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We now witness a very strong interest by users across different domains on data series (a.k.a. time series) management. It is not unusual for industrial applications that produce data series to involve numbers of sequences (or subsequences) in the order of billions (i.e., multiple TBs). As a result, analysts are unable to handle the vast amounts of data series that they have to manage and process. The goal of this seminar is to enable researchers and practitioners to exchange ideas and foster collaborations in the topic of data series management and identify the corresponding open research directions. The main questions answered are the following: i) What are the data series management needs across various domains and what are the shortcomings of current systems, ii) How can we use machine learning to optimize our current data systems, and how can these systems help in machine learning pipelines? iii) How can visual analytics assist the process of analyzing big data series collections? The seminar focuses on the following key topics related to data series management: 1)Data series storage and access paterns, 2) Query optimization, 3) Machine learning and data mining for data serie, 4) Visualization for data series exploration, 5) Applications in multiple domains.