Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of time stamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions. A probabilistic framework is introduced for reducing the in herentuncertainty of trajectory data collected for RFID-monitored objects. The framework represents the position of an object at each instant as a random variable over the set of possible locations. The probability density function of this random variable is initialized according to an apriority probability distribution, and then revised by conditioning the event that integrity constraints are satisfied.