Towards mining approaches for trajectory data

Abstract

The increasing frequency of location-acquisition technologies (RFID, GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the prospect of discovering usable knowledge about movement behaviour, which encourages novel applications and services. Therefore trajectory data mining is emerging as a novel area of research. There are many constraints to mining the trajectory data and analyze the mobility data by means of appropriate patterns and models extracted by efficient algorithms and development of novel knowledge discovery processes explicitly modified to analyze trajectory with reference to geography, at appropriate scales and granularity. Urban traffic simulations are a straightforward example of application for this kind of knowledge, since a classification model can represent a sophisticated alternative to the simple ad- hoc behavior rules, provided by domain experts, on which actual simulators are based. In this study we present brief review of the trajectory data mining. There are still open research issues those still unexplored. Therefore in this paper, some research challenges and future works are reported