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Visualizing and exploring event databases: a methodology to benefit from process analytics

Author

Listed:
  • Pavlos Delias

    (Eastern Macedonia and Thrace Institute of Technology)

  • Vassilios Zoumpoulidis

    (Eastern Macedonia and Thrace Institute of Technology)

  • Ioannis Kazanidis

    (Eastern Macedonia and Thrace Institute of Technology)

Abstract

Events, routinely broadcasted by news media all over the world, are captured and get recorded to event databases in standardized formats. This wealth of information can be aggregated and get visualized with several ways, to result in alluring illustrations. However, existing aggregation techniques tend to consider that events are fragmentary, or that they are part of a strictly sequential chain. Nevertheless, events’ occurrences may appear with varying structures (i.e., others than sequence), reflecting elements of a larger, implicit process. In this work, we propose a methodology that will support analysts to get richer insights from event datasets by enabling a process perspective. Through a case study about a political phenomenon, we provide concrete recommendations on data reviewing, process discovery, and visually facilitated interpretations. We furthermore discuss the methodological and epistemological aspects that are needed to make our approach applicable for event analytics.

Suggested Citation

  • Pavlos Delias & Vassilios Zoumpoulidis & Ioannis Kazanidis, 2019. "Visualizing and exploring event databases: a methodology to benefit from process analytics," Operational Research, Springer, vol. 19(4), pages 887-908, December.
    • Handle: RePEc:spr:operea:v:19:y:2019:i:4:d:10.1007_s12351-018-00447-z
    DOI: 10.1007/s12351-018-00447-z
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    References listed on IDEAS

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    1. Rebecca H. Best & Christine Carpino & Mark J.C. Crescenzi, 2013. "An analysis of the TABARI coding system," Conflict Management and Peace Science, Peace Science Society (International), vol. 30(4), pages 335-348, September.
    2. Scholz, Michael, 2016. "R Package clickstream: Analyzing Clickstream Data with Markov Chains," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i04).
    3. Wai-Ki Ching & Ximin Huang & Michael K. Ng & Tak-Kuen Siu, 2013. "Higher-Order Markov Chains," International Series in Operations Research & Management Science, in: Markov Chains, edition 2, chapter 0, pages 141-176, Springer.
    4. Roy, Bernard, 1994. "On operational research and decision aid," European Journal of Operational Research, Elsevier, vol. 73(1), pages 23-26, February.
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    Cited by:

    1. Rahul Basole & Elliot Bendoly & Aravind Chandrasekaran & Kevin Linderman, 2022. "Visualization in Operations Management Research," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 172-187, October.
    2. Potoniec, Jedrzej & Sroka, Daniel & Pawlak, Tomasz P., 2022. "Continuous discovery of Causal nets for non-stationary business processes using the Online Miner," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1304-1320.
    3. Lorraine Eden & Charles F. Hermann & Stewart R. Miller, . "Evidence-based policymaking in a VUCA world," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.

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