Density-based pattern discovery in distributed time series

Time series data is a very common kind of data in many different fields. In particular, unknown frequent pattern discovery is one of the core activities in many time series mining algorithms. Several solutions to pattern discovery have been proposed so far. However, all solutions assume centralized dataset. With increasingly development of network technology distributed data analysis has become popular, raising issues like scalability and cost minimization. Additionally, some scenarios such as mining distributed medical or financial data involves the question of how to preserve data privacy. In this paper, we present a density based pattern discovery algorithm for time series, which is shown to be efficient and privacy-preserving.