Recent advances in affordable positioning hardware and software have made the availability of location data ubiquitous. Personal devices such as tablet PCs, smart phones and even sport watches are all able to collect and store a user’s location over time, providing an ever-growing supply of spatiotemporal data. Managing this plethora of data is a relatively new challenge and there has been a great deal of research in the recent years devoted to the problems that arise from spatiotemporal data. This book chapter surveys recent developments in the techniques used for the management and mining of spatiotemporal data. We focus our survey on three main areas: (i) data management, which includes indexing and querying mobile objects, (ii) tracking, making use of noisy location observations to infer an object’s actual or future position, and (iii) mining, extracting interesting patterns from spatiotemporal data. First, we cover recent advances in database systems for managing spatiotemporal data, including index structures and efficient algorithms for processing queries. Next, we review the problem of tracking for mobile objects to estimate an object’s location given a sequence of noisy observations. We discuss some of the common approaches used for tracking and examine some recent work which focuses specifically on tracking vehicles using a road network. Then we review the recent literature on mining spatiotemporal data. We conclude by discussing some interesting areas of future research.
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