Data management challenges for computational transportation
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Multi-dimensional phenomenon-aware stream query processing
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
MG-join: detecting phenomena and their correlation in high dimensional data streams
Distributed and Parallel Databases
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A phenomenon appears in a sensor network when a group of sensors persist to generate similar behavior over a period of time. PhenomenaBases (or databases of phenomena) are equipped with Phenomena Detection and Tracking (PDT) techniques that continuously run in the background of a sensor database system to detect new phenomena and to track already existing phenomena. The process of phenomena detection and tracking is initiated by a multi-way join operator that comes at the core of PDT techniques to report similar sensor readings. With the increase in the sensor network size, the join operator (and, consequently, query processing in the PhenomenaBase) face several scalability challenges. In this paper, we present a join operator for PhenomenaBases (the SNJoin operator) that is specially-designed for dynamically-configured large-scale sensor networks with distributed processing capabilities. Experimental studies illustrate the scalability of the proposed join operator in PhenomenaBases with respect to the number of detected phenomena and the output delay.