Quantifying the Re-identification Risk of Event Logs for Process Mining

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.Comment: Accepted to CAiSE-202

A Cross-Organizational Process Mining Framework for Obtaining Insights from Software Products: Accurate Comparison Challenges

Software vendors offer various software products to large numbers of enterprises to support their organization, in particular Enterprise Resource Planning (ERP) software. Each of these enterprises use the same product for similar goals, albeit with different processes and configurations. Therefore, software vendors want to obtain insights into how the enterprises use the software product, what the differences are in usage between enterprises, and the reasons behind these differences. Cross-organizational process mining is a possible solution to address these needs, as it aims at comparing enterprises based on their usage. In this paper, we present a novel Cross-Organizational Process Mining Framework which takes as input, besides event log, semantics (meaning of terms in an enterprise) and organizational context (characteristics of an enterprise). The framework provides reasoning capabilities to determine what to compare and how. Besides, the framework enables one to create a catalog of metrics by deducing diagnostics from the usage. By using this catalog, the framework can monitor the (positive) effects of changes on processes. An enterprise operating in a similar context might also benefit from the same changes. To accommodate these improvement suggestions, the framework creates an improvement catalog of observed changes. Later, we provide a set of challenges which have to be met in order to obtain the inputs from current products to show the feasibility of the framework. Next to this, we provide preliminary results showing they can be met and illustrate an example application of the framework in cooperation with an ERP software vendor

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included