MORE | Management of Real-time Energy Data - IT, big data and machine learning challenges in time series data - Renewable Energy Sources (RES) sector | Management of Real-time Energy Data

Themis Palpanas

Easy, Accurate, and Fast Complex Analytics on Big Data Series
Collections: Renewable Energy Sources and Beyond

There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to manage and analyze very large collections of sequences, or data series. Examples of such applications come from various monitoring applications, including in power utility companies, where we need to apply machine learning techniques for knowledge extraction. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. However, no existing data management solution can offer native support for sequences and the corresponding operators necessary for complex analytics. In this talk, we describe our efforts in designing techniques for indexing and analyzing truly massive collections of data series that enable scientists to run complex analytics on their data. These techniques are orders of magnitude faster than the state of the art, and are applied, among others, on datasets derived from operation monitoring applications (e.g., from sensors on wind turbine farms), as well as remote sensing applications with the purpose of guiding predictive maintenance and operational optimization pipelines.

Profile

Themis Palpanas is Senior Member of the French University Institute (IUF), a distinction that recognizes excellence across all academic disciplines, and professor of computer science at the University of Paris (France), where he is director of the Data Intelligence Institute of Paris (diiP), and director of the data management group, diNo. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the University of California at Riverside, University of Trento, and at IBM T.J. Watson Research Center, and visited Microsoft Research, and the IBM Almaden Research Center. His interests include problems related to data science (big data analytics and machine learning applications). He is the author of 9 US patents (3 of which have been implemented in world-leading commercial data management products), and 2 French patents. He is the recipient of 3 Best Paper awards, and the IBM Shared University Research (SUR) Award. He is currently serving on the VLDB Endowment Board of Trustees, as an Associate Editor in the TKDE, and IDA journals, as well as on the Editorial Advisory Board of the IS journal, and the Editorial Board of the TLDKS Journal. He has served as Editor in Chief for the BDR Journal (that he drove to an impact factor of 3.578 and cite score of 8.6), as General Chair for VLDB 2013, Associate Editor for VLDB 2022, 2019 and 2017, Research PC Vice Chair for ICDE 2020, and Workshop Chair for EDBT 2016, ADBIS 2013, and ADBIS 2014, General Chair for the PDA@IOT International Workshop (in conjunction with VLDB 2014), and General Chair for the Event Processing Symposium 2009.