Jan Mendling

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Seven process modeling guidelines (7PMG)

Business process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research. A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have. Existing approaches towards model quality might be of benefit, but they suffer from at least one of the following problems. On the one hand, frameworks like SEQUAL and the Guidelines of Modeling are too abstract to be applicable for novices and non-experts in practice. On the other hand, there are collections of pragmatic hints that lack a sound research foundation. In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand. As a synthesis we propose a set of seven process modeling guidelines (7PMG). Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners. Furthermore, we analyze how the guidelines are prioritized by industry experts. In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice.

What makes process models understandable

Despite that formal and informal quality aspects are of significantimportance to business process modeling, there is only little empiricalwork reported on process model quality and its impact factors. Inthis paper we investigate understandability as a proxy for quality of processmodels and focus on its relations with personal and model characteristics.We used a questionnaire in classes at three European universitiesand generated several novel hypotheses from an exploratory data analysis.Furthermore, we interviewed practitioners to validate our findings.The results reveal that participants tend to exaggerate the differences inmodel understandability, that self-assessment of modeling competenceappears to be invalid, and that the number of arcs in models has animportant influence on understandability.

Measuring Similarity between Business Process Models

Quality aspects become increasingly important when business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding of how a degree of behavioral similarity can be defined. In this paper we address this challenge in a novel way. We use causal footprintsas an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs. Based on the causal footprint derived from two models we calculate their similarity based on the established vector space model from information retrieval. We validate this concept with an experiment using the SAP Reference Model and an implementation in the ProM framework.

From business process models to process-oriented software systems

Several methods for enterprise systems analysis rely on flow-oriented representations of business operations, otherwise known as business process models. The Business Process Modeling Notation (BPMN) is a standard for capturing such models. BPMN models facilitate communication between domain experts and analysts and provide input to software development projects. Meanwhile, there is an emergence of methods for enterprise software development that rely on detailed process definitions that are executed by process engines. These process definitions refine their counterpart BPMN models by introducing data manipulation, application binding, and other implementation details. The de facto standard for defining executable processes is the Business Process Execution Language (BPEL). Accordingly, a standards-based method for developing process-oriented systems is to start with BPMN models and to translate these models into BPEL definitions for subsequent refinement. However, instrumenting this method is challenging because BPMN models and BPEL definitions are structurally very different. Existing techniques for translating BPMN to BPEL only work for limited classes of BPMN models. This article proposes a translation technique that does not impose structural restrictions on the source BPMN model. At the same time, the technique emphasizes the generation of readable (block-structured) BPEL code. An empirical evaluation conducted over a large collection of process models shows that the resulting BPEL definitions are largely block-structured. Beyond its direct relevance in the context of BPMN and BPEL, the technique presented in this article addresses issues that arise when translating from graph-oriented to block-structure flow definition languages.

Activity labeling in process modeling: Empirical insights and recommendations

Few studies have investigated the factors contributing to the successful practice of process modeling. In particular, studies that contribute to the act of developing process models that facilitate communication and understanding are scarce. Although the value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels, there has been hardly any work on the quality of these labels. Accordingly, the research presented in this paper examines activity labeling practices in process modeling. Based on empirical data from process modeling practice, we identify and discuss different labeling styles and their use in process modeling praxis. We perform a grammatical analysis of these styles and use data from an experiment with process modelers to examine a range of hypotheses about the usability of the different styles. Based on our findings, we suggest specific programs of research towards better tool support for labeling practices. Our work contributes to the emerging stream of research investigating the practice of process modeling and thereby contributes to the overall body of knowledge about conceptual modeling quality.

A Study Into the Factors That Influence the Understandability of Business Process Models

Business process models are key artifacts in the development of information systems. While one of their main purposes is to facilitate communication among stakeholders, little is known about the factors that influence their comprehension by human agents. On the basis of a sound theoretical foundation, this paper presents a study into these factors. Specifically, the effects of both personal and model factors are investigated. Using a questionnaire, students from three different universities evaluated a set of realistic process models. Our findings are that both types of investigated factors affect model understanding, while personal factors seem to be the more important of the two. The results have been validated in a replication that involves professional modelers.

A discourse on complexity of process models

Complexity has undesirable effects on, among others, the correctness, maintainability, and understandability of business process models. Yet, measuring complexity of business process models is a rather new area of research with only a small number of contributions. In this paper, we survey findings from neighboring disciplines on how complexity can be measured. In particular, we gather insight from software engineering, cognitive science, and graph theory, and discuss in how far analogous metrics can be defined on business process models.

Detection and prediction of errors in EPCs of the SAP reference model

Up to now there is neither data available on how many errors can be expected in process model collections, nor is it understood why errors are introduced. In this article, we provide empirical evidence for these questions based on the SAP reference model. This model collection contains about 600 process models expressed as Event-driven Process Chains (EPCs). We translated these EPCs into YAWL models, and analyzed them using the verification tool WofYAWL. We discovered that at least 34 of these EPCs contain errors. Moreover, we used logistic regression to show that complexity of EPCs has a significant impact on error probability.

Configurable multi-perspective business process models

A configurable process model provides a consolidated view of a family of business processes. It promotes the reuse of proven practices by providing analysts with a generic modeling artifact from which to derive individual process models. Unfortunately, the scope of existing notations for configurable process modeling is restricted, thus hindering their applicability. Specifically, these notations focus on capturing tasks and control-flow dependencies, neglecting equally important ingredients of business processes such as data and resources. This research fills this gap by proposing a configurable process modeling notation incorporating features for capturing resources, data and physical objects involved in the performance of tasks. The proposal has been implemented in a toolset that assists analysts during the configuration phase and guarantees the correctness of the resulting process models. The approach has been validated by means of a case study from the film industry.