BMQ-Processor: A High-Performance Border-Crossing Event Detection Framework for Large-Scale Monitoring Applications

• Published in: IEEE transactions on knowledge and data engineering Vol. 21; no. 2; pp. 234 - 252
• Main Authors: LEE, Jinwon, KANG, Seungwoo, LEE, Youngki, SANG JEONG LEE, SONG, Junehwa
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-02-2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithms; Applied sciences; BMQ-Index; border monitoring; Borders; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Construction costs; Costs; Data stream processing; Data structures; Event detection; event-based system; Exact sciences and technology; incremental processing; Information systems. Data bases; Large-scale systems; Memory and file management (including protection and security); Memory organisation. Data processing; mobile environment; Monitoring; Monitors; Multidimensional systems; Performance analysis; Query processing; region transition monitoring; Scalability; Semantics; sensor network; Sensor systems; Software; stateful query index; Streams; Sensor networks; Data stream processsing; Query index; Mobile environments; Event-based system; Streaming; High performance; Scalability; Database query; mobile environment; Updating; BMQ-lndex; Context aware; incremental processing; region transition monitoring; Storage access; stateful query index; Reactive system; Surveillance; sensor network; Semantics; event-based system; border monitoring; Sensor array; Data structure; Data stream processing; Mobile computing; Monitoring
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2008.140

In this paper, we present BMQ-Processor, a high-performance border-crossing event (BCE) detection framework for large-scale monitoring applications. We first characterize a new query semantics, namely, border monitoring query (BMQ), which is useful for BCE detection in many monitoring applications. It monitors the values of data streams and reports them only when data streams cross the borders of its range. We then propose BMQ-Processor to efficiently handle a large number of BMQs over a high volume of data streams. BMQ-Processor efficiently processes BMQs in a shared and incremental manner. It develops and operates over a novel stateful query index, achieving a high level of scalability over continuous data updates. Also, it utilizes the locality embedded in data streams and greatly accelerates successive BMQ evaluations. We present data structures and algorithms to support 1D as well as multidimensional BMQs. We show that the semantics of border monitoring can be extended toward more advanced ones and build region transition monitoring as a sample case. Lastly, we demonstrate excellent processing performance and low storage cost of BMQ-Processor through extensive analysis and experiments.