NILE-PDT: A phenomenon detection and tracking framework for data stream management systems
Source of Publication
VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases
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.
Data stream management systems, Phenomenon detection, Sensor data rates, Tracking framework, Data processing, Electronic circuit tracking, Error detection, Query languages, Sensor data fusion, Sensors, Servers, Database systems
Ali, M. H.; Aref, W. G.; Bose, R.; Elmagarmid, A. K.; Helal, A.; Kamel, I.; and Mokbel, M. F., "NILE-PDT: A phenomenon detection and tracking framework for data stream management systems" (2005). All Works. 2510.
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