Dirk Fahland

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Instantaneous Soundness Checking of Industrial Business Process Models

We report on a case study on control-flow analysis of business process models. We checked 735 industrial business process models from financial services, telecommunications and other domains. We investigated these models for soundness (absence of deadlock and lack of synchronization) using three different approaches: the business process verification tool Woflan, the Petri net model checker LoLA, and a recently developed technique based on SESE decomposition. We evaluate the various techniques used by these approaches in terms of their ability of accelerating the check. Our results show that industrial business process models can be checked in a few milliseconds, which enables tight integration of modeling with control-flow analysis. We also briefly compare the diagnostic information delivered by the different approaches.

Discovering block-structured process models from event logs containing infrequent behaviour

Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

Analysis on demand: Instantaneous soundness checking of industrial business process models

We report on a case study on control-flow analysis of business process models. We checked 735 industrial business process models from financial services, telecommunications, and other domains. We investigated these models for soundness (absence of deadlock and lack of synchronization) using three different approaches: the business process verification tool Woflan, the Petri net model checker LoLA, and a recently developed technique based on SESE decomposition. We evaluate the various techniques used by these approaches in terms of their ability of accelerating the check. Our results show that industrial business process models can be checked in a few milliseconds, which enables tight integration of modeling with control-flow analysis. We also briefly compare the diagnostic information delivered by the different approaches and report some first insights from industrial applications.

Declarative versus imperative process modeling languages : the issue of maintainability

The rise of interest in declarative languages for process modeling both justifies and demands empirical investigations into their presumed advantages over more traditional, imperative alternatives. Our concern in this paper is with the ease of maintaining business process models, for example due to changing performance or conformance demands. We aim to contribute to a rigorous, theoretical discussion of this topic by drawing a link to well-established research on maintainability of information artifacts.

Where did i misbehave? diagnostic information in compliance checking

Compliance checking is gaining importance as todays 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 patterns and alignments. 55 control flow oriented compliance rules, distributed over 15 categories, have been formalized in terms of Petri-net patterns describing the compliant behavior. To check compliance with respect to a rule, the event log describing the observed behavior is aligned with the corresponding pattern. The approach is flexible (easy to add new patterns), robust (the selected alignment between log and pattern 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. The applicability of the approach has been evaluated using various real-life event logs.

Modeling and enacting complex data dependencies in business processes

Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this paper, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability.

Towards Process Models for Disaster Response

In the immediate aftermath of a disaster, routine processes, even if specifically designed for such a situation, are not enacted blindly. Actions and processes rather adapt their behavior based on observations and available information. Attempts to support these processes by technology rely on process models that faithfully capture process execution and adaptation. Based on experiences from actual disaster response settings, we propose to specify an adaptive process as a set of scenarios using a Petri net syntax. Our operational model provides an adaptation operator that synthesizes and adapts the system behavior at run-time based on the given scenarios. An example illustrates our approach.

Repairing process models to reflect reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Processes models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from Dutch municipalities.

Tracing the Process of Process Modeling with Modeling Phase Diagrams

The quality of a business process model is presumably highly dependent upon the modeling process that was followed to create it. Still, there is a lack of concepts to investigate this connection empirically. This paper introduces the formal concept of a phase diagram through which the modeling process can be analyzed, and a corresponding implementation to study a modeler’s sequence of actions. In an experiment building on these assets, we observed a group of modelers engaging in the act of modeling. The collected data is used to demonstrate our approach for analyzing the process of process modeling. Additionally, we are presenting first insights and sketch requirements for future experiments.