Continuous Query Processing on Spatio-Temporal Data Streams

This thesis addresses important challenges in the areas of streaming and spatio-temporal databases. It focuses on continuous querying of spatio-temporal environments characterized by (1) a large number of moving and stationary objects and queries; (2) need for near real-time results; (3) limited memory and cpu resources; and (4) different accuracy requirements. The first part of the thesis studies the problem of performance vs. accuracy tradeoff using different location modelling techniques when processing continuous spatio-temporal range queries on moving objects. Two models for modeling the movement, namely: continuous and discrete models are described. This thesis introduces an accuracy comparison model to estimate the quality of the answers returned by each of the models. Experimental evaluations show the effectiveness of each model given certain characteristics of spatio-temporal environment (e.g., varying speed, location update frequency). The second part of the thesis introduces SCUBA, a Scalable Cluster Based Algorithm for evaluating a large set of continuous queries over spatio-temporal data streams. Unlike the commonly used static grid indices, the key idea of SCUBA is to group moving objects and queries based on common dynamic properties (e.g., speed, destination, and road network location) at run-time into moving clusters. This results in improvement in performance which facilitate scalability. SCUBA exploits shared cluster-based execution consisting of two phases. In phase I, the evaluation of a set of spatio-temporal queries is abstracted as a spatial join between moving clusters for cluster-based filtering of true negatives. There after, in phase II, a fine-grained join process is executed for all pairs identified as potentially joinable by a positive cluster-join match in phase I. If the clusters don’t satisfy the join predicate, the objects and queries that belong to those clusters can be savely discarded as being guaranteed to not join individually either. This provides processing cost savings.Another advantage of SCUBA is that moving cluster-driven load shedding is facilitated. A moving cluster (or its subset, called nucleus) approximates the locations of its members. As a consequence relatively accurate answers can be produced using solely the abstracted cluster location information in place of precise object-by-object matches, resulting in savings in memory and i improvement in processing time. A theoretical analysis of SCUBA is presented with respect to the memory requirements, number of join comparisons and I/O costs. Experimental evaluations on real datasets demonstrate that SCUBA achieves a substantial improvement when executing continuous queries on highly dense moving objects. The experiments are conducted in a real data streaming system (CAPE) developed at WPI on real datasets generated by the Network-Based Moving Objects Generator.