Process mining exploits data recorded in information systems of organizations to unleash insight and knowledge into their operational processes. As process mining techniques are reaching maturity, their applications are becoming more widespread across various domains. Therefore, more research on the methodological and practical perspective is required to steer and guide these applications to successful result. This position paper sketches the rst steps to be taken toward a standard methodology for process mining.
Process Mining has evolved into a well-known technology to provide valuable insight into the underlying processes and work ows of organizations. During the recent years, many process mining techniques and algorithms have been developed and are reaching maturity and their applications have been investigated and proved valuable across variety of domains. Despite this level of maturity in techniques and algorithms, the broader process mining discipline has not yet matured. Although process mining projects involve various steps and activities from extracting and preparing required data to providing useful knowledge and insight, the entire spectrum of activities have not been thoroughly investigated. Instead most of the academic focus has been concentrated on the development and improvement of techniques and algorithms. Besides, most case studies and projects have been carried out in an unstructured and ad-hoc manner involving a great amount of manual and time intensive work and there is little guidance on conducting such projects successfully in both industrial and academic settings. Therefore, this work focuses on methodological aspects of process mining rather than on speci c methods, in order to provide well-de ned foundations for process mining. This not only helps practitioners and academics in conducting successful process mining projects, but positions process mining techniques into a broader spectrum of process-related knowledge discovery and sheds more light on steps that have received less research attention. Thus, inspired by related works in the eld of data mining and practical experience, this work takes the initial steps towards a comprehensive standard methodology for process mining.
The remainder of this paper is structured as follows. The next section discusses related works and their limitations. Section 3 outlines the approach to be followed and steps to be taken for establishment of the comprehensive methodology, followed by a high-level overview of the initial developments of the methodology in section 4. Finally, section 5 concludes the paper. 2
Methodology provides the theoretical foundation for understanding which
methods, set of methods, or best practices can be applied to a speci c case, which is
employed for the design, planning, implementation and achievement of project
objectives [
In order to construct a comprehensive methodology we will rst de ne methodology and clarify what a methodology is and what it should contain and motivate the use and bene ts of such methodologies. Then we will formally compare related work from both process mining literature and related elds such as data mining based on their structure, applicability and reputation. We will also analyze case studies and use cases from a structural point of view before establishing the methodology. In addition, we will make use of questionnaires among process mining experts both in academia and industry to consolidate the motivation and formation of the methodology. Afterwards, the methodology needs to be tested, evaluated and consequently adjusted followed by continuous re nements. The Towards a Comprehensive Methodology for Process Mining next section provides an initial high-level overview of the methodology to be developed. 4
A methodology should be able to be applied to speci c projects in di erent
context with di erent goals and requirements while remaining as generic as possible
[
In addition, the methodology contains a user guide with best practises,
common approaches and techniques in order to guide the user with various
challenges. The methodology describes the overall approach to extract knowledge
and insight into processes and provide a roadmap to follow while planning and
carrying out process mining projects, addressing two of the process mining
challenges stated in the process mining manifesto [
Process mining projects usually involve the following high level phases: Planning which focuses on both the business and technical aspects of the project. In this phase project plan, requirements, objectives and available resources are identi ed and discussed and a concrete project plan is prepared.
Data Discovery containing data extraction and event log preparation. The journey here, which could be one of the most challenging parts of the project starts with identifying relevant data sources, nding, extracting, merging and cleaning the extracted data and leads to preparing an event log in required formats.
Process Discovery consisting of explorative analysis, process overview and control- ow discovery. Depending on requirements and the nature of the project, explorative or goal-driven, the steps taken in this phase vary. Initial insights and statistics into the process is gained which assist the following step of analysis. based on this initial insights project might go back to previous phases to modify and adjust plans or to collect additional data or to adopt di erent views on the data.
Analysis focusing on the main analysis and evaluation of the result. Different types of analysis can be performed and process mining techniques can be combined with data mining, statistics and other types of analysis to provide useful insight and knowledge and address the project objectives.
Knowledge Transfer phase which can be reporting diagnostics and improvement insights and/or preparing a monitoring system for operational support. Due to the iterative nature of process mining projects, there should be multiple iterations introduced between di erent phases. 5
This paper outlines an overview and the landscape of a comprehensive methodology for process mining. The prospective methodology will involve several hierarchical levels, consisting of high level phases to speci c activities for each phase. In addition, a user guide and best practises and techniques will be included to facilitate successful projects in various settings. Continuous extension, re nement and evaluation need to be performed before and after establishment of the methodology to ensure generality, completeness and applicability.