Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/1052199.1052203acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdmsnConference Proceedingsconference-collections
Article

Predictive filtering: a learning-based approach to data stream filtering

Published: 01 August 2004 Publication History

Abstract

Recent years have witnessed an increasing interest in filtering of distributed data streams, such as those produced by networked sensors. The focus is to conserve bandwidth and sensor battery power by limiting the number of updates sent from the source while maintaining an acceptable approximation of the value at the sink. We propose a novel technique called Predictive Filtering. We use matching predictors at the source and the sink simultaneously to predict the next update. The update is streamed only when the difference between the actual and the predicted value at the source increases beyond a threshold. Different predictors can be plugged into our framework, and we present a comparison of the effectiveness of various predictors. Through experiments performed on a bee-motion tracking log we demonstrate the effectiveness of our algorithm in limiting the number of updates while maintaining a good approximation of the streamed data at the sink.

References

[1]
C. Olston, J. Jiang, J. Widom. Adaptive Filters for Continuous Queries over Distributed Data Streams. In proceedings of the ACM SIGMOD International Conference on Management of Data, 2003.
[2]
R. Min, M. Bharadwaj, S. Cho, A. Sinha, E. Shih, A. Wang and A. Chandrakasan. Low-power wireless sensor networks. In proceedings of the Fourteenth International Conference on VLSI Design, India, January 2001.
[3]
H. Yu and A. Vahdat. Efficient numerical error bounding for replicated network services. In proceedings of the Twenty Sixth International Conference on Very Large Databases, Cairo, Egypt, September 2000.
[4]
S. Babu, J. Widom (2001) Continuous Queries over Data Streams. SIGMOD Record 30(3):109--120
[5]
D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, G. Seidman, M. Stonebraker, N. Tatbul, S. Zdonik. Monitoring Streams: A new class of data management applications. In proceedings of the twenty seventh International Conference on Very Large Databases, Hong Kong, August 2002.
[6]
J. Chen, D. DeWitt, F. Tian, Y. Wang. NiagaraCQ: A scalable continuous query system for internet databases. In proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, May 2000.
[7]
S. Chandrasekaran, M. Franklin. Streaming Queries over Streaming Data. In proceesings of the twenty seventh International Conference on Very Large Databases, Hong Kong, August 2002.
[8]
J. M. Kahn, R. H. Katz, K. S. J. Pister. Next century challenges: Mobile Networking for "smart dust". In the proceedings of the ACM/IEEE International Conference on Mobile Computing and Network Monitoring (MobiComm-99), Seattle, Washington, August 1999.
[9]
S. Madden, M. J. Franklin. Fjording the stream: An Architecture for queries over streaming sensor data. In the proceedings of the 18th International Conference on Data Engineering, San Jose, California, February 2002.
[10]
G. J. Pottie, W. J. Kaiser. Wireless integrated network sensors. Communications of the ACM, 43(5):551--558, May 2000
[11]
Joseph J. LaViola Jr. Double exponential smoothing: an alternative to Kalman filter-based predictive tracking. Proceedings of the workshop on Virtual environments, Zurich, 2003.
[12]
C. Lee Giles, Steve Lawrence, A. C. Tsoi. Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference. Machine Learning Journal, volume 44, 2001.
[13]
Amalia Foka. Time Series Prediction Using Evolving Polynomial Neural Networks. MS Dissertation.
[14]
S. Bengio, F. Fessant, and D. Collobert. A Connectionist System for Medium-Term Horizon Time Series Prediction. In International Workshop on Applications of Neural Networks to Telecommunications, Stockholm, Sweden, 1995.
[15]
G. Cybenko. Approximation by superposition of sigmoidal functions. Mathematics of Control, Signal and Systems, 2:303--314, 1989.
[16]
F. Fessant, S. Bengio, and D. Collobert. On the Prediction of Solar Activity Using Different Neural Network Models. Annales Geophysicae, 1995.
[17]
F. Fessant, S. Bengio, and D. Collobert. Use of Modular Architectures for Time Series Prediction. Neural Processing Letters, 1995.
[18]
Greg Eisenhauer, Fabian Bustamente and Karsten Schwan. A Middleware Toolkit for Client-Initiated Service Specialization. Proceedings of the PODC Middleware Symposium - July 18--20, 2000.
[19]
Greg Eisenhauer. The ECho Event Delivery System. Technical Report GIT-CC-99-08, College of Computing, Georgia Institute of Technology, Atlanta.
[20]
http://borg.cc.gatech.edu/biotracking/

Cited By

View all
  • (2014)A neural data-driven approach to increase Wireless Sensor Networks' lifetime2014 World Symposium on Computer Applications & Research (WSCAR)10.1109/WSCAR.2014.6916805(1-3)Online publication date: Jan-2014
  • (2011)Balancing load in stream processing with the cloudProceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops10.1109/ICDEW.2011.5767653(16-21)Online publication date: 11-Apr-2011
  • (2010)Towards in-network data prediction in wireless sensor networksProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774210(592-596)Online publication date: 22-Mar-2010
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
DMSN '04: Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
August 2004
124 pages
ISBN:9781450377959
DOI:10.1145/1052199
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]

Sponsors

  • Intel: Intel

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2004

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Article

Acceptance Rates

Overall Acceptance Rate 6 of 16 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 29 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2014)A neural data-driven approach to increase Wireless Sensor Networks' lifetime2014 World Symposium on Computer Applications & Research (WSCAR)10.1109/WSCAR.2014.6916805(1-3)Online publication date: Jan-2014
  • (2011)Balancing load in stream processing with the cloudProceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops10.1109/ICDEW.2011.5767653(16-21)Online publication date: 11-Apr-2011
  • (2010)Towards in-network data prediction in wireless sensor networksProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774210(592-596)Online publication date: 22-Mar-2010
  • (2010)Study on Cost-Sensitive Communication Models on Large-scale Monitor NetworksProceedings of the 2010 International Conference on E-Business and E-Government10.1109/ICEE.2010.539(2133-2136)Online publication date: 7-May-2010
  • (2009)Maximizing the lifetime of wireless sensor networks through intelligent clustering and data reduction techniquesProceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference10.5555/1688345.1688784(2508-2513)Online publication date: 5-Apr-2009
  • (2009)A query processor for prediction-based monitoring of data streamsProceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology10.1145/1516360.1516409(415-426)Online publication date: 24-Mar-2009
  • (2009)Maximizing the Lifetime of Wireless Sensor Networks through Intelligent Clustering and Data Reduction Techniques2009 IEEE Wireless Communications and Networking Conference10.1109/WCNC.2009.4917803(1-6)Online publication date: Apr-2009
  • (2009)A Prediction Framework for Distributed Data Stream ProcessingProceedings of the 2009 Pacific-Asia Conference on Circuits, Communications and Systems10.1109/PACCS.2009.194(179-183)Online publication date: 16-May-2009
  • (2009)Research on Cost-Sensitive Communication Models over Distributed Data Streams ProcessingProceedings of the 2009 First International Conference on Advances in Databases, Knowledge, and Data Applications10.1109/DBKDA.2009.19(120-124)Online publication date: 1-Mar-2009
  • (2008)Agregação e predição de dados em rede com precisão ajustável no processamento de consultas em redes de sensores sem fioProceedings of the 23rd Brazilian symposium on Databases10.5555/1498932.1498947(150-164)Online publication date: 13-Oct-2008
  • Show More Cited By

View Options

Get Access

Login options

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