Discovering, analyzing and enhancing BPMN models using ProM

Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners

Analysing Event Data through Process Mining

Most organizations create business processes, which are sometimes difficult to control and comprehend. Understanding these processes is however an absolute prerequisite prior to taking on any improvement initiative. Process mining provides a new perspective that makes easier and faster to get a complete and objective picture of business processes to better control and continuously improve them, by reducing their costs, production time and risks. This is made possible by analysing vast quantities of event data available in today’s information systems. Mainly, which activities are performed, when, and by whom. In that sense, process mining sits between computational intelligence and data mining on the one hand, and business process management on the other hand. The reference framework for process mining focuses on: (i) conceptual models describing processes, organizational structures, and the corresponding relevant data; and (ii) the real execution of processes, as reflected by the footprint of reality logged and stored by the information systems in use within an enterprise. For process mining to be applicable, such information has to be structured in the form of explicit event logs. In fact, all process mining techniques assume that it is possible to record the sequencing of relevant events occurred within an enterprise, such that each event refers to an activity (i.e., a well-defined step in some process) and is related to a particular case. Through process mining, decision makers can discover process models from event logs (process discovery), compare expected and actual behaviors (conformance checking), and enrich models with key information about their actual execution (process enhancement). This, in turn, provides the basis to understand, maintain, and enhance processes based on reality. In this tutorial, we introduce the process mining framework, the main process mining techniques and tools, and the different phases of event data analysis through process mining, discussing the various ways data and process analysts can make use of the mined models. Finally, we discuss common pitfalls and critical issues, and give suggestions on how to mitigate them

From zero to hero: A process mining tutorial

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development

A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft

The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges