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


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.

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We would like to thank our graduate students Zafeiris Papavaritis and Christianna Pantermali who spent many hours in checking every event of the original dataset for relevance, and who manually filtered them out.

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Correspondence to Pavlos Delias.

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Delias, P., Zoumpoulidis, V. & Kazanidis, I. Visualizing and exploring event databases: a methodology to benefit from process analytics. Oper Res Int J 19, 887–908 (2019).

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  • Event data
  • Process mining
  • Process analytics