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MoveMine: mining moving object databases

Published: 06 June 2010 Publication History

Abstract

With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.

References

[1]
H. Cao, N. Mamoulis, and D. W. Cheung. Discovery of periodic patterns in spatiotemporal sequences. In TKDE'07.
[2]
J. Gudmundsson, P. Laube, and T. Wolle. Movement patterns in spatio-temporal data. In Encyclopedia of GIS 2008.
[3]
J. Gudmundsson and M. van Kreveld. Computing longest duration flocks in trajectory data. In GIS'06.
[4]
H. Jeung, H. T. Shen, and X. Zhou. Convoy queries in spatio-temporal databases. In ICDE'08.
[5]
J.-G. Lee, J. Han, and X. Li. Trajectory outlier detection: A partition-and-detect framework. In ICDE'08.
[6]
J.-G. Lee, J. Han, X. Li, and H. Gonzalez. Traclass: Trajectory classification using hierarchical region-based and trajectory-based clustering. In VLDB'08.
[7]
J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: A partition-and-group framework. In SIGMOD'07.
[8]
Z. Li, B. Ding, J. Han, and R. Kays. Mining hidden periodic behaviors for moving objects. In Submission.
[9]
Z. Li, B. Ding, J. Han, and R. Kays. Swarm: Mining relaxed temporal moving object clusters. In Submission.

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    cover image ACM Conferences
    SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
    June 2010
    1286 pages
    ISBN:9781450300322
    DOI:10.1145/1807167
    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]

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    New York, NY, United States

    Publication History

    Published: 06 June 2010

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    Author Tags

    1. moving objects
    2. pattern/trajectory mining
    3. visualization

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    SIGMOD/PODS '10
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    SIGMOD/PODS '10: International Conference on Management of Data
    June 6 - 10, 2010
    Indiana, Indianapolis, USA

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    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    • (2022)Understanding Mobility via Deep Multi-Scale LearningProcedia Computer Science10.1016/j.procs.2019.01.251147:C(487-494)Online publication date: 15-Apr-2022
    • (2022) MinUS: Mining User Similarity with Trajectory PatternsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44845-8_29(436-439)Online publication date: 10-Mar-2022
    • (2020)Scalable Detection of Crowd Motion PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287907932:1(152-164)Online publication date: 1-Jan-2020
    • (2020)Detecting Representative Trajectories in Moving Objects Databases from ClustersInformation Technology and Systems10.1007/978-3-030-40690-5_14(141-151)Online publication date: 31-Jan-2020
    • (2019)A Survey of Parallel Indexing Techniques for Large-Scale Moving Object DatabasesUtilizing Big Data Paradigms for Business Intelligence10.4018/978-1-5225-4963-5.ch003(72-105)Online publication date: 2019
    • (2019)A System of Mining Semantic Trajectory Patterns from GPS Data of Real UsersSymmetry10.3390/sym1107088911:7(889)Online publication date: 8-Jul-2019
    • (2019)Context-based Markov Model toward Spatio-Temporal Prediction with Realistic DatasetProceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility10.1145/3356995.3364534(24-32)Online publication date: 5-Nov-2019
    • (2019)Top-k Queries over Digital TracesProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319857(954-971)Online publication date: 25-Jun-2019
    • (2019)Towards a Smart(er) Social Science Using High-Dimensional Continuous-Time Trajectories from the Open Dynamic Interaction Networks (ODIN) Platform2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00051(37-44)Online publication date: Aug-2019
    • (2019)MOASM: Anomalous Sub-trajectory Monitoring Of Moving Objects Over Trajectory Streams2019 5th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA.2019.8802841(35-40)Online publication date: Jul-2019
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