In recent years, with the popularization of the mobile devices, more and more location-based applications have been developed. As a result, location data of various objects is widely available. Identifying object groups that tend to move together is an emerging research topic. Existing approaches for identifying mobile group patterns assume the existence of raw location data which records a given object¡¦s position at every equal-spaced time point. However, a moving object may become disconnected voluntarily or involuntarily from time to time, and thus this assumption may not always valid. In this research, we describe the locations of moving object as a (non-continuous) trajectory function. Based on the new model, we re-define the mobile group mining problem and develop efficient algorithms for mining mobile groups. The proposed algorithms are evaluated via synthetic data generated by IBM City Simulator.

In the past few years, due to the development of the mobile devices and the improvement of database technology, the geometric information has become widely available. Identifying object groups based on spatial-temporal dimension is an emerging research topic. Previous work has incorporating the spatial and temporal information pertaining to moving objects in finding mobile groups. Considering that mobile groups tend to exhibit some calendar-like temporal features, we define a new temporal presentation mechanism called flexible calendar pattern, which allows users to specify the desired calendar patterns at a coarse level. In addition, we developed efficient algorithms for mining mobile groups pertaining some user-specified flexible calendar pattern. The proposed algorithms are evaluated via the synthetic data generated by IBM City Simulator. The results show that our approaches prove to perform more efficiently than other intuitive approaches.

In this paper, we present a novel approach to mine moving object group patterns from object movement database. At first, our approaches summarize the raw data in the source object movement database into trajectories, and then discover valid 2-groups mainly from the trajectory-based object movement database. We propose two trajectory conversion methods, namely linear regression and vector conversion. We further propose a trajectory based mobile group mining algorithm that is intended to reduce the overhead of mining 2-Group Patterns. The use of trajectories allows valid 2-groups to be mined using smaller number of summarized records (in trajectory model) and examining smaller number of candidate 2-groups. Finally, we conduct series of comprehensive experiments to evaluate and compare the performances of the proposed methods with existing approaches that use source object movement database or other summarization techniques. The experimental results demonstrate the superior performance of our proposed approach.