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An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in and implied by the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large and complex event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out however that existing divideand-conquer process discovery approaches often produce less accurate models than flat discovery techniques when applied to real-life event logs. This article investigates the hypothesis that the weaknesses of existing divide-andconquer approaches lies in the way stages are identified. The article contributes a technique to identify stages from an event log based on a modularity measure as well as a technique for discovering process models based on a given stage decomposition. An experimental evaluation shows that the proposed stage identification technique finds stages that are closer to those identified by human experts, while the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and model complexity.

Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities, and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines.

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark covering twelve publicly-available real-life event logs and eight quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of several methods and a strong divergence in their performance with respect to the different quality metrics used.

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Context - Healthcare processes such as patient flows in the emergency department, are notoriously complex because of their high degree of variability. Yet, hospital stakeholders need to understand differences between variants of these processes, e.g. between different patient cohorts or different times of the day, to offer a consistent and reliable service to their patients. Objective - Analysis of these processes through classic process mining techniques is hampered by the inherently high variability of these processes. Method - This paper contributes two novel visualisation and animation techniques, specifically catered for highly-varied business processes, which can distill both static and dynamic differences between variants of the same business process, using their execution logs. Results - We validate these techniques via two case studies with two Australian hospitals and obtained insights on the differences that underpin patient flows for various patient cohorts through the Emergency Department. Conclusion - The Configurable Model Visualisation technique provides a birds-eye view of similarities and differences be- tween process variants while the Log Animation tool addresses behavioural comparison and is unique in its ability to display concurrently the processing of multiple process variants through a single BMPN model.

Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over-generalization. This paper proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.

Predictive business process monitoring is a family of techniques to determine how running instances of a business process are likely to unfold in the future. Techniques in this space differ according to their object of prediction. Some predict whether or not a running process instance will fulfill a compliance rule, others predict whether or not a given activity will occur, while others predict the remaining execution time. These and other predictive process monitoring problems are subsumed by the problem of predicting the remaining sequence of activities of a given process instance. In this paper, we tackle this latter problem using two alternative approaches. In the first one, we statically construct a transition system from an event log of completed process instances and annotate each transition with a probability calculated using a k-nearest neighbors classifier. At runtime, we map the (incomplete) trace of a process instance to a state in the transition system. To predict the remaining activity sequence of a process instance, we calculate a highest-probability path starting from the current state. In the second approach, we treat the problem of activity sequence prediction as a structured output prediction problem and apply recurrent neural networks. The accuracy of the two proposed approaches is evaluated on real-life and synthetic datasets and compared against an existing baseline technique.

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

The management of risks in business processes has been a subject of active research in the past few years. Many benefits can potentially be obtained by integrating the two traditionally-separated fields of risk management and business process management, including the ability to minimize risks in business processes (by design) and to mitigate risks at run time. In the past few years, an increasing amount of research aimed at delivering such an integrated system has been proposed. However, these research efforts vary in terms of their scope, goals, and functionality. Through systematic collection and evaluation of relevant literature, this paper compares and classifies current approaches in the area of risk-aware business process management in order to identify and explain relevant research gaps. The process through which relevant literature is collected, filtered, and evaluated is also detailed.

Encompasses the whole BPM lifecycle, including process identification, modelling, analysis, redesign, automation and monitoring Class-tested textbook complemented with additional teaching material on the accompanying website Covers both relevant conceptual background, industrial standards and actionable skills Business Process Management (BPM) is the art and science of how work should be performed in an organization in order to ensure consistent outputs and to take advantage of improvement opportunities, e.g. reducing costs, execution times or error rates. Importantly, BPM is not about improving the way individual activities are performed, but rather about managing entire chains of events, activities and decisions that ultimately produce added value for an organization and its customers. This textbook encompasses the entire BPM lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 100 hands-on exercises – many with solutions – as well as numerous suggestions for further reading. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website fundamentals-of-bpm.org.

This month, Jan Recker turns his attention to the technological side of BPM research and education. He engaged in a collaboration with two colleagues at Queensland University, Dr Marcello La Rosa and Eike Bernhard, on an initiative on the development of an advanced BPM technology - an Advanced Process Model Repository called Apromore. In this Column, they use the example of Apromore to showcase how BPM technologies are conceived, designed, developed and applied.

As business process management technology matures, organisations acquire more and more business process models. The resulting collections can consist of hundreds, even thousands of models and their management poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks. This query language is independent of the particular process modelling notation used, but we will demonstrate how it can be used in the context of Petri nets by showing how the semantic relationships can be determined for these nets in such a way that state space explosion is avoided as much as possible. An experiment with three large process model repositories shows that queries expressed in our language can be evaluated efficiently.

As the Service-oriented architecture paradigm has become ever more popular, different standardization efforts have been proposed by various consortia to enable interaction among heterogeneous environments through this paradigm. This chapter will overview the most prevalent of these SOA Efforts. It will first show how technical services can be described, how they can interact with each other and be discovered by users. Next, the chapter will present different standards to facilitate service composition and to design service-oriented environments in light of a universal understanding of service orientation. The chapter will conclude with a summary and a discussion on the limitations of the reviewed standards along their ability to describe service properties. This paves the way to the next chapters where the USDL standard will be presented, which aim to lift such limitations.

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

As a result of the growing adoption of Business Process Management (BPM) technology different stakeholders need to understand and agree upon the process models that are used to configure BPM systems. However, BPM users have problems dealing with the complexity of such models. Therefore, the challenge is to improve the comprehension of process models. While a substantial amount of literature is devoted to this topic, there is no overview of the various mechanisms that exist to deal with managing complexity in (large) process models. It is thus hard to obtain comparative insight into the degree of support offered for various complexity reducing mechanisms by state-of-the-art languages and tools. This paper focuses on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e. the structure of a process model. These mechanisms are captured as patterns, so that they can be described in their most general form and in a language- and tool-independent manner. The paper concludes with a comparative overview of the degree of support for these patterns offered by state-of-the-art languages and language implementations.

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short.

In recent years several scientific Workflow Management Systems (WfMSs) have been developed with the aim to automate large scale scientific experiments. As yet, many offerings have been developed, but none of them has been promoted as an accepted standard. In this paper we propose a pattern-based evaluation of three among the most widely used scientific WfMSs: Kepler, Taverna and Triana. The aim is to compare them with traditional business WfMSs, emphasizing the strengths and deficiencies of both systems. Moreover, a set of new patterns is defined from the analysis of the three considered systems.

Vendors provide reference process models as consolidated, off-the-shelf solutions to capture best practices in a given industry domain. Customers can then adapt these models to suit their specific requirements. Traditional process flexibility approaches facilitate this operation, but do not fully address it as they do not sufficiently take controlled change guided by vendors’ reference models into account. This tension between the customer’s freedom of adapting reference models, and the ability to incorporate with relatively low effort vendor-initiated reference model changes, thus needs to be carefully balanced. This paper introduces process extensibility as a new paradigm for customising reference processes and managing their evolution over time. Process extensibility mandates a clear recognition of the different responsibilities and interests of reference model vendors and consumers, and is concerned with keeping the effort of customer-side reference model adaptations low while allowing sufficient room for model change., Enterprise Modelling and Information Systems Architectures, Vol 5, No 3 (2010)

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective than developing them from scratch. As process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. Experiments are conducted to demonstrate that our proposal achieves a significant reduction in query evaluation time.

The YAWL system is structured as a service-oriented architecture. It is composed of an extensible set of YAWL Services [1], each of which is deployed at a certain endpoint and offers one or multiple interfaces. Some of these services are userfacing, meaning that they offer interfaces to end users, while others offer interfaces to applications or other services.

This chapter describes how the YAWL meta-model was extended to support the definition of variation points. These variation points can be used to describe different variants of a YAWL process model in a unified, configurable model. The model can then be configured to suit the needs of specific settings, e.g. for a new organization of project.

This appendix describes the Order Fulfillment process followed by a fictitious company named Genko Oil. The process is freely inspired by the VICS (Voluntary Inter-industry Commerce Solutions) reference model1 and provides a demonstration of YAWL’s capabilities in modelling complex control-flow, data and resourcing requirements.

A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions.} In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the ``operating guidelines'' used for partner synthesis. We view the configuration process as an external service, and compute a characterization of all such services which meet particular requirements using the notion of configuration guideline. As a result, we can characterize all feasible configurations (i.\,e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model.

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with process model complexity in order to improve the understanding of a process model by stakeholders and process analysts. Features for dealing with this complexity can be classified in two categories: 1) those that are solely concerned with the appearance of the model, and 2) those that in essence change the structure of the model. In this paper we focus on the former category and present a collection of patterns that generalize and conceptualize various existing features. The paper concludes with a detailed analysis of the degree of support of a number of state-of-the-art languages and language implementations for these patterns.

Configurable process models integrate different variants of a business process into a single model. Through configuration users of such models can then combine the variants to derive a process model optimally fitting their individual needs. While techniques for such models were suggested in previous research, this paper presents a case study in which these techniques were extensively tested on a real-world scenario. We gathered information from four Dutch municipalities on registration processes executed on a daily basis. For each process we identified variations among municipalities and integrated them into a single, configurable process model, which can be executed in the YAWL workflow environment. We then evaluated the approach through interviews with organizations that support municipalities in organizing and executing their processes. The paper reports on both the feedback of the interviewed partners and our own observations during the model creation.

Configurable process models enable a systematic documentation and reuse of standardized "best" practices, while allowing process analysts to understand possible variations contemplated by these standards, and to link these variations to business decisions. This article discusses the potential benefits of configurable process models and introduces a method and a toolset for process design based on configurable process models.

Workflow modelling languages allow for the specification of executable business processes. They, however, do typically not provide any guidance for the adaptation of workflow models, i.e. they do not offer any methods or tools explaining and highlighting which adaptations of the models are feasible and which are not. Therefore, an approach to identify so-called configurable elements of a workflow modelling language and to add configuration opportunities to workflow models is presented in this paper. Configurable elements are the elements of a workflow model that can be modified such that the behavior represented by the model is restricted. More precisely, a configurable element can be either set to enabled, to blocked, or to hidden. To ensure that such configurations lead only to desirable models, our approach allows for imposing so-called requirements on the model’s configuration. They have to be fulfilled by any configuration, and limit therefore the freedom of configuration choices. The identification of configurable elements within the workflow modelling language of YAWL and the derivation of the new "configurable YAWL" language provide a concrete example for a rather generic approach. A transformation of configured models into lawful YAWL models demonstrates its applicability.

A reference process model represents multiple variants of a common business process in an inte-grated and reusable manner. It is intended to be individualized in order to fit the requirements of a specific organization or project. This practice of individualizing reference process models provides an attractive alternative with respect to designing process models from scratch. In particular, it en-ables the reuse of proven practices. This chapter introduces techniques for representing variability in the context of reference process models, as well as techniques that facilitate the individualization of reference process model with respect to a given set of requirements.

Recent developments in the field of business process management have made it possible to effectively deal with large collections of process models that exhibit many similarities but also context-dependent differences. In this paper these developments are exploited in the domain of screen business. In particular, different processes in audio editing are described in an integrated artifact, called reference process model, capturing their context-dependent variation points. This model can then be configured by domain experts, who are asked to select the process variants that best suit the needs of a specific audio editing project, via the use of a questionnaire. It is argued that configurable reference process models provide a structured basis for communicating the state-of-the-art in this rapidly evolving field.

The verification of business process models is an important step in the design phase of process-aware information systems. While a process model often describes different perspectives like control flow, object flow, and role assignment, most of the contributions in the areas of verification consider only the control flow. Hardly any work considers these three perspectives in a combined verification approach. In this paper we address this gap and introduce Integrated EPCs (iEPCs), a business process modeling language that extends EPCs with a concept of object flow and role assignment. By abstracting from the subtle differences of recent EPC semantics definitions, we show how any of these formalizations can be enhanced with transition rules that consider object existence and role availability as part of the state concept. Furthermore, we define three theorems that relate soundness of EPCs to soundness of iEPCs with different initial role and object set. These theorems provide the basis for a systematic verification approach of iEPCs that first identifies control-flow problems, then object-flow problems, and finally suitable role subsets. This way, our work contributes to a better identification of correctness issues already in conceptual process models in the early design phases.

The application of Business Process Management (BPM) technology can provide significant benefits to an organisation in terms of e.g. cost savings and responsiveness to changes. In this paper the application of this technology in the context of film production is investigated. A prototype named YAWL4Film was developed on top of a state-of-the-art BPM system. YAWL4Film supports collection and entering of production-related data and automatic generation of reports required during film production. This system was deployed in two pilot projects at the Australian Film Television and Radio School.

A configurable process model is an integrated representation of multiple variants of a business process. It is designed to be individualized to meet a particular set of requirements. As such, configurable process models promote systematic reuse of proven or common practices. Existing notations for configurable process modeling focus on capturing tasks and control-flow dependencies, neglecting equally important aspects of business processes such as data flow, material flow and resource management. This paper fills this gap by proposing an integrated meta-model for configurable processes with advanced features for capturing resources involved in the performance of tasks (through task-role associations) as well as flow of data and physical artifacts (through task-object associations). Although embodied as an extension of a popular process modeling notation, namely EPC, the meta-model is defined in an abstract and formal manner to make it applicable to other notations.

Configuration, be it at the level of models or at the level of code, is a recurrent issue in systems engineering. It arises for example in enterprise systems, where modules are adapted and composed to meet the needs of individual customers based on modifications to a reference model. It also manifests itself in the context of software product families, where variants of a system are built from a common code base. Configuration of such generic systems generally involves two phases: (i) collecting data by answering a set of questions; and (ii) performing certain actions on an existing model or code base to produce an individualized model or system. This paper focuses on the first of these phases. The paper proposes a formal foundation for representing configuration models as well as methods to ensure the consistency of these models and to generate questionnaires from them. The generated questionnaires are interactive, in the sense that questions are only posed if and when they can be answered, and the space of allowed answers to a question is determined by previous answers. The approach has been implemented and tested against a reference model from the logistics domain.

Reference models are a widely accepted means to facilitate reusable information system and organizational design. At present, besides domain knowledge, the configuration of reference models requires a thorough understanding of both the reference model and the language it is captured in. This hinders the involvement of domain experts without specialized modeling background, in the configuration of reference models. In this paper, we propose a questionnaire-driven approach to reference model configuration which abstracts away from the modeling language. For illustration, we show how this approach can be applied to reference process models captured in the Configurable EPC notation. To demonstrate its applicability, the proposal has been implemented as a toolset that guides users through the configuration process by means of a form-based interface.

Empirical research in business process management(BPM) is coming of age. In 2009, when the inaugural ER-BPM workshop was held, the field of BPM research was characterized by a strong emphasis on solution development, but also by an increasing demand for insights or evaluations of BPM technology based on dedicated empirical research strategies. The ER-BPM workshop series was created to provide an international forum for researchers to discuss and present such research.

The representation of business process models has been a continuing research topic for many years now. However, many process model representations have not developed beyond minimally interactive 2D icon-based representations of directed graphs and networks, with little or no annotation for information over- lays. With the rise of desktop computers and commodity mobile devices capable of supporting rich interactive 3D environments, we believe that much of the research performed in computer human interaction, virtual reality, games and interactive entertainment has much potential in areas of BPM; to engage, pro- vide insight, and to promote collaboration amongst analysts and stakeholders alike. This initial visualization workshop seeks to initiate the development of a high quality international forum to present and discuss research in this field. Via this workshop, we intend to create a community to unify and nurture the development of process visualization topics as a continuing research area.