Multi-dimensional phenomenon-aware stream query processing

Geographically co-located sensors tend to participate in the same environmental phenomena. Phenomenon-aware stream query processing improves scalability by subscribing each query only to a subset of sensors that participate in the phenomena of interest to that query. In the case of sensors that generate readings with a multi-attribute schema, phenomena may develop across the values of one or more attributes. However tracking and detecting phenomena across all attributes does not scale well as the dimensions increase. As the size of sensor network increases, and as the number of attributes being tracked by a sensor increases this becomes a major bottleneck. In this paper, we present a novel n-dimensional Phenomenon Detection and Tracking mechanism (termed as nd-PDT) over n-ary sensor readings. We reduce the number of dimensions to be tracked by first dropping dimensions without any meaningful phenomena, and then we further reduce the dimensionality by continuously detecting and updating various forms of functional dependencies amongst the phenomenon dimensions