Spatiotemporal Compression Techniques for Moving Point Objects

Moving object data handling has received a fair share of attention over recent years in the spatial database community. This is understandable as positioning technology is rapidly making its way into the consumer market, not only through the already ubiquitous cell phone but soon also through small, on-board positioning devices in many means of transport and in other types of portable equipment. It is thus to be expected that all these devices will start to generate an unprecedented data stream of time-stamped positions. Sooner or later, such enormous volumes of data will lead to storage, transmission, computation, and display challenges. Hence, the need for compression techniques. Although previously some work has been done in compression for time series data, this work mainly deals with one-dimensional time series. On the other hand, they are good for short time series and in absence of noise, two characteristics not met by moving objects. We target applications in which present and past positions of objects are important, so focus on the compression of moving object trajectories. The paper applies some older techniques of line generalization, and compares their performance against algorithms that we specifically designed for compressing moving object trajectories