Stream Window Join: Tracking Moving Objects in Sensor-Network Databases

Stream Window Join: Tracking Moving Objects in Sensor-Network Databases (2003)

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by Moustafa A. Hammad , Walid G. Aref , Ahmed K. Elmagarmid

Venue:	In SSDBM
Citations:	38 - 11 self

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Datum	Value	Source
TITLE	Stream Window Join: Tracking Moving Objects in Sensor-Network Databases	INFERENCE
AUTHOR NAME	Moustafa A. Hammad	user correction - Legacy Corrections
AUTHOR AFFIL	Purdue University	user correction - Legacy Corrections
AUTHOR ADDR	West Lafayette, IN 47907, USA	user correction - Legacy Corrections
AUTHOR NAME	Walid G. Aref	user correction - Legacy Corrections
AUTHOR AFFIL	Purdue University	user correction - Legacy Corrections
AUTHOR ADDR	West Lafayette, IN 47907, USA	user correction - Legacy Corrections
AUTHOR NAME	Ahmed K. Elmagarmid	user correction - Legacy Corrections
ABSTRACT	The widespread use of sensor networks presents revolutionary opportunities for life and environmental science applications. Many of these applications involve continuous queries that require the tracking, monitoring, and correlation of multi-sensor data that represent moving objects. We propose to answer these queries using a multi-way stream window join operator. This form of join over multisensor data must cope with the infinite nature of sensor data streams and the delays in network transmission. This paper introduces a class of join algorithms, termed W-join, for joining multiple infinite data streams. W-join addresses the infinite nature of the data streams by joining stream data items that lie within a sliding window and that match a certain join condition. W-join can be used to track the motion of a moving object or detect the propagation of clouds of hazardous material or pollution spills over time in a sensor network environment. We describe two new algorithms for W-join, and address variations and local/global optimizations related to specifying the nature of the window constraints to fulfill the posed queries. The performance of the proposed algorithms are studied experimentally in a prototype stream database system, using synthetic data streams and real time-series data. Tradeoffs of the proposed algorithms and their advantages and disadvantages are highlighted, given variations in the aggregate arrival rates of the input data streams and the desired response times per query.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	In SSDBM	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	75--84	INFERENCE
CITATIONS	17 found	ParsCit 1.0