UNIWA Diavgeia: Automated Subject and Type Categorization on Organizational Records

This paper researches the intentions and the potential benefits associated with the integration of deep and machine learning technologies into archival and records management practices. With the escalating volume and intricacy of digital records, conventional methods of organizing, categorizing, and administering records confront modern-day challenges. Deep learning (DL) technologies offer prospects to revolutionize the maintenance, accessibility, and utilization of records. This research proposes a case study implementation of deep learning methodologies for thematic and type categorization of records within the University of West Attica (UNIWA). Findings highlight the necessity of deepening the standardization of governmental records management processes in the new big data era. By delving into this subject, the paper endeavors to contribute to a deeper comprehension of the transformative potential of deep and machine learning technologies in archives and records management, aiming to guide future practices and decision-making in the field. Additionally, it represents the initial practical segment of an ongoing research endeavor concerning the computational archival science of records at UNIWA.