poster

Spatiotemporal sampling for trajectory streams

Authors:

Mohamed K. El Mahrsi,

Christine Potier,

Georges Hébrail,

Fabrice RossiAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 1627 - 1628

https://doi.org/10.1145/1774088.1774438

Published: 22 March 2010 Publication History

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Abstract

In this paper, we present a summarization technique adapted to trajectory streams. The Spatiotemporal Stream Sampling (STSS) algorithm is a single-pass sampling technique that takes advantage of both the spatial and temporal dimensions inherent to these streams in order to reduce their processing and storage costs.

References

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L. Golab and M. T. Özsu. Issues in data stream management. SIGMOD Record, 32(2):5--14, 2003.

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[2]

N. Meratnia and R. A. de By. Spatiotemporal compression techniques for moving point objects. In 9th Int. Conf. on Extending Database Technology (EDBT'04), pages 765--782, 2004.

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[3]

M. Potamias, K. Patroumpas, and T. Sellis. Sampling trajectory streams with spatiotemporal criteria. In Proc. of 18th Int. Conf. on Scientific and Statistical Database Management (SSDBM'06), pages 275--284, 2006.

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Nibali AHe Z(2015)TrajicIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.243693227:11(3138-3151)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1109/TKDE.2015.2436932
Oh JYu HChen XLebanon GWang HZaki M(2012)iSamplingProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398494(1667-1671)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398494
Kolesnikov A(2011)Efficient Online Algorithms for the Polygonal Approximation of Trajectory DataProceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 0110.1109/MDM.2011.53(49-57)Online publication date: 6-Jun-2011
https://dl.acm.org/doi/10.1109/MDM.2011.53

Index Terms

Spatiotemporal sampling for trajectory streams
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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SAC'10

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SIGAPP

SAC'10: The 2010 ACM Symposium on Applied Computing

March 22 - 26, 2010

Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Nibali AHe Z(2015)TrajicIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.243693227:11(3138-3151)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1109/TKDE.2015.2436932
Oh JYu HChen XLebanon GWang HZaki M(2012)iSamplingProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398494(1667-1671)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398494
Kolesnikov A(2011)Efficient Online Algorithms for the Polygonal Approximation of Trajectory DataProceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 0110.1109/MDM.2011.53(49-57)Online publication date: 6-Jun-2011
https://dl.acm.org/doi/10.1109/MDM.2011.53

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Sub-trajectory- and Trajectory-Neighbor-based Outlier Detection over Trajectory Streams

Research Issues of Outlier Detection in Trajectory Streams Using GPUs

Unsupervised trajectory sampling