@article{WoltmannVolkDinzingeretal., author = {Woltmann, Lucas and Volk, Peter and Dinzinger, Michael and Gr{\"a}f, Lukas and Strasser, Sebastian and Schildgen, Johannes and Hartmann, Claudio and Lehner, Wolfgang}, title = {Data Science Meets High-Tech Manufacturing - The BTW 2021 Data Science Challenge}, series = {Datenbank-Spektrum}, volume = {45}, journal = {Datenbank-Spektrum}, publisher = {Springer Nature}, doi = {10.1007/s13222-021-00398-4}, pages = {5 -- 10}, abstract = {For its third installment, the Data Science Challenge of the 19th symposium "Database Systems for Business, Technology and Web" (BTW) of the Gesellschaft f{\"u}r Informatik (GI) tackled the problem of predictive energy management in large production facilities. For the first time, this year's challenge was organized as a cooperation between Technische Universit{\"a}t Dresden, GlobalFoundries, and ScaDS.AI Dresden/Leipzig. The Challenge's participants were given real-world production and energy data from the semiconductor manufacturer GlobalFoundries and had to solve the problem of predicting the energy consumption for production equipment. The usage of real-world data gave the participants a hands-on experience of challenges in Big Data integration and analysis. After a leaderboard-based preselection round, the accepted participants presented their approach to an expert jury and audience in a hybrid format. In this article, we give an overview of the main points of the Data Science Challenge, like organization and problem description. Additionally, the winning team presents its solution.}, language = {en} }