Key Concepts of Group Pattern Discovery Algorithms from Spatio-Temporal Trajectories

Over the years, the increasing development of location acquisition devices have generated a significant amount of spatio-temporal data. This data can be further analysed in search for some interesting patterns, new information, or to construct predictive models such as next location prediction. The goal of this paper is to contribute to the future research and development of group pattern discovery algorithms from spatio-temporal data by providing an insight into algorithms design in this research area which is based on a comprehensive classification of state-of-the-art models. This work includes static, big data as well as data stream processing models which to the best of authors' knowledge is the first attempt of presenting them in this context. Furthermore, currently available surveys and taxonomies in this research area do not focus on group pattern mining algorithms nor include the state-of-the-art models. The authors conclude with the proposal of a conceptual model of Universal, Streaming, Distributed and Parameter-light (USDP) algorithm that addresses current challenges in this research area.