The purpose of this paper is to present a methodology to reveal complex events structures in events’ occurrences by analyzing event databases, targeting to systematizing events’ analysis and surpassing the need for idiographic approaches.
A process-oriented point of view is enabled by purposeful data transformations, and higher-order dependencies are discovered and exploited to capture the flows among the events.
Political events do not follow a linear movement that is implied by a sequence, but they occur in varying patterns that cannot be reflected accurately when assuming only first-order dependencies.
The methodology suggests a novel way to look and to analyze raw event data and it offers an accessible, practicable and supplementary tool as it does not disturb any of the established relevant research designs, and it does not require any additional data to be applied.
We would like to thank our graduate students Zafeiris Papavarytis and Christiana Pantermali, who spent many hours in checking every event of the original data set for relevance, and who manually filtered them out.
Delias, P. and Kazanidis, I. (2020), "Exploiting higher-order dependencies for process analytics: The case for political events’ analysis", Kybernetes, Vol. 49 No. 4, pp. 1253-1266. https://doi.org/10.1108/K-09-2018-0500
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