Using Ontology Based Knowledge Discovery in Location Based Services
Ali Mousavi, Andrew J. S. Hunter · 2012
Rapid development of information technology for mobile computing, improvement in the accuracy of positioning systems, and ubiquitous use of mobile devices has generated large quantities of raw trajectories that represent the movement of moving objects. Mining such data, which contain not only space and time attributes, but also context attributes, is significant for many applications within the location based services domain. Such systems can provide more effective services to users through an understanding of a moving objects location, its context, and interests. But, in spite of the fact that most service providers offer various services to users, they are unable to identify relevant customers at the right time and right place. Recently different methods of data mining have been used in location based services for extracting patterns and modeling the behavior of users. However, due to the excessive number of extracted patterns it has been very difficult to infer knowledge from these patterns for an application domain. Given that several criteria such as geographic area, the nature of the entity, etc., influence movement behavior, one needs to consider this complexly during the knowledge discovery process. Therefore, this paper proposes a model that integrates an ontology-based approach for efficient interpretation of extracted patterns from an objects movement behavior.