Fraunhofer-Institut für Angewandte Informationstechnik FIT

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  • Publication
    Towards Improving Accountability in Sensitive-Disclosure Scenarios
    ( 2024-06)
    Matzutt, Roman
    ;
    Wagner, Eric
    Public transparency has become increasingly important to uphold trust in government agencies and private companies alike, e.g., by establishing police accountability and proving abiding to ethical supply chain practices. Oftentimes, however, this public interest conflicts with the need for confidentiality of ongoing processes. In this paper, we investigate these sensitive-disclosure scenarios and the requirements for technical solutions to support the data dissemination in these scenarios. We identify translucent blockchains as a promising building block to provide transparency in sensitive-disclosure scenarios with fine-granular access control.
  • Publication
    Finding and analysing energy research funding data: The EnArgus system
    ( 2021)
    Oppermann, Leif
    ;
    Hirzel, Simon orcid-logo
    ;
    Güldner, Alexander
    ;
    Heiwolt, Karoline
    ;
    Krassowski, Joachim
    ;
    Schade, Ulrich
    ;
    Lange, Christoph
    ;
    Prinz, Wolfgang
    This paper presents the concept, a system-overview, and the evaluation of EnArgus, the central information system for energy research funding in Germany. Initiated by the German Federal Ministry for Economic Affairs and Energy (BMWi), EnArgus establishes a one-stop information system about all recent and ongoing energy research funding projects in Germany. Participants ranging from laypersons to experts were surveyed in three workshops to evaluate both the public and expert interfaces of the EnArgus system in comparison to peer systems. The results showed that the EnArgus system was predominantly evaluated positively by the various participants. It contributes to making the energy sector more transparent and offers clear advantages for professional use compared to similar systems. The system's semantic processing enables more precise hits and better coverage by including semantically related terms in search results; its intelligence makes it fail-safe, rendering it suitable for areas where poor results can have dire consequences. Reporting on an actual real-world system, the paper also provides a roadmap-view of how electronic filing of administrative project data can be semantically enhanced and opened-up to provide the basis for new ways into the data that are key for future breakthrough AI interfaces.