr-2024
    • (2024)Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3323535(1-21)Online publication date: 2024
    • (2024)Data Mining of Fertility Intention based on LSTM Neural Network2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)10.1109/IMCEC59810.2024.10575535(131-135)Online publication date: 24-May-2024
    • (2024)Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00334(4383-4395)Online publication date: 13-May-2024
    • (2023)A composite trend representation-based tracking system with historical portfolio data for portfolio optimizationJournal of Computational Methods in Sciences and Engineering10.3233/JCM-22663823:2(1021-1042)Online publication date: 1-Jan-2023
    • (2023)Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock MarketsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587298(1-4)Online publication date: 30-Apr-2023
    • (2023)Doubly elastic net regularized online portfolio optimization with transaction costsScientific Reports10.1038/s41598-023-46059-213:1Online publication date: 2-Nov-2023
    • (2023)On robustness against evacuees’ unexpected movement in automatic evacuation guidingComputers and Electrical Engineering10.1016/j.compeleceng.2022.108531105:COnline publication date: 1-Jan-2023
    • (2022)Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 PandemicSystems10.3390/systems1005014610:5(146)Online publication date: 8-Sep-2022
    • Show More Cited By

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media