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Spatiotemporal summarization of traffic data streams

Published: 02 November 2010 Publication History

Abstract

With resource-efficient summarization and accurate reconstruction of the historic traffic sensor data, one can effectively manage and optimize transportation systems (e.g., road networks) to become smarter (better mobility, less congestion, less travel time, and less travel cost) and greener (less waste of fuel and less greenhouse gas production). The existing data summarization (and archival) techniques are generic and are not designed to leverage the unique characteristics of the traffic data for effective data reduction. In this paper, we propose and explore a family of data summaries that take advantage of the high temporal and spatial redundancy/correlation among sensor readings from individual sensors and sensor groups, respectively, for effective data reduction. In particular, with these summaries we derive and maintain a "signature" as well as a series of "outliers" for the readings received from each individual sensor or group of co-located sensors. While signatures capture the typical readings that estimate the actual readings with bounded error, the outliers represent the actual readings where the error-bound is violated. With the combination of signatures and outliers, our proposed data summaries can effectively represent the actual data with much smaller storage footprint, while allowing for efficient querying of the sensor data with bounded error. Our experiments with a real traffic sensor dataset shows that our proposed data summaries use only 23% of the storage space otherwise required for storing the actual data, while allowing for highly accurate query results with guaranteed precision.

References

[1]
D. J. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S. Zdonik. Aurora: a new model and architecture for data stream management. In VLDB Journal, August 2003.
[2]
A. Arasu, B. Babcock, S. Babu, M. Datar, K. Ito, I. Nishizawa, J. Rosenstein, and J. Widom. Stream: The stanford stream data manager. In IEEE Data engeneering Bulletin, volume 26, 2003.
[3]
H. Cao, O. Wolfson, and G. Trajcevski. Spatio-temporal data reduction with deterministic error bounds. In The VLDB Journal - The International Journal on Very Large Data Bases, volume 23, pages 211--228. Springer-Verlag New York, Inc., 2006.
[4]
K. Chakrabarti, M. Garofalakis, R. Rastogi, and K. Shim. Approximate query processing using wavelets. In Proceedings of the 26th VLDB Conference, 2000.
[5]
S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein, W. Hong, S. Krishnamurthy, S. R. Madden, F. Reiss, and M. A. Shah. Telegraphcq: continuous dataflow processing. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data, pages 668--668, San Diego, California, 2003.
[6]
Y. Datar, A. Gionis, P. Indyk, and R. Motwani. Maintaining stream statistics over sliding windows. In Proc. of the 13th Annual ACM-SIAM Symp. on Discrete Algorithms, January 2002.
[7]
P. B. Gibbons, Y. Matias, and V. Poosala. Fast incremental maintenance of approximate histograms. In Proc. of the 23rd Intl. Conf. on Very Large Data Bases (VLDB), August 1997.
[8]
S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. OŠCallaghan. Clustering data streams. In Proc. of the 2000 Annual Symp. on Foundations of Computer Science (FOCS), November 2000.
[9]
M. Jahangiri, D. Sacharidis, and C. Shahabi. Shift-split: I/o efficient maintenance of wavelet-transformed multidimensional data. In 24th ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, June 2005.
[10]
I. T. Jolliffe. Principal component analysis. In Springer-Verlag, 1986.
[11]
Y. Matias, J. S. Vitter, and M. Wang. Dynamic maintenance of wavelet-based histograms. In Proc. of the 26rd Intl. Conf. on Very Large Data Bases (VLDB), September 2000.
[12]
J. Naughton and et al. The niagara internet query system. In Proceedings of the 26th VLDB Conference, 2000.
[13]
R. R. Schmidt and C. Shahabi. Propolyne: A fast wavelet-based algorithm for progressive evaluation of polynomial range-sum queries(extended version). In VIII. Conference on Extending Database Technology, Prague, March 2002
[14]
J. S. Vitter and M. Wang. Approximate computation of multidimensional aggregates of sparse data using wavelets. In Proc. of the 1999 ACM SIGMOD Conf., pages 193--204, Philadelphia, Pennsylvania, 1999.
[15]
M. E. Wall, A. Rechtsteiner, and L. M. Rocha. Singular value decomposition and principal component analysis. 2003.

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  • (2021)kD-STR: A Method for Spatio-Temporal Data Reduction and ModellingACM/IMS Transactions on Data Science10.1145/34393342:3(1-31)Online publication date: 18-May-2021
  • (2020)Reducing and linking spatio-temporal datasets with kD-STRProceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities10.1145/3423455.3430317(10-19)Online publication date: 3-Nov-2020
  • (2020)Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-ArtsJournal of Computer Science and Technology10.1007/s11390-020-9349-035:3(665-696)Online publication date: 29-May-2020
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cover image ACM Conferences
IWGS '10: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
November 2010
67 pages
ISBN:9781450304313
DOI:10.1145/1878500
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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Published: 02 November 2010

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Author Tags

  1. query
  2. spatiotemporal data streams
  3. summarization
  4. transportation

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Cited By

View all
  • (2021)kD-STR: A Method for Spatio-Temporal Data Reduction and ModellingACM/IMS Transactions on Data Science10.1145/34393342:3(1-31)Online publication date: 18-May-2021
  • (2020)Reducing and linking spatio-temporal datasets with kD-STRProceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities10.1145/3423455.3430317(10-19)Online publication date: 3-Nov-2020
  • (2020)Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-ArtsJournal of Computer Science and Technology10.1007/s11390-020-9349-035:3(665-696)Online publication date: 29-May-2020
  • (2017)Modeling and Prediction of People's Needs (Vision Paper)Proceedings of the 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3148044.3148047(1-4)Online publication date: 7-Nov-2017
  • (2017)Spatial and Spatiotemporal Big Data ScienceSpatial Big Data Science10.1007/978-3-319-60195-3_2(15-44)Online publication date: 15-Jul-2017
  • (2015)Spatiotemporal Data Mining: A Computational PerspectiveISPRS International Journal of Geo-Information10.3390/ijgi40423064:4(2306-2338)Online publication date: 28-Oct-2015
  • (2015)Dynamic Taxi Trip Information Management using G* SystemProceedings of the 2015 International Conference on Big Data Applications and Services10.1145/2837060.2837093(193-197)Online publication date: 20-Oct-2015
  • (2013)On the Understanding of the Stream Volume Behavior on TwitterPattern Recognition - Applications and Methods10.1007/978-3-642-36530-0_14(171-180)Online publication date: 2013
  • (2012)Trajectories for novel and detailed traffic informationProceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoStreaming10.1145/2442968.2442973(32-39)Online publication date: 6-Nov-2012

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