From Zero to Hero: A Process Mining Tutorial

2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2014

Current study uses the event process logs of a custom software development process used internally in the IT department of a large automotive company. The target is to extract hidden information about the process, difficult to identify due to the wideness and complexity of the input data. The log file contains all incident records tracked during one release of a software product. This analysis was performed using process mining techniques and tools included in ProM Framework, an academic project of the Eindhoven Technical University. The paper describes the steps followed to extract the working process model, organizational network and statistical information. Based on the obtained results, an action plan is put in practice with the scope of improving the development process and the release result.

Computers in Industry, 2004

It is exciting to see the spectacular developments in process mining since I started to work on this in the late 1990-ties. Many of the techniques we developed 15-20 years ago have become standard functionality in today's process mining tools. Therefore, it is good to view current and future developments in this historical context. This chapter starts with a brief summary of the history of process mining showing how ideas from academia got adopted in commercial tools. This provides the basis to talk about the expanding scope of process mining, both in terms of applications and in terms of functionalities supported. Despite the rapid development of the process mining discipline, there are still several challenges. Some of these challenges are new, but there are also several challenges that have been around for a while and still need to be addressed urgently. This requires the concerted action of process mining users, technology providers, and scientists. Adoption of traditional process mining techniques Process mining started in the late nineties when I had a sabbatical and was working for one year at the University of Colorado in Boulder (USA). Before, I was mostly focusing on concurrency theory, discrete event simulation, and workflow management. We had built our own simulation engines (e.g., ExSpect) and workflow management systems. Although our research was well-received and influential, I was disappointed by the average quality of process models and the impact process models had on reality. In both simulation studies and workflow implementations, the real processes often turned out to be very different from what was modeled by the people involved. As a result, workflow and simulation projects often failed. Therefore, I decided to focus on the analysis of processes through event data [1]. Around the turn of the century, we developed the first process discovery algorithms [2]. The Alpha algorithm was the first algorithm able to learn concurrent process models from event data and still provide formal guarantees. However, at the time, little event data were available and the assumptions made by the first algorithms were unrealistic. People working on data mining and machine learning were (and perhaps still are) not interested in process analysis. Therefore, it was not easy to convince other researchers to work on this. Nevertheless, for me, it was crystal clear that process mining would become a crucial ingredient of any process management or process improvement initiative. In the period that followed, I stopped working on the traditional business process management topics and fully focused on process mining. It is interesting to see that concepts such as conformance checking, organizational process mining, decision mining, token animation, time prediction, etc. were already developed and implemented 15 years ago [2]. These capabilities are still considered to be cutting-edge and not supported by most of the commercial process mining tools.

Process mining is an emerging topic in the BPM marketplace. Recently , several (commercial) software solutions have become available. Due to the lack of an evaluation framework, it is very difficult for potential users to assess the strengths and weaknesses of these process mining tools. As the first step towards such an evaluation framework, we developed a set of process mining use cases and validated these use cases by means of expert interviews and a survey. We present the list of use cases and discuss the insights from our empirical validation. These use cases will then form the basis for a detailed evaluation of current process mining tools on the market.

LECTURE NOTES IN BUSINESS INFORMATION PROCESSING, 2011

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 ...

A Process-Aware Information System (PAIS) is a software system that manages and executes operational processes involving people, applications, and/or information sources on the basis of process models. Example PAISs are workflow management systems, case-handling systems, enterprise information systems, etc. This paper provides a brief introduction to these systems and discusses the role of process models in the PAIS life-cycle. Moreover, it provides a critical reflection on the state-of-the-art based on experiences with process mining. Process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. The insights provided by process mining are very valuable for the development of the next generation PAISs because they clearly show a mismatch between the models proposed for driving these systems and reality. On the one hand, models tend to oversimplify things resulting in systems that are too restrictive. On the other hand, models fail to capture important aspects of business processes.

International Journal of Advanced Trends in Computer Science and Engineering, 2020

To date, the amount of data collected are significant and the growth is exponential in various fields over the last decades. Most companies typically address the issue of storage system capacities and therefore such situation creates the issue handling "big data." However, most companies sometimes may find it a challenge to obtain useful knowledge from a large amount of information. As such, numerous organizations are expected to tackle the data-related difficulties. In contrast, there is a necessity to improve and support business processes in progressively changing conditions. Generally, it is possible to analyses the available data and propose improvements because some tools and methods exist to facilitate such cause. Process mining may offer effective approaches to complement the business process. Process mining allows organizations to take advantage of the information within the systems, but can also be used to check process conformance, predict execution challenges, and identify bottlenecks. For instance, healthcare has rapidly changed over decades of research and development. Through scientific discovery, more diseases can be treated. However, it also affects the feasibility of health institutions to accommodate more complicated health treatment processes and systems effectively. Indeed, many solutions exist to cure a particular disease, since machines are becoming further complex, requiring medical staff to be equipped with necessary training. Also, it significantly increases the cost of health care. This paper presents the insights of process mining, highlighting the possible approaches used to gather and analyses the data using feasible method in process mining including real discovery processes. The paper also discusses the challenges of process mining.

International Journal of Business Process Integration and Management, 2017

Process mining is a discipline that allows organisations to discover, analyse and improve their business processes. Although the techniques, algorithms and software packages intended for the application of this discipline have been evolving positively, challenges have appeared as it is applied to process improvement projects. This is so because most of the methodological approaches developed for its application provide general guidelines, but they do not define the specific steps and tactics for the challenges that a practitioner must go through when facing a process redesign project through process mining. The current development contributes to improving the applicability of this discipline by designing a methodology that allows professionals and organizations to conduct process mining projects. The methodology is illustrated by applying to three case studies, after which it was subjected to evaluation by the personnel of the companies that participated in its application. The limitations of the designed methodology and future research perspectives are finally discussed.

Process mining is a relatively new and emerging research area dealing with the process modeling and process analysis by studying on the event logs. By mining these logs, one can understand what is actually happening in the organization, which will bring quite different results than what people think. That is the reason why big organizations start using process mining to x-ray their organizations by various industrial and scientific process mining applications. This study aims to enlighten the researchers about the details of process mining and ProM framework. To accomplish this, firstly, the details and the conversion process of MXML format, which is the correct format applicable for ProM tool, are explained. After that, some main process mining algorithms are detailed by analyzing Alpha Algorithm, Heuristic Mining Algorithm and Social Network Algorithm. Finally, how to extract data through Software Development cycle process is presented, and the results, which are obtained by analyzing the related data, are depicted.

2019

Business process in an organization consists of numerous activities performed by different actors. A process model is a representation of process executions. In practices, a process model is typically created through meetings and interviews with various stakeholders in the organization. This traditional approach usually takes up to several years to complete. On the other hand, process mining offers an automatic means to develop a process model. The process model discovered by process mining is based on actual process behavior recorded in the event log. However, process mining is a relatively young field, and there is a lack of attention about how to perform a process mining project. In this thesis, we proposed a three-phase process analysis approach using process mining techniques involving process owner and process analyst. The application of the proposed approach is demonstrated using real-life data sets. The approach elaborations and result of the demonstration is combined into a...