Process Mining Put into Context (original) (raw)
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Enhancement in process mining : guideline for process owner and process analyst
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...
PM 2 : a Process Mining Project Methodology
Process mining aims to transform event data recorded in information systems into knowledge of an organisation's business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM 2 , a methodology to guide the execution of process mining projects. We successfully applied PM 2 during a case study within IBM, a multinational technology corporation, where we identified potential process improvements for one of their purchasing processes.
Development of the Process Mining Discipline
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.
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 ...
Challenges and Use Cases of Process Discovery in Process Mining
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.
Process Mining: A 360 Degree Overview
Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today's information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
The Importance of Process Mining in Enhancing Process Performance in Organisation
2015
academic conference concerned with research, education and application in The development of business intelligence allows organisations to manage and enhance the decision-making process by providing methods and tools for analysing data. Process mining in organisation is needed to develop connection between data mining as business intelligence method and Business Process Management. The main purpose of process mining is to discover process model based on existing event log data that can be used for different objectives. This research will examine the essential concepts of process mining and its tools in order to analyse data and deliver proposal to enhance process performance in organisation. This research conducts focus group discussion as a qualitative method to discuss the advantages of process mining and to compare the process mining tools. The analysis highlights that the process mining has important role in organisation in determining: basic performance metrics; process model; and organisational model, and analysing social network and performance characteristics. Interestingly, both of process mining tools ProM and DISCO have different features and capabilities to discover the business process in organisation. This allows organisation to assess the data of business process transaction and provide some improvement approaches based on the result in process mining. By using the process-mining algorithm and tools, organisation can manage how to improve their process of business more effectively and efficiently in order to achieve their objectives.
Visuliazation of Business Process and Its Risks Using Process Mining
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Process mining is a strategy related to data science and process management which is mainly used to analyze the processes based on event logs. The goal of process mining is to transform event data into details and actions with the help of graphics. Process mining techniques use event data to show what people, machines and organizations are actually doing [1]. Process mining will be able to provide new information about the process that can be used to identify the existing technique in the process and to address their operational and compliance issues.
In recent years, process mining emerged as a new and exciting collection of analysis approaches. Process mining combines process models and event data in various novel ways. As a result, one can find out what people and organizations really do. For example, process models can be automatically discovered from event data. Compliance can be checked by confronting models with event data. Bottlenecks can be uncovered by replaying timed events on discovered or normative models. Hence, process mining can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. Despite the many successful applications of process mining, few people are aware of the recent advances in process mining. One of the main reasons is that process mining is not part of existing (a) data mining, (b) machine learning, (c) business intelligence, (d) process modeling, and (e) simulation approaches and tools. For example, conventional "data miners" use a very broad definition of data mining, but at the same time focus on a limited set of classical problems unrelated to process models (e.g., decision tree learning, regression, pattern mining, and clustering). None of the classical data mining tools supports process mining techniques such as process discovery, conformance checking, and bottleneck analysis. This keynote paper briefly summarizes the differences between process mining and more established analysis and modeling approaches. Moreover, the paper emphasis the need to extract process-related knowledge.