Wmp Wil van der Aalst

Discovering Social Networks from Event Logs

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.

Replaying history on process models for conformance checking and performance analysis

Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand‐made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis.

Process flexibility: A survey of contemporary approaches

Business processes provide a means of coordinating interactions between workers and organisations in a structured way. However the dynamic nature of the modern business environment means these processes are subject to a increasingly wide range of variations and must demonstrate flexible approaches to dealing with these variations if they are to remain viable. The challenge is to provide flexibility and offer process support at the same time. Many approaches have been proposed in literature and some of these approaches have been implemented in flexible workflow management systems. However, a comprehensive overview of the various approaches has been missing. In this paper, we take a deeper look into the various ways in which flexibility can be achieved and we propose an extensive taxonomy of flexibility. This taxonomy is subsequently used to evaluate a selection of systems and to discuss how the various forms of flexibility fit together.

Mining process models with non-free-choice constructs

Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the “careflow.” Many process mining approaches have been proposed in recent years. However, in spite of many researchers’ persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.

Product-Based Workflow Design

In manufacturing, the interaction between the design of a product and the process to manufacture this product is studied in detail. Consider, for example, material requirements planning (MRP) as part of current enterprise resource planning (ERP) systems, which is mainly driven by the bill of material (BOM). For information-intensive products such as insurances, and many other services, the workflow process typically evolves or is redesigned without careful consideration of the structure and characteristics of the product. In this paper, we present a method named product-based workflow design (PBWD). PBWD takes the product specification and three design criteria as a starting point, after which formal models and techniques are used to derive a favorable new design of the workflow process. The ExSpect tool is used to support PBWD. Finally, using a real case study, we demonstrate that a full evaluation of the search space for a workflow design may be feasible depending on the chosen design criteria and the specific nature of the product specifications.

Supporting Flexible Processes through Recommendations Based on History

In todays fast changing business environment flexible Process Aware Information Systems (PAISs) are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing flexibility in large PAISs usually leads to less guidance for its users and consequently requires more experienced users. To allow for flexible systems with a high degree of support, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with flexible PAISs, can support end users during process execution by giving recommendations on possible next steps. Recommendations are generated based on similar past process executions by considering the specific optimization goals. In this paper we also evaluate the proposed recommendation service, by means of experiments.

Trace clustering in process mining

Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but still have problems discovering and visualizing less structured ones. Unfortunately, process mining is most interesting in domains requiring flexibility. A typical example would be the treatment process in a hospital where it is vital that people can deviate to deal with changing circumstances. Here it is useful to provide insights into the actual processes but at the same time there is a lot of diversity leading to complex models that are difficult to interpret. This paper presents an approach using trace clustering, i.e., the event log is split into homogeneous subsets and for each subset a process model is created. We demonstrate that our approach, based on log profiles, can improve process mining results in real flexible environments. To illustrate this we present a real-life case study.

XES, XESame, and ProM 6

Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality.

On the role of fitness, precision, generalization and simplicity in process discovery

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are many other metrics that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, several metrics exist to measure the complexity of a model irrespective of the log.

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.