A new perspective on trajectory compression techniques

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 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. Previously some work has been done in data compression for GIS and mainly in line generalization area considering two dimensional data. However, time-stamped positions data do not form a line, as they are historically traced points. On the other hand, researches in compression for time series data mainly deal with one dimensional time series and are good for short time series and in absence of noise. This paper addresses the drawbacks of existing compression techniques and describes different techniques to compress short as well as lengthy stream of time-stamped positions data