Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

A systematic investigation of risk management and process mining ontologies

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.This study proposes and examines the ‘’Risk – Process’’ ontology with respect to and in comparison with the Process mining methodology. The ontology consists of Process elements (Process Mining, Business Process Management and Business Process Intelligence) and Risk elements (Governance, Risk Management & Compliance, Internal Audit and Enterprise Risk Management). A two-fold literature review is executed, focusing firstly on the six key elements of the ‘’Risk - Process’’ ontology, and secondly at the “Risk” components of the ontology. Moving on, as an original contribution, the popularity and the coherence of the aforementioned elements in internet searches from 2004 to 2018 is presented and forecasted with the use of the Google Trends tool. As a last step, a statistical analysis of the time series obtained through Google Trends is performed, in order to find relation, correlations, statistical significance and predictors with respect to Process minin

Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company