research-article

Swarm: mining relaxed temporal moving object clusters

Editors: Elisa Bertino, Paolo Atzeni, Kian Lee Tan, Yi Chen, Y. C. Tay Authors:

Roland KaysAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 3, Issue 1-2

Pages 723 - 734

https://doi.org/10.14778/1920841.1920934

Published: 01 September 2010 Publication History

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 the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. 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-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.

References

[1]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, 1994.

Digital Library

[2]

G. Al-Naymat, S. Chawla, and J. Gudmundsson. Dimensionality reduction for long duration and complex spatio-temporal queries. In SAC, 2007.

Digital Library

[3]

I. Assent, R. Krieger, E. Müller, and T. Seidl. Inscy: Indexing subspace clusters with in-process-removal of redundancy. In ICDM, 2008.

Digital Library

[4]

M. Benkert, J. Gudmundsson, F. Hubner, and T. Wolle. Reporting flock patterns. In COMGEO, 2008.

Digital Library

[5]

L. Chen and R. T. Ng. On the marriage of lp-norms and edit distance. In VLDB, 2004.

Digital Library

[6]

L. Chen, M. T. Özsu, and V. Oria. Robust and fast similarity search for moving object trajectories. In SIGMOD, 2005.

Digital Library

[7]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In KDD, 1996.

[8]

S. Gaffney, A. Robertson, P. Smyth, S. Camargo, and M. Ghil. Probabilistic clustering of extratropical cyclones using regression mixture models. In Technical Report UCI-ICS 06-02, 2006.

[9]

J. Gudmundsson and M. van Kreveld. Computing longest duration flocks in trajectory data. In GIS, 2006.

Digital Library

[10]

J. Gudmundsson, M. J. van Kreveld, and B. Speckmann. Efficient detection of motion patterns in spatio-temporal data sets. In GIS, 2004.

Digital Library

[11]

J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. DMKD, 2004.

Digital Library

[12]

H. Jeung, H. T. Shen, and X. Zhou. Convoy queries in spatio-temporal databases. In ICDE, 2008.

Digital Library

[13]

H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen. Discovery of convoys in trajectory databases. In PVLDB, 2008.

Digital Library

[14]

P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In SSTD, 2005.

Digital Library

[15]

P. Kröger, H.-P. Kriegel, and K. Kailing. Density-connected subspace clustering for high-dimensional data. In SDM, 2004.

[16]

P. Laube and S. Imfeld. Analyzing relative motion within groups of trackable moving point objects. In GIS, 2002.

Digital Library

[17]

J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: A partition-and-group framework. In SIGMOD, 2007.

Digital Library

[18]

Z. Li, M. Ji, J.-G. Lee, L. Tang, Y. Yu, J. Han, and R. Kays. Movemine: Mining moving object databases, to appear. In SIGMOD, 2010.

Digital Library

[19]

J. Pei, J. Han, and R. Mao. Closet: An efficient algorithm for mining frequent closed itemsets. In DMKD, 2000.

[20]

M. Vlachos, D. Gunopulos, and G. Kollios. Discovering similar multidimensional trajectories. In ICDE, 2002.

Digital Library

[21]

J. Wang, J. Han, and J. Pei. Closet+: searching for the best strategies for mining frequent closed itemsets. In KDD, 2003.

Digital Library

[22]

Y. Wang, E.-P. Lim, and S.-Y. Hwang. Efficient mining of group patterns from user movement data. In DKE, 2006.

Digital Library

[23]

X. Yan, J. Han, and R. Afshar. CloSpan: Mining closed sequential patterns in large datasets. In SDM, 2003.

[24]

B.-K. Yi, H. V. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. In ICDE, 1998.

Digital Library

[25]

M. J. Zaki and C. J. Hsiao. Charm: An efficient algorithm for closed itemset mining. In SDM, 2002.

Cited By

Francia MGallinucci EGolfarelli M(2024)Colossal Trajectory MiningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122055238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122055
Tritsarolis AChondrodima ETampakis PPikrakis ATheodoridis Y(2024)Predicting Co-movement patterns in mobility dataGeoinformatica10.1007/s10707-022-00478-x28:2(221-243)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10707-022-00478-x
Kim WMémoli F(2024)Extracting Persistent Clusters in Dynamic Data via Möbius InversionDiscrete & Computational Geometry10.1007/s00454-023-00590-171:4(1276-1342)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00454-023-00590-1
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Swarm: mining relaxed temporal moving object clusters
1. Information systems
  1. Information systems applications

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 3, Issue 1-2

September 2010

1658 pages

ISSN:2150-8097

Editors:
Elisa Bertino,
Paolo Atzeni,
Kian Lee Tan,
Yi Chen,
Y. C. Tay

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Publisher

VLDB Endowment

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Published: 01 September 2010

Published in PVLDB Volume 3, Issue 1-2

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Cited By

Francia MGallinucci EGolfarelli M(2024)Colossal Trajectory MiningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122055238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122055
Tritsarolis AChondrodima ETampakis PPikrakis ATheodoridis Y(2024)Predicting Co-movement patterns in mobility dataGeoinformatica10.1007/s10707-022-00478-x28:2(221-243)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10707-022-00478-x
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https://dl.acm.org/doi/10.1007/s00454-023-00590-1
Zhao WYang PWang LChen H(2024)Meeting Pattern Detection from Trajectories in Road NetworkWeb and Big Data10.1007/978-981-97-7235-3_27(405-420)Online publication date: 31-Aug-2024
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Uddin RMahin MRajan PRavishankar CTsotras V(2023)Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory DataACM Transactions on Spatial Algorithms and Systems10.1145/35438509:2(1-35)Online publication date: 12-Apr-2023
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Gao YFang ZXu JGong SShen CChen L(2023)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331231936:5(1857-1873)Online publication date: 5-Sep-2023
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