Efficiently Querying Moving Objects with Pre-defined Paths in a Distributed Environment

Due to the recent growth of the World Wide Web, numerous spatio-temporal applications can obtain their required information from publicly available web sources. We consider those sources maintaining moving objects with predefined paths and schedules, and investigate different plans to perform queries on the integration of these data sources efficiently. Examples of such data sources are networks of railroad paths and schedules for trains running between cities connected through these networks. A typical query on such data sources is to find all trains that pass through a given point on the network within a given time interval. We show that traditional filter+semi-join plans would not result in efficient query response times on distributed spatio-temporal sources. Hence, we propose a novel spatio-temporal filter, called deviation filter, that exploits both the spatial and temporal characteristics of the sources in order to improve the selectivity. We also report on our experiments in comparing the performances of the alternative query plans and conclude that the plan with spatio-temporal filter is the most viable and superior plan.