GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects

Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel to-gether. Usually, these object sets are called spatio-temporal patterns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different ap-proaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pat-tern. In addition to being a painstaking task due to the large number of algorithms used to mine and manage pat-terns, it is also time consuming. Moreover, we have to exe-cute these algorithms again whenever new data are added to the existing database. To address these issues, we first re-define spatio-temporal patterns in the itemset context. Sec-ondly, we propose a unifying approach, named GeT Move, which uses a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT Move is implemented in two versions which are GeT Move and Incremental GeT Move. To optimize the efficiency and to free the parameters setting, we also propose a Parameter Free Incremental GeT Move al-gorithm. Comprehensive experiments are performed on real datasets as well as large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches. 1