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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  • M.H. Ali
  • W.G. Aref
  • R. Bose
  • A.K. Elmagarmid
  • Sumi Helal
  • I. Kamel
  • M.F. Mokbel
Publication date2005
Host publicationVLDB '05 Proceedings of the 31st international conference on Very large data bases
PublisherVLDB Endowment
Number of pages4
ISBN (print)1595931546
<mark>Original language</mark>English


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.