Supporting sliding window queries for continuous data streams

Lin Qiao, D. Agrawal, A. El Abbadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Although traditional databases and data warehouses have been exploited widely to manage persistent data, a large number of applications from sensor network need functional supports for transient data in the continuous data stream. One of the crucial functions is to summarize the data items within a sliding window. A sliding window contains a fixed width span of data elements. The data items are implicitly deleted from the sliding window, when it moves out of the window scope. Several one-dimensional histograms have been proposed to store the succinct time information in a sliding window. Such histograms, however, only handle the data items with attribute values in unary domains. In this paper, we explore the problem of extending the value to a multi-valued domain. A two-dimensional histogram, the hybrid histogram, is proposed to support sliding window queries on a practical multi-valued domain. The basic building block of the hybrid histogram is the exponential histogram. The hybrid histogram is maintained to capture the changes of data distribution. To further compress the exponential histograms, we propose a condensed exponential histogram without losing the error bound. Results of an extensive experimental study are included to evaluate the benefits of the proposed technique.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Scientific and Statistical Database Management, SSDBM
PublisherIEEE Computer Society
Pages85-94
Number of pages10
Volume2003-January
ISBN (Print)0769519644
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event15th International Conference on Scientific and Statistical Database Management, SSDBM 2003 - Cambridge, United States
Duration: 9 Jul 200311 Jul 2003

Other

Other15th International Conference on Scientific and Statistical Database Management, SSDBM 2003
CountryUnited States
CityCambridge
Period9/7/0311/7/03

Fingerprint

Data warehouses
Sensor networks

Keywords

  • Computer science
  • Data warehouses
  • Databases
  • Histograms
  • Lightning
  • Optical reflection
  • Radar measurements
  • Temperature sensors
  • Windows
  • Wireless sensor networks

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Qiao, L., Agrawal, D., & El Abbadi, A. (2003). Supporting sliding window queries for continuous data streams. In Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM (Vol. 2003-January, pp. 85-94). [1214970] IEEE Computer Society. https://doi.org/10.1109/SSDM.2003.1214970

Supporting sliding window queries for continuous data streams. / Qiao, Lin; Agrawal, D.; El Abbadi, A.

Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. Vol. 2003-January IEEE Computer Society, 2003. p. 85-94 1214970.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Qiao, L, Agrawal, D & El Abbadi, A 2003, Supporting sliding window queries for continuous data streams. in Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. vol. 2003-January, 1214970, IEEE Computer Society, pp. 85-94, 15th International Conference on Scientific and Statistical Database Management, SSDBM 2003, Cambridge, United States, 9/7/03. https://doi.org/10.1109/SSDM.2003.1214970
Qiao L, Agrawal D, El Abbadi A. Supporting sliding window queries for continuous data streams. In Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. Vol. 2003-January. IEEE Computer Society. 2003. p. 85-94. 1214970 https://doi.org/10.1109/SSDM.2003.1214970
Qiao, Lin ; Agrawal, D. ; El Abbadi, A. / Supporting sliding window queries for continuous data streams. Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM. Vol. 2003-January IEEE Computer Society, 2003. pp. 85-94
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