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On the Dutch workflow market, a new and interesting paradigm named "case handling" is emerging. The goal of case handling is to overcome the limitations of existing workflow management systems. By using a data-driven approach combined with implicit routing and carefully avoiding context tunneling, awareness and flexibility are improved. Currently, many organizations are considering case handling systems such as FLOWer (Pallas Athena) rather than the more traditional workflow management systems. This paper provides a critical assessment of this development. The goal is to show the pro's and con's of case handling. Moreover, based on this assessment, an alternative approach using slightly extended workflow management systems is proposed. This approach is being pursued by the Dutch government in a project involving the workflow management system Staffware. Based on our experiences thus far, we provide guidelines for selecting the proper technology.

Compliance checking is gaining importance as today's organizations need to show that operational processes are executed in a controlled manner while satisfying predefined (legal) requirements. Deviations may be costly and expose the organization to severe risks. Compliance checking is of growing importance for the business process management and auditing communities. This paper presents a comprehensive compliance checking approach based on Petri-net fragments and alignments. 55 control flow oriented compliance rules, distributed over 15 categories. We formalize them in terms of Petri-net fragments describing the compliant behavior. To check compliance with respect to a rule, the event log describing the observed behavior is aligned with the corresponding fragment. The approach is flexible (easy to add new patterns), robust (the selected alignment between log and fragment is guaranteed to be optimal), and allows for both a quantification of compliance and intuitive diagnostics explaining deviations at the level of alignments. The approach can also handle resource-based and data-based compliance rules and is supported by ProM plug-ins.

Information systems are facing conflicting requirements. On the one hand, systems need to be adaptive and self-managing to deal with rapidly changing circumstances. On the other hand, legislation such as the Sarbanes-Oxley Act, is putting increasing demands on monitoring activities and processes. As processes and systems become more flexible, both the need for, and the complexity of monitoring increases. Our earlier work on process mining has primarily focused on process discovery, i.e., automatically constructing models describing knowledge extracted from event logs. In this paper, we focus on a different problem complementing process discovery. Given an event log and some property, we want to verify whether the property holds. For this purpose we have developed a new language based on Linear Temporal Logic (LTL) and we combine this with a standard XML format to store event logs. Given an event log and an LTL property, our LTL Checker verifies whether the observed behavior matches the (un)expected/(un)desirable behavior.

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in steady-state. Processes may change suddenly or gradually. The drift may be periodic (e.g. due to seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For process management it is crucial to discover and understand such concept drifts in processes. In this paper, we present a case study of analyzing concept drifts in three different processes within a large Dutch municipality.

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches. Keywords: Business process compliance; Compliance monitoring; Operational support

Compliance checking is gaining importance as today's organizations need to show that operational processes are executed in a controlled manner while satisfying prede¿ned (legal) requirements. Deviations may be costly and expose the organization to severe risks. Compliance checking is of growing importance for the business process management and auditing communities. This paper presents an approach for checking compliance of observed process executions recorded in an event log to control-¿ow and temporal compliance requirements. We show a collection of 54 control ¿ow and 15 temporal compliance rules, distributed respectively over 10 and 7 categories. In addition we present how temporal compliance requirements discussed in literature can be uni¿ed and formalized using a generic temporal compliance rule. To check compliance with respect to a compliance rule, the event log describing the observed behavior is aligned with the corresponding rule. The alignment then shows which events occurred out of speci¿ed order and which events deviated by which amount of time from the prescribed behavior. The approach is ¿exible (easy to express new rules), and allowing for multi-perspective diagnostic information in case of compliance violations. The technique and corresponding tool support have been experimentally validated using a case study. Keywords: compliance checking, process mining, conformance checking, data-aware conformance checking, Petri-nets

Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today's information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.

Auditors validate information about organizations and their business processes. Reliable information is needed to determine whether these processes are executed within certain boundaries set by managers, governments, and other stakeholders. Violations of specific rules enforced by law or company policies may indicate fraud, malpractice, risks, or inefficiencies. Traditionally, an audit can only provide reasonable assurance that business processes are executed within the given set of boundaries. Auditors assess the operating effectiveness of process controls, and when these controls are not in place or functioning as expected, they typically check samples of factual data. However, with detailed information about processes increasingly available in high-quality event logs, auditors no longer have to rely on a small set of samples offline. Instead, using process mining techniques, they can evaluate all events in a business process, and do so while it is still running. The omnipresence of electronically recorded business events coupled with process mining technology enable a new form of auditing that will dramatically change the role of auditors: Auditing 2.0.