<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>1160</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>other</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-10-28</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Schema Inference with Explainable AI for Data Engineering in Government Institutions</title>
    <abstract language="eng">Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions.</abstract>
    <subTitle language="eng">Systematic Review Protocol</subTitle>
    <identifier type="urn">urn:nbn:de:bvb:92-opus4-11609</identifier>
    <identifier type="doi">10.34646/thn/ohmdok-1160</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">true</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christian Koch</author>
    <author>Dirk Riehle</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Explainable Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Explainable Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Schema Inference</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Government</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät Betriebswirtschaft</collection>
    <thesisPublisher>Technische Hochschule Nürnberg Georg Simon Ohm</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-ohm/files/1160/schema_inference_slr_protocol.pdf</file>
  </doc>
</export-example>
