A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025)

• URL: http://arxiv.org/abs/2603.00399v2
• Date: Tue, 03 Mar 2026 22:40:07 GMT
• Title: A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025)
• Authors: H. Sinan Bank, Casey E. Eaton,
• Abstract summary: This paper presents a computational analysis of IISE proceedings from 2002 to 2025.<n>We map thematic evolution to identify dominant, emerging, and receding research topics.<n>The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowledge diffusion and community structure. Through this comprehensive analysis, we establish a baseline for understanding the trajectory of ISE research and offer valuable insights for researchers, practitioners, and educators. The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE. To foster reproducibility and further research, the curated dataset used in this study and the results will be made publicly available.

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