Efficient STMPM (Spatio-Temporal Moving Pattern Mining) Using Moving Sequence Tree

Recently, based on the dynamic location or mobility of a moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for the development of location based services. The performance of moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data. Some of the traditional spatio-temporal pattern mining methods[1,2,3,4,13] have proposed to solve these problems, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose a new STMPM (spatio-temporal moving pattern mining) method which efficiently extracts the periodical or sequential moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces frequent moving patterns mining execution time, using the moving sequence tree which is generated from the historical data of moving objects based on hash tree. And also, to minimize the required memory space, the method generalizes detained historical data, including spatio-temporal attributes, into the real world scopes of space and time by using spatio-temporal concept hierarchy.