Lijie Wen

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.

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.

Process Mining: Overview and Outlook of Petri Net Discovery Algorithms

Within the research domain of process mining, process discovery aims at constructing a process model as an abstract representation of an event log. The goal is to build a model (e.g., a Petri net) that provides insight into the behavior captured in the log. The theory of regions can be used to transform a state-based model or a set of words into a Petri net that exactly mimics the behavior given as input. Recently several papers appeared on the application of the theory of regions for process discovery. This paper provides an overview of different Petri net based discovery algorithms from both the area of process mining and the theory of regions. The overview encompasses five categories of algorithms, for which common assumptions and problems are indicated. Furthermore, based on the shortcomings of the algorithms in each category, a set of directions for future research in the process discovery area is discussed.

Detecting implicit dependencies between tasks from event logs

Process mining aims at extracting information from event logs to capture the business process as it is being executed. In spite of many researchers’ persistent efforts, there are still some challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, where the process models are represented in Petri nets. In fact, there are totally two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. Implicit dependency is very hard to mine by current mining approaches. Thus we propose three theorems to detect implicit dependency between tasks and give their proofs. The experimental results show that our approach is powerful enough to mine process models with non-free-choice constructs.

A novel approach for process mining based on event types

Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the α-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.

A workflow net similarity measure based on transition adjacency relations

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity or the distance between two processes. Although several approaches have recently been proposed to measure the similarity between business processes, neither the definitions of the similarity notion between processes nor the measure methods have gained wide recognition. In this paper, we define the similarity and the distance based on firing sequences in the context of workflow nets (WF-nets) as the unified reference concepts. However, to many WF-nets, either the number of full firing sequences or the length of a single firing sequence is infinite. Since transition adjacency relations (TARs) can be seen as the genes of the firing sequences which describe transition orders appearing in all possible firing sequences, we propose a practical similarity definition based on the TAR sets of two processes. It is formally shown that the corresponding distance measure between processes is a metric. An algorithm using model reduction techniques for the efficient computation of the measure is also presented. Experimental results involving comparison of different measures on artificial processes and evaluations on clustering real-life processes validate our approach.

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Querying business process models based on semantics

In recent years, the technology of business process management is being more widely used, so that there are more and more business process models (graphs). How to manage such a large number of business process models is challenging, among which the business process model query is a basic function. For example, based on business process model query, the model designer can find the related models and evolve them instead of starting from scratch. It will save a lot of time and is less errorprone. To this end, we propose a language (BQL) for users to express their requirements based on semantics. For efficiency, we adopt an efficient method to compute the semantic features of business process models and use indexes to support the query processing. To make our approach more applicable, we consider the semantic similarity between labels. Our approach proposed in this paper is implemented in our system BeehiveZ. Analysis and experiments show that our approach works well.

DMC: a more precise cohesion measure for classes

Abstract In object-oriented systems, a single class consists of attributes and methods and its cohesion denotes the degree of relatedness among these elements. To quantify the cohesiveness of a class, a large number of measures that only depict method–attribute reference relationships have been proposed in last decade. However, the flow-dependence relationships among attributes, the direction of method–attribute references, and the potential dependence relationships among the elements in the class are ignored. To address this problem, this paper first depicts four types of explicit dependence relationships and uses a class element dependence graph to represent all dependencies among the elements in a class. Then, a dependence matrix that reflects the degree of direct dependence and indirect dependence among the elements in a class is computed. Finally, a more precise cohesion measure for classes is proposed.

Querying business process model repositories

Business process management technology is becoming increasingly popular, resulting in more and more business process models being created. Hence, there is a need for these business process models to be managed effectively. For effective business process model management, being able to efficiently query large amount of business process models is essential. For example, it is preferable to find a similar or related model to customize, rather than building a new one from scratch. This would not only save time, but would also be less error-prone and more coherent with the existing models of the enterprise. Querying large amounts of business process models efficiently is also vital during company amalgamation, in which business process models from multiple companies need to be examined and integrated. This paper provides: an overview of the field of querying business process models; a summary of its literature; and a list of challenges (and some potential solutions) that have yet to be addressed. In particular, we aim to compare the differences between querying business process models and general graph querying. We also discuss literature work from graph querying research that can be used when querying business process models.