A rule-based method for discovering trajectory profiles

The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people’s profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery.The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people's profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery

Trajectory data mining: A review of methods and applications

The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used