Significant stochastic dependencies in process models

Process mining uses event data recorded by information systems to reveal the actual execution of business processes in organizations. By doing this, event logs can expose sensitive information that may be attributed back to individuals (e.g., reveal information on the performance of individual employees). Due to GDPR organizations are obliged to consider privacy throughout the complete development process, which also applies to the design of process mining systems. The aim of this paper is to develop a privacy-preserving system design for process mining. The user-centered view on the system design allows to track who does what, when, why, where and how with personal data. The approach is demonstrated on an IoT manufacturing use case.

Process mining is an emerging discipline incorporating methods and tools for the analysis of system/process executions captured in the form of event logs [1]. Traditionally process mining can be divided into three research areas: discovery (construction of process models from event logs), conformance checking (finding log and model deviations), and enhancement of existing process models with additional event log data.BPMN (Business Process Model and Notation) 2.0 [2] is a widely used process modeling notation, supported by various process modeling and analysis tools, and is a de-facto process modeling standard. Using BPMN within process mining opens perspectives for applicability of the existing process mining techniques: for instance discovered process models can be analyzed or enacted using existing BPMN-based software, and vice versa, manually created models can be imported to a process mining tool, verified against event logs, and enhanced with additional data.In this work we bridge the gap between conventional process modeling formalisms used in the context of process mining (e.g., Petri nets, causal nets, process trees) and BPMN. For that purpose we developed a suite of conversion algorithms and provide formal guarantees relating the behavior of Petri nets (including non-free-choice nets) to the corresponding BPMN models (and vice versa). The derived relations are used to enhance the BPMN models with information learned from the event logs.The developed conversion techniques are described in detail in [3] and have been implemented [4] as a part of ProM (Process Mining Framework) [5] -- an open source tool for process mining and verified on real event log data. Moreover, cases for which conversion algorithms give more compact process models in comparison with the initial models are identified. Although the developed algorithms deal with basic control flow constructs only, they can be applied in the discovery of advanced BPMN modeling elements [2], including subprocesses [6-7], cancellations [8], conditional branching and data objects, swimlanes, message flows, and others.

To solve the problem of modeling cross-organizational business process, a method of mining the cross-organizational businessprocess model from event logs is proposed. The method describes the different aspects of the cross-organizational business processfrom three perspectives: internal perspective, public perspectiveand collaborative perspective, so as to gain the intra-organization-al process model, the inter-organizational public process model, the collaborative process model in cross-organizational collaborat-ion business. The intra-organizational process model formed bymining, analyzing and sorting the running log of the organization involved in cross-organizational collaboration. The inter-organiza-tional process model is obtained by simplifying the intra-organ-izational process model, and finally the cooperative process modelcan be modelled. Compared with the existing modeling methods, this method can protect the details of the internal business proc-ess, and provides the public process model to public only, which can effectively support the individual characteristics of thecross- organizational business process.

Historical perspective

Implementation of enterprise resource planning system to support business process in organization has increased rapidly. Higher education institution is one of organizations which can use enterprise resource planning system to optimize their business process. Universitas Indonesia (UI) is one of the prominent universities in Indonesia who uses enterprise resource planning system for their financial business processes, but the adoption process has not been completed until now. This happened because there are many factors that hinder enterprise resource planning system adoption at the UI. This study combines Technology, Organization, Environment framework and Iacovou model as a guideline to analyze challenges and supporting dimension in adopting enterprise resource planning system in UI. Our research found that challenges are the fact that UI usually developed their own system, lack of communication process in organization, no regulation from government about enterprise resource planning implementation in higher education, and the perceived benefits are not satisfied employees yet. Supporting factors are the existance of good information technology infrastructure, top management support, various enterprise resource planning vendor, and the benefits is slowly felt by employee.

Enterprise systems (ES) are industry-specific, customizable software packages that integrate information and business process in organizations. Despite the advantages associated with implementing ES, their success has been mixed. This has attracted the interest of researchers and resulted in a proliferation of literature on implementation. However, the extant ES studies lack a theoretical framework for the examination of ES use and its implications for change in organizations. We seek to bridge this research gap by proposing a theoretical framework for change induced through ES use. This paper contributes to theory in two areas. First, by analytically separating the notions of structure, institution, and organization, we illustrate a consistent application of Giddens' structuration theory to ES use. Second, we develop a structurational model of ES-induced change that explicates the relationship between specific characteristics of ES and the nature of change occasioned through their use by human agents within a historically shaped organizational context. Towards this objective, we distinguish ES from other information systems. Implications for practitioners and researchers are presented.

Process mining embeddings: Learning vector representations for Petri nets

The lack of effective controls over organizational business processes can cause serious consequences for a company's reputation and even jeopardize its existence. There is a need for continuous monitoring of controls and systematic collection and evaluation of relevant data. Compliance management is essential for ensuring that organizational business processes and supporting information system are in compliance with laws, regulations, and various legislative or technical documents pertaining to the place of business. The focus of this chapter is to provide an insight into compliance management and discuss the integration and automation of compliance management in service-driven computing. The chapter elaborates conceptual models for specifying compliance requirements originating from various sources and details aspects such as multi-view process modeling annotated with compliance requirements, annotation of service interfaces and behavioral characteristics, development and reuse of compliant process fragments, architectural patterns to simplify compliance management, and abstract frameworks to ensure compliance in the context of service-driven computing through service adaptation and runtime governance. Finally, approaches to automating compliance management through formalization of compliance requirements, rule- and event-based monitoring, and integration of compliance governance systems with automated reasoning and verification tools are detailed.

Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.

A marked Petri net is lucent if there are no two different reachable markings enabling the same set of transitions, i.e., states are fully characterized by the transitions they enable. This paper explores the class of marked Petri nets that are lucent and proves that perpetual marked free-choice nets are lucent. Perpetual free-choice nets are free-choice Petri nets that are live and bounded and have a home cluster, i.e., there is a cluster such that from any reachable state there is a reachable state marking the places of this cluster. A home cluster in a perpetual net serves as a “regeneration point” of the process, e.g., to start a new process instance (case, job, cycle, etc.). Many “well-behaved” process models fall into this class. For example, the class of short-circuited sound workflow nets is perpetual. Also, the class of processes satisfying the conditions of the \(\alpha \) algorithm for process discovery falls into this category. This paper shows that the states in a perpetual marked free-choice net are fully characterized by the transitions they enable, i.e., these process models are lucent. Having a one-to-one correspondence between the actions that can happen and the state of the process, is valuable in a variety of application domains. The full characterization of markings in terms of enabled transitions makes perpetual free-choice nets interesting for workflow analysis and process mining. In fact, we anticipate new verification, process discovery, and conformance checking techniques for the subclasses identified.

Enabling flexible location-aware business process modeling and execution

Process modelling is often used in connection to the introduction of standardised information systems in traditional organisational forms. Existing work on success measures of process modelling has summarised important aspects In this setting.In this paper we present an investigation into process modelling success in connection to harmonising the work across units that has traditionally been to a large extent autonomous. We present a pilot case from a global service-providing enterprise, from which we have tried to extract the value of process modelling in a networked organisation, comparing to existing frameworks of process modelling success. Although many of our findings are supported by existing frameworks, we have found a need to combine and extend these to better grasp the value of process modelling in connection to harmonising more loosely integrated organizational forms.KeywordsWork ProcessSemantic QualityVirtual EnterpriseBusiness AreaEnterprise ModellingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs.KeywordsProcess MiningPetri NetsGenetic AlgorithmsProcess DiscoveryBusiness Process IntelligenceBusiness Activity Monitoring

As organizations change, their information systems can evolve from simple systems to complex systems, which are hard to understand, and therefore hard to maintain or extend. Process mining can help organizations in trying to understand the information systems by analyzing the system.In this paper we propose a methodology to perform process diagnostics, based on process mining. Given an event log of an information system within an organization, process diagnostics gives a broad overview of the organization's process(es) within a short period of time. In the process diagnostics methodology, several perspectives of the process are highlighted. The outcome covers the control flow perspective, the performance perspective and the organizational perspective. We used the methodology on a case study for a Dutch governmental organization.

Process mining techniques have matured over the last decade and more and more organization started to use this new technology. The two most important types of process mining are process discovery (i.e., learning a process model from example behavior recorded in an event log) and conformance checking (i.e., comparing modeled behavior with observed behavior). Process mining is motivated by the availability of event data. However, as event logs become larger (say terabytes), performance becomes a concern. The only way to handle larger applications while ensuring acceptable response times, is to distribute analysis over a network of computers (e.g., multicore systems, grids, and clouds). This paper provides an overview of the different ways in which process mining problems can be distributed. We identify three types of distribution: replication, a horizontal partitioning of the event log, and a vertical partitioning of the event log. These types are discussed in the context of both procedural (e.g., Petri nets) and declarative process models. Most challenging is the horizontal partitioning of event logs in the context of procedural models. Therefore, a new approach to decompose Petri nets and associated event logs is presented. This approach illustrates that process mining problems can be distributed in various ways.