Process mining is a prominent discipline in business process management. It collects a variety of techniques for gathering information from event logs, each fulfilling a different mining purpose. Event logs are always necessary for assessing and validating mining techniques in relation to specific purposes. Unfortunately, event logs are hard to find and usually contain noise that can influence the validity of the results of a mining technique. In this paper, we propose a framework, named purple , for generating, through business model simulation, event logs tailored for different mining purposes, i.e., discovery, what-if analysis, and conformance checking. It supports the simulation of models specified in different languages, by projecting their execution onto a common behavioral model, i.e., a labeled transition system. We present eleven instantiations of the framework implemented in a software tool by-product of this paper. The framework is validated against reference log generators through experiments on the purposes presented in the paper.

Abstract Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Abstract Process mining encompasses a range of methods designed to analyze event logs. Among these methods, control-flow discovery algorithms are particularly significant, as they enable the identification of real-world process models, known as in-vivo processes, in contrast to anticipated models. An obstacle faced by control-flow discovery algorithms is their limited ability to recognize duplicated activities, which are activities that occur in multiple locations within a process. This issue is particularly relevant in the healthcare sector, where numerous instances of duplicated activities exist in processes but remain undetected by conventional algorithms. This article introduces a novel concept for a control-flow discovery algorithm capable of effectively revealing duplicated activities. The effectiveness of this technique is demonstrated through experimentation on a synthetic dataset. Moreover, the algorithm has been implemented and its source code is available as open-source software, accessible both as a ProM plugin and a Java Maven dependency.

Process automation is used to increase the performance of processes. One of the leading process automation tools is Microsoft Process Advisor. This tool requires users to select the corresponding connectors for the automation of different tasks, which can be a challenging endeavor for users who have limited business knowledge as there are various connectors and templates exist. To overcome this challenge, we present a process-aware recommender system for connectors that eases the labeling task for end users. The results of applying this method to real event logs indicate that it can recommend relevant connectors and, therefore, the usage of the same mechanism might be generalized to broader contexts.

The creation of a process model is a process consisting of five distinct phases, i.e., problem understanding, method finding, modeling, reconciliation, and validation. To enable a fine-grained analysis of process model creation based on phases or the development of phase-specific modeling support, an automatic approach to detect phases is needed. While approaches exist to automatically detect modeling and reconciliation phases based on user interactions, the detection of phases without user interactions (i.e., problem understanding, method finding, and validation) is still a problem. Exploiting a combination of user interactions and eye tracking data, this paper presents a two-step approach that is able to automatically detect the sequence of phases a modeler is engaged in during model creation. The evaluation of our approach shows promising results both in terms of quality as well as computation time demonstrating its feasibility.

In community detection, the theme of correctly identifying overlapping nodes, i.e. nodes which belong to more than one community, is important as it is related to role detection and to the improvement of the quality of clustering: proper detection of overlapping nodes gives a better understanding of the community structure. In this paper, we introduce a novel measure, called cuttability, that we show being useful for reliable detection of overlaps among communities and for improving the quality of the clustering, measured via modularity. The proposed algorithm shows better behaviour than existing techniques on the considered datasets (IRC logs and Enron e-mail log). The best behaviour is caught when a network is split between micro-communities. In that case, the algorithm manages to get a better description of the community structure.

Control flow discovery algorithms are able to reconstruct the workflow of a business process from a log of performed activities. These algorithms, however, do not pay attention to the reconstruction of roles, i.e. they do not group activities according to the skills required to perform them. Information about roles in business processes is commonly considered important and explicitly integrated into the process representation, e.g. as swimlanes in BPMN diagrams. This work proposes an approach to enhance a business process model with information on roles. Specifically, the identification of roles is based on the detection of handover of roles. On the basis of candidates for roles handover, the set of activities is first partitioned and then subsets of activities which are performed by the same originators are merged, so to obtain roles. All significant partitions of activities are automatically generated. Experimental results on several logs show that the set of generated roles is not too large and it always contains the correct definition of roles. We also propose an entropy based measure to rank the candidate roles which returns promising experimental results.

Process mining is a relatively new field of research: its final aim is to bridge the gap between data mining and business process modelling. In particular, the assumption underpinning this discipline is the availability of data coming from business process executions. In business process theory, once the process has been defined, it is possible to have a number of instances of the process running at the same time. Usually, the identification of different instances is referred to a specific “case id” field in the log exploited by process mining techniques. The software systems that support the execution of a business process, however, often do not record explicitly such information. This paper presents an approach that faces the absence of the “case id” information: we have a set of extra fields, decorating each single activity log, that are known to carry the information on the process instance. A framework is addressed, based on simple relational algebra notions, to extract the most promising case ids from the extra fields. The work is a generalization of a real business case.