Process equivalence in the context of genetic mining

In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matching some desired service description, or to compare some normative process model with a process model discovered using process mining techniques. Although many researchers have worked on different notions of equivalence (e.g., trace equivalence, bisimulation, branching bisimulation, etc.), most of the existing notions are not very useful in this context. First of all, most equivalence notions result in a binary answer (i.e., two processes are equivalent or not). This is not very helpful, because, in real-life applications, one needs to differentiate between slightly different models and completely different models. Second, not all parts of a process model are equally important. There may be parts of the process model that are rarely activated (i.e., "process veins") while other parts are executed for most process instances (i.e., the "process arteries"). Clearly, differences in some veins of a process are less important than differences in the main artery of a process. To address the problem, this paper proposes a completely new way of comparing process models. Rather than directly comparing two models, the process models are compared with respect to some typical behavior. This way, we are able to avoid the two problems just mentioned. The approach has been implemented and has been used in the context of genetic process mining. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics

Towards an evaluation framework for process mining algorithms

Although there has been a lot of progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results. Furthermore, we describe two possible approaches to evaluate a discovered model (i) using existing comparison metrics that have been developed by the process mining research community, and (ii) based on the so-called k-fold-cross validation known from the machine learning community. To illustrate the application of these two approaches, we compared a set of models discovered by different algorithms based on a simple example log

The need for a process mining evaluation framework in research and practice

Although there has been much progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results

Semantic concepts in product usage monitoring and analysis

Nowadays, complex electronic products, such as DVD players or mobile phones, offer a huge number of functions. As a consequence of the complexity of the devices, customers often have problems to use such products effectively. For example, it has been observed that an increasing number of technically sound products is returned due to, e.g., interaction problems. One possible root cause of this problem is that most product development processes are still too technologydriven, i.e., potential users are brought into contact with the product only at a very late stage. If early consumer tests are carried out, then these typically aim at abstract market evaluations rather than formulating concrete requirements towards the functionality of the product. As a result, products often have little meaning or relevance to the customers. Therefore, we need better ways to involve users in the development of such products. This can be achieved by observing product usage in the field and incorporating the gained knowledge in the product creation process. This paper proposes an approach to build automatic observation modules into products, collect usage data, and analyze these data by means of process mining techniques exploiting a novel semantic link between observation and analysis. This link yields two main benefits: (i) it adds focus to the potential mass of captured data items; and (ii) it reduces the need for extensive post-processing of the collected data. Together, these two benefits speed up the information feedback cycle towards development

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

Translating Message Sequence Charts to other Process Languages Using Process Mining

Message Sequence Charts (MSCs) are often used by software analysts when discussing the behavior of a system with different stakeholders. Often such discussions lead to more complete behavioral models in the form of, e.g., Event-driven Process Chains (EPCs), Unified Modeling Language (UML), activity diagrams, Business Process Modeling Notation (BPMN) models, Petri nets, etc. Process mining on the other hand, deals with the problem of constructing complete behavioral models by analyzing event logs of information systems. In contrast to existing process mining techniques, where logs are assumed to only contain implicit information, the approach presented in this paper combines the explicit knowledge captured in individual MSCs and the techniques and tools available in the process mining domain. This combination allows us to discover high-quality process models. To constructively add to the existing work on process mining, our approach has been implemented in the process mining framework ProM (www.processmining.org)