Trial lecture
"Transparency and explainable AI in business applications”.
Time and place: June 5, 2024 11:15 AM, Kristen Nygaards sal (5370), Ole-Johan Dahls hus
Main research findings
In recent years, organizations have increasingly utilized Visual Analytics (VA) to harness their data assets and generate meaningful insights for decision-making support. VA, as the science of analytical reasoning with the help of interactive data visualizations, facilitates deriving significant information from raw data. Nevertheless, there are still many challenges regarding the application and integration of VA into business operations.
This study aimed to identify VA application challenges, specifically in the Supply Chain (SC) domain, provide a solution to mitigate those challenges, and finally, create a demonstrator for the solution to evaluate the feasibility and applicability of the solution. As a result, a conceptual framework was proposed that can be used as a process guideline for VA system development.
The practical contribution involves proposing a solution for VA development that include a mapping of SC business processes to VA tasks. Furthermore, a VA tool aligned with the proposed framework structure was developed. The tool includes a solution for the automatic generation of visualization dashboards through a no-code platform for non-technical users.
The results of this research can benefit both research and practice by providing methodological and practical contributions to the fields of VA and SC analytics.
Adjudication committee
- Associate Professor Kazi Shah Nawaz Ripon, Oslo Metropolitan University (OsloMet), Norway
- Assistant Professor Clara Maathuis, Open University of the Netherlands, Netherland
- Associate Professor Michael Kirkedal Thomsen, Department of Informatics, University of Oslo, Norway
Supervisors
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Professor Ricardo Colomo-Palacios, Department of Computer Science and Communication, Østfold University College, Norway
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Professor Antonio Martini, Department of Informatics, University of Oslo, Norway
Chair of defence:
Professor Xing Cai
Contact information at Department: Mozhdeh Sheibani Harat