Nile-PDT: a phenomenon detection and tracking framework for data stream management systems

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Author

MH Ali, WG Aref, R Bose, AK Elmagarmid, A Helal, I Kamel, MF Mokbel

Entry type

inproceedings

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.

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Date

2005

Booktitle

Proceedings of the 31st international conference on Very large data bases

Journal

Very Large Data Bases

Key alpha

Aref

Pages

1295 - 1298

Publisher

ACM

Publication Date

2005-01-01

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@Inproceedings{ Aref,
	title = "Nile-PDT: a phenomenon detection and tracking framework for data stream management systems",
	author = "MH Ali, WG Aref, R Bose, AK Elmagarmid, A Helal, I Kamel, MF Mokbel",
	year = "2005",
	booktitle = "Proceedings of the 31st international conference on Very large data bases",
	journal = "Very Large Data Bases",
	pages = "1295 - 1298",
	publisher = "ACM",
	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.",
}