Data series are a prevalent data type that has
attracted lots of interest in recent years. Specifically, there
has been an explosive interest towards the analysis of large
volumes of data series in many different domains. This is both in
businesses (e.g., in mobile applications) and in sciences (e.g., in
biology). In this tutorial, we focus on applications that produce
massive collections of data series, and we provide the necessary
background on data series storage, retrieval and analytics. We
look at systems historically used to handle and mine data in the
form of data series, as well as at the state of the art data series
management systems that were recently proposed. Moreover,
we discuss the need for fast similarity search for supporting
data mining applications, and describe efficient similarity search
techniques, indexes and query processing algorithms. Finally, we
look at the gap of modern data series management systems in
regards to support for efficient complex analytics, and we argue
in favor of the integration of summarizations and indexes in
modern data series management systems. We conclude with the
challenges and open research problems in this domain.