Temporal moving pattern mining for location-based service

This paper proposes a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions, and uses the spatial operation to generalize a location of moving point.

Abstract

The primary objective of location-based service (LBS) which is generally described as a mobile information service is to provide useful location aware information, at a minimum cost and resources, to its users. This functionality can be implemented through data mining techniques. However, since the conventional studies on data mining do not consider spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the spatial attributes that is changing over time. Defining individual users of LBS as moving objects, this paper proposes a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions. For this purpose, we use the spatial operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Through the experiments, we show that our technique generates temporal patterns found in frequent moving sequences in efficient. Finally, the spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the quality of LBS.