Generating Event Logs for High-Level Process Models
Business Process Model and Notation (BPMN) is a de-facto standard for practitioners working in the Business Process Management (BPM) field. The BPMN standard  offers high-level modeling constructs, such as subprocesses, events, data and message flows, lanes, and is widely used to model processes in various domains. Recently several BPMN-based process mining techniques [2, 3, 4] were introduced. These techniques allow representing processes, discovered from the event logs of process-aware information systems, in a convenient way, using the BPMN standard. To test these mining approaches an appropriate tool for the generation of event logs from BPMN models is needed. In this work we suggest such a tool. We propose a formal token-based executable BPMN semantics, which takes into account BPMN 2.0 with its expressive constructs. The developed tool is based on these semantics and allows simulation of hierarchical process models (including models with cancellations), models with data flows and pools, and models interacting through message flows. To manage the control flow, script-based gateways and choice preferences are implemented as well. The proposed simulation technique was implemented on top of existing plug-ins for ProM (Process Mining Framework) , and was verified on models created by practitioners from various domains.
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.
Monitoring and analyzing the operation of enterprises is a key capability of Governance, Risk, and Compliance (GRC) solutions and is relevant for high-risk organizations, such as financial services. The potential of state-of-the-art process mining (data-driven process analysis) is limited by quality issues with transactional data registration and extraction. A novel approach is proposed to address these challenges: the Enterprise Operational Analysis (EOA) founded in DEMO and the Enterprise Operating System (EOS). The EOS is a software system based on enterprise engineering, and stores, interprets, and executes DEMO models as native source code. The EOS provides workflow-like capabilities and supports EOA. Combining the EOS with state-of-the-art process mining offers the following advantages: guaranteed completeness of analysis, elimination of ‘mining’ for events, facilitating process conformance checking, analysis on various levels of granularity from various perspectives. It enables enterprises to systematically analyze, improve and deploy business procedures. A professional business case is analyzed. © Springer International Publishing Switzerland 2015.
The issue contains papers accepted for presentation at the 10th Spring/Summer Young Researchers’ Colloquium on Software Engineering (SYRCoSE 2016) held in Krasnovidovo, Mozhaysky District, Moscow Oblast, Russia on May 30-June 1, 2016. The paper selection was based on originality and contributions to the field. Each paper was peer-reviewed by at least three referees.
The colloquium’s topics include programming languages, software development tools, embedded and cyber-physical systems, software and hardware verification, formal methods, information security, and others.
Comparing business process models is one of the most significant challenges for business and systems analysts. The complexity of the problem is explained by the fact there is a lack of tools that can be used for comparing business process models. Also there is no universally accepted standard for modeling them. EPC, YAWL, BPEL, XPDL and BPMN are only a small fraction of available notations that have found acceptance among developers. Every process modeling standard has its advantages and disadvantages, but almost all of them comprise an XML schema, which defines process serialization rules. Due to the fact that XML naturally represents hierarchical and reference structure of business process models, these models can be compared using their XML representations. In this paper we propose a generic comparison approach, which is applicable to XML representations of business process models. Using this approach we have developed a tool, which currently supports BPMN 2.0  (one of the most popular business process modeling notations), but can be extended to support other business process modeling standards.
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.
Information systems in different domains, such as healthcare, tourism, banking, government and others, record operational behavior in the form of event logs. The process mining discipline offers dozens of techniques to discover, analyze, and visualize processes running in information systems, based on their event logs. The representational bias (the language for processes representation) plays an important role in the process discovery. In this work BPMN (Business Process Model and Notation) language was chosen as a representational bias and as a starting point for the process discovery, analysis and enhancement. BPMN is a common process modeling language, widely used by consultants, managers, analysts, and software engineers in various application domains. This work aims to bridge the gap between process mining techniques and BPMN. Existing techniques are often limited to a single perspective, e.g., just the control flow, subprocesses, or just resources. The goal of this work is to fully support the BPMN specification in the context of process mining and suggest a unified and integrated approach allowing for the discovery, analysis and enhancement of hierarchical high-level BPMN models. The approach proposed in this thesis is supported by tools that enable users to analyze discovered processes in BPMN-compliant tools and even automate their executions, using existing BPMN engines.
The ICPM Doctoral consortium was co-located with the first International Conference on Process Mining ICPM 2019, held in Aachen, Germany. As this was the first time the conference was organized, it was also the first co-located doctoral consortium. In total seven manuscripts were received and evaluated by the Jury consisting of the following senior members of the process mining research community. All seven proposals received feedback from the Jury and six proposals were selected to be discussed at the doctoral consortium and on Sunday June 23 2019, the six PhD researchers gathered to discuss their research proposals with the process mining community. The community was represented by a large number of attendees. Over 30 researchers helped in the discussion.
Companies from various domains record their operational behavior in a form of event logs. These event logs can be analyzed and relevant process models representing the real companies’ behavior can be discovered. One of the main advantages of the process discovery methods is that they commonly produce models in a form of graphs which can be easily visualized giving an intuitive view of the executed processes. Moreover, the graph-based representation opens new challenging perspectives for the application of graph comparison methods to find and explicitly visualize differences between discovered process models (representing real behavior) and reference process models (representing expected behavior). Another important area where graph comparison algorithms can be used is the recognition of process modeling patterns. Unfortunately, exact graph comparison algorithms are computationally expensive. In this paper, we adapt an inexact tabu search algorithm to find differences between BPMN (Business Process Model and Notation) models. The tabu search and greedy algorithms were implemented within the BPMNDiffViz tool and were tested on BPMN models discovered from synthetic and real-life event logs. It was experimentally shown that inexact tabu search algorithm allows to find a solution which is close to the optimal in most of the cases. At the same, its computational complexity is significantly lower than the complexity of the exact A search algorithm investigated earlier.
Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. In this paper we consider the problem of models and event logs conformance checking. Conformance checking is intensively studied in the frame of process mining research, but only models and event logs of the same granularity were considered in the literature. Here we present and justify the method of checking conformance between a high-level model (e.g. built by an expert) and a low-level log (generated by a system).