Big Data Science, Streams and Process Mining
Data Science combines statistical modeling and methods from computer science to better support application domain experts in analyzing their data. We witness improvements in both, quality and efficiency of data analyses throughout all areas of our technical, scientific, economic and societal environments. The limits of algorithms in machine learning, data mining, and knowledge discovery are not in sight yet. As an example, new methods for analyzing processes in production, logistics and administration show better failure detection, time savings and improved user support.
Thomas Seidl is professor for Computer Science and head of the Database Systems and Data Mining group at Ludwig-Maximilians-Universität München (LMU Munich). He studied Computer Science at Technische Universität München (TUM) and obtained his PhD in 1997 and his Habilitation in 2001 from LMU. From 2002 to 2016 he headed the data management and data exploration group at RWTH Aachen University. His fundamental research on data mining and database technologies with application domains in engineering, business, life science and humanities yielded more than 250 scientific publications so far. He serves on many program committees and scientific boards, directs the Munich Center for Machine Learning (MCML) and is co-chair of the LMU Data Science Lab and of the elite Master program in Data Science at LMU.