<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>2655</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>5</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>45</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data Science Meets High-Tech Manufacturing – The BTW 2021 Data Science Challenge</title>
    <abstract language="eng">For its third installment, the Data Science Challenge of the 19th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für 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 Universität 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.</abstract>
    <parentTitle language="eng">Datenbank-Spektrum</parentTitle>
    <identifier type="doi">10.1007/s13222-021-00398-4</identifier>
    <enrichment key="opus.import.date">2022-01-28T13:58:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Lucas Woltmann</author>
    <author>Peter Volk</author>
    <author>Michael Dinzinger</author>
    <author>Lukas Gräf</author>
    <author>Sebastian Strasser</author>
    <author>Johannes Schildgen</author>
    <author>Claudio Hartmann</author>
    <author>Wolfgang Lehner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Science</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
  </doc>
</export-example>
