NILE-PDT: A phenomenon detection and tracking framework for data stream management systems

M. H. Ali, W. G. Aref, R. Bose, A. K. Elmagarmid, A. Helal, I. Kamel, M. F. Mokbel

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

28 Citations (Scopus)

Abstract

In this demo, we present Nile-PDT, a Phenomenon Detection and Tracking framework using the Nile data stream management system. A phenomenon is characterized by a group of streams showing similar behavior over a period of time. The functionalities of Nile-PDT is split between the Nile server and the Nile-PDT application client. At the server side, Nile detects phenomenon candidate members and tracks their propagation incrementally through specific sensor network operators. Phenomenon candidate members are processed at the client side to detect phenomena of interest to a particular application. Nile-PDT is scalable in the number of sensors, the sensor data rates, and the number of phenomena. Guided by the detected phenomena, Nile-PDT tunes query processing towards sensors that heavily affect the monitoring of phenomenon propagation.

Original languageEnglish
Title of host publicationVLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases
Pages1295-1298
Number of pages4
Publication statusPublished - 1 Dec 2005
EventVLDB 2005 - 31st International Conference on Very Large Data Bases - Trondheim, Norway
Duration: 30 Aug 20052 Sep 2005

Publication series

NameVLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases
Volume3

Other

OtherVLDB 2005 - 31st International Conference on Very Large Data Bases
CountryNorway
CityTrondheim
Period30/8/052/9/05

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ali, M. H., Aref, W. G., Bose, R., Elmagarmid, A. K., Helal, A., Kamel, I., & Mokbel, M. F. (2005). NILE-PDT: A phenomenon detection and tracking framework for data stream management systems. In VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases (pp. 1295-1298). (VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases; Vol. 3).