Modern data acquisition techniques such as Global positioning system (GPS), Radio-frequency identification (RFID) and mobile phones have resulted in the collection of huge amounts of data in the form of trajectories during the past years. Popularity of these technologies and ubiquity of mobile devices seem to indicate that the amount of spatio-temporal data will increase at accelerated rates in the future. Many previous studies have focused on efficient techniques to store and query trajectory databases. Early approaches to recovering information from this kind of data include single predicate range and nearest neighbour queries. However, they are unable to capture collective behaviour and correlations among moving objects. Recently, a new interest for querying patterns capturing ‘group’ or ‘common’ behaviours have emerged. An example of this type of pattern are moving flocks. These are defined as groups of moving objects that move together (within a predefined distance to each other) for a certain continuous period of time. Current algorithms to discover moving flock patterns report problems in scalability and the way the discovered patterns are reported. The field of frequent pattern mining has faced similar problems during the past decade, and has sought to provided efficient and scalable techniques which successfully deal with those issues. This research proposes a framework which integrates techniques for clustering, pattern mining detection, postprocessing and visualization in order to discover and analyse moving flock patterns in large trajectory datasets. The proposed framework was tested and compared with a current method (BFE algorithm). Synthetic datasets simulating trajectories generated by large number of moving objects were used to test the scalability of the framework. Real datasets from different contexts and characteristics were used to assess the performance and analyse the discovered patterns. The framework shows to be efficient, scalable and modular. This research shows that moving flock patterns can be generalized as frequent patterns and state-of-the-art algorithms for frequent pattern mining can be used to detect the moving flock patterns. This research develops preliminary visualization of the most relevant findings. Appropriate interpretation of the results demands further analysis in order to display the most relevant information.
MSc in Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM). The University of Southampton (UK), Lund University (Sweden), The University of Twente (Netherlands)