KeyValueSets : event logs revisited

In process mining, event logs have traditionally been considered as strictly hierarchical lists of events, where each event belongs to exactly one case, refers to exactly one activity and has a timestamp. Based on this assumption, the XES standard has been developed to describe event logs. In this paper, we reconsider the notion of an event log, by focussing on events as the primary entity. Furthermore, we do not assume the presence of traditional notions like cases and timestamps (or even ordering), but we introduce mappings for sorting and grouping our KeyValueSets. This allows us to provide a generic transformation from a wide variety of formats to standard XES logs

Translating labelled P/T nets into EPCs for sake of communication

Petri nets can be used to capture the behavior of a process in a formal and precise way. However, Petri nets are less suitable to communicate the process to its owner, as simple routing constructs in the process might require a large number of transitions. This paper in- troduces a translation from labelled P/T nets to EPCs in such a way that many transitions can be translated into one EPC connector. The algorithm even allows for translating a set of transitions into an OR connector, even though the concept of OR connectors (especially the OR join connector) has no real equal in Petri nets. Using this translation presented here, labelled P/T nets may be communicated to the process owner by means of the created EPC

On the degree of behavioral similarity between business process models

Quality aspects become increasingly important while business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding of how a degree of behavioral similarity can be defined. In this paper we address this challenge in a novel way. We use causal footprints as an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs. Based on the causal footprint derived from two models we calculate their similarity based on the established vector space model from information retrieval. We illustrate this concept with an example from the SAP Reference Model and present a prototypical implementation as a plug-in to the ProM framework

Filter techniques for region-based process discovery

The goal of process discovery is to learn a process model based on example behavior recorded in an event log. Region-based process discovery techniques are able to uncover complex process structures (e.g., milestones) and, at the same time, provide formal guarantees w.r.t. the model discovered. For example, it is possible to ensure that the discovered model is able to replay the event log and that there are bounds on the amount of additional behavior allowed by the model that is not present in the event log. Unfortunately, region-based discovery techniques cannot handle exceptional behavior. The presence of a few exceptional traces may result in an incomprehensible model concealing the dominant behavior observed. Hence, despite their promise, region-based approaches cannot be applied in everyday process mining practice. This paper addresses the problem by proposing two filtering techniques tailored towards ILP-based process discovery (an approach based on integer linear programming and language-based region theory). Both techniques help to produce models that are less over-fitting w.r.t. the event log and have been implemented in ProM. One of the techniques is also feasible in real-life settings as it, in most cases, reduces computation time compared to conventional region-based techniques. Additionally the technique is able to produce understandable process models that better capture the dominant behavior present in the event log. Keywords: Process mining, process discovery, integer linear programming, filterin

Translating message sequence charts to other process languages using process mining

Message Sequence Charts (MSCs) are a well known language for specifying scenarios that describe how di??erent actors (e.g., system components, people, or organizations) interact. MSCs are often used as a starting point for software analysts to discuss the behavior of a system with di??erent stakeholders. Often such discussions lead to more complete behavioral models described by e.g. Event-driven Process Chains (EPCs), UML activity diagrams, BPMN models, Petri nets, etc. The contribution of this paper is to present a method that uses process mining to translate a set of MSCs that represent example scenarios into a complete process model, e.g., represented in terms of EPCs or Petri nets. Our approach takes MSCs and translates them into a special kind event logs. Unlike all known process mining techniques, we use a new approach that uses event logs containing explicit causal dependencies. This allows us to discover high-quality process models. The approach has been implemented in the process mining framework ProM

Change Mining in Adaptive Process Management Systems

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms

Similarity of business process models : metrics and evaluation

It is common for large and complex organizations to maintain repositories of business process models in order to document and to continuously improve their operations. Given such a repository, this paper deals with the problem of retrieving those process models in the repository that most closely resemble a given process model or fragment thereof. The paper presents three similarity metrics that can be used to answer such queries: (i) label matching similarity that compares the labels attached to process model elements; (ii) structural similarity that compares element labels as well as the topology of process models; and (iii) behavioral similarity that compares element labels as well as causal relations captured in the process model. These similarity metrics are experimentally evaluated in terms of precision and recall, and in terms of correlation of the metrics with respect to human judgement. The experimental results show that all three metrics yield comparable results, with structural similarity slightly outperforming the other two metrics. Also, all three metrics outperform traditional search engines when it comes to searching through a repository for similar business process models

Supporting flexible processes through recommendations based on history

In today's fast changing business environment exible information systems are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing exibility in large information system usually leads to less guidance for its users and consequently requires more experienced users. In order to allow for exible systems with a high degree of guidance, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with exible information systems, can guide 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. This paper also describes an implementation of the proposed recommendation service in the context of ProM and the declarative work ow management system DECLARE