Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. TherefS3A we have developed techniques fi discovering workflow models. Starting pointfS such techniques is a so-called "workflow log" containinginfg3SfiHfl" about the workflow process as it is actually being executed. We present a new algorithm to extract a process modelf3q such a log and represent it in terms of a Petri net. However, we will also demonstrate that it is not possible to discover arbitrary workflow processes. In this paper we explore a classof workflow processes that can be discovered. We show that the #-algorithm can successfqFS mine any workflow represented by a so-called SWF-net. Key words: Workflow mining, Workflow management, Data mining, Petri nets. 1

Modern window-based user interface systems generate user interface events as natural products of their normal operation. Because such events can be automatically captured and because they indicate user behavior with respect to an application's user interface, they have long been regarded as a potentially fruitful source of information regarding application usage and usability. However, because user interface events are typically voluminos and rich in detail, automated support is generally required to extract information at a level of abstraction that is useful to investigators interested in analyzing application usage or evaluating usability. This survey examines computer-aided techniques used by HCI practitioners and researchers to extract usability-related information from user interface events. A framework is presented to help HCI practitioners and researchers categorize and compare the approaches that have been, or might fruitfully be, applied to this problem. Because many of the techniques in the research literature have not been evaluated in practice, this survey provides a conceptual evaluation to help identify some of the relative merits and drawbacks of the various classes of approaches. Ideas for future research in this area are also presented. This survey addresses the following questions: How might user interface events be used in evaluating usability? How are user interface events related to other forms of usability data? What are the key challenges faced by investigators wishing to exploit this data? What approaches have been brought to bear on this problem and how do they compare to one another? What are some of the important open research questions in this area?

Contemporary workflow management systems are ch'iven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. Starting point for such techniques are so-called "workflow logs" containing information about the workflow process as it is actually being executed. In this paper, we extend our existing mining technique c [4] to incorporate time. We assume that events in workflow logs bear timestamps. This information is used to attribute timing such as queue times to the discovered workflow model. The approach is based on Petri nets and timing information is attached to places. This paper also presents our workflow-mining tool EMIT. This tool translates the workflow log of several commercial systems (e.g., Staffware) to an independent XML format. Based on this format the tool mines for causal relations and produces a graphical workflow model expressed in terms of Petri nets.

Enterprise information systems support and control operational business processes ranging from simple internal back-office processes to complex interorganizational processes. Technologies such as Workflow Management (WFM), Enterprise Application Integration (EAI), Enterprise Resource Planning (ERP), and Web Services (WS) typically focus on the realization of IT support rather than monitoring the operational business processes. Process mining aims at extracting information from event logs to capture the business process as it is being executed. In this paper, we try to put the topic of process mining into context, discuss the main issues around process mining, and finally we introduce the papers in this special issue.

Process studies and improvement efforts typically call for new instrumentation on the process in order to collect the data they have deemed necessary. This can be intrusive and expensive, and resistance to the extra workload often foils the study before it begins. The result is neither interesting new knowledge nor an improved process. In many organizations, however, extensive historical process and product data already exist. Can these existing data be used to empirically explore what process factors might be affecting the outcome of the process? If they can, organizations would have a cost-effective method for quantitatively, if not causally, understanding their process and its relationship to the product. We present a case study that analyzes an in-place industrial process and takes advantage of existing data sources. In doing this, we also illustrate and propose a methodology for such exploratory empirical studies. The case study makes use of several readily available repositories of process data in the industrial organization. Our results show that readily available data can be used to correlate both simple aggregate metrics and complex process metrics with defects in the product. Through the case study, we give evidence supporting the claim that exploratory empirical studies can provide significant results and benefits while being cost effective in their demands on the organization.

Abstract Most information systems log events (e.g., transaction logs, audit trails) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: “Does the event log conform to the process model and vice versa?”. This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework. 1

Understanding the dynamic behavior of a workflow is crucial for being able to modify, maintain, and improve it. A particularly difficult aspect of some behavior is concurrency. Automated techniques which seek to mine workflow data logs to discover information about the workflows must be able to handle the concurrency that manifests itself in the workflow executions. This paper presents techniques to discover patterns of concurrent behavior from traces of workflow events. The techniques are based on a probabilistic analysis of the event traces. Using metrics for the number, frequency, and regularity of event occurrences, a determination is made of the likely concurrent behavior being manifested by the system. Discovering this behavior can help a workflow designer better understand and improve the work processes they are managing.

Current workflow management systems require the explicit design of the workflows that express the business process of an organization. This process design is very time consuming and error prone. Considerable work has been done to develop heuristics to mine event-data logs to produce a process model that can support the workflow design process. However, all the existing heuristic-based mining algorithms have their limitations. To achieve more insight into these limitations the starting point in this paper is the α-algorithm [3] for which it is proved under which conditions and process constructs the algorithm works. After presentation of the α-algorithm, a classification is given of the process constructs that are difficult to handle for this type of algorithms. Then, for some constructs (i.e. short loops) it is illustrated in which way the α-algorithm can be extended so that it can correctly discover these constructs.

Research in formal methods has produced fruitful techniques that can verify global properties of a design of a real-time system, or exact behavioral correspondence to the design. However, exactness is often not achieved, yet understanding how close the design and system correspond would be very valuable, to direct further efforts in achieving exactness, or to modify the design where the system simply cannot achieve the requirements. This paper describes a method and tool that quantitatively measures how closely the behavior of a real-time system corresponds to its specification, given in a timed, concurrent model.

Process support environments (PSEs) are widely used for modelling, enacting and analyzing human intensive processes. The benefits of a PSE become apparent when processes to be supported are long lived and distributed and contain heterogeneous components. Generally, such processes are subject to dynamic evolution, i.e. they have to be changed during their execution.