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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.

The purpose of this paper is to manifest a method that exploits process analytics to discover critical knowledge for a business process. This knowledge eventually answers to the question if process behavior can suggest which activities should be outsourced to get the performance improved.

The author linked waste sources to process behavioral patterns, and adopted the positive deviance paradigm to highlight compelling behaviors. Various analytic tools (generalized regression, clustering, etc.) were used to provide recommendations.

By outsourcing small parts of the process, significant process improvement is expected. Evidence-based process analytics can effectively support the relevant decisions.

The author had no access to the relevant policy makers (process owners).

The author proposed an operationalization of concepts that connects process behavior to waste sources. The author presented the use of positive deviance as a guide for waste elimination projects.