Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Analysis of Business Process Mining Practice
Methodology for Comparative Analysis of Business
Discussion
Conclusions
References

Business process mining: from theory to practice

Profile image of Ilas AdriansyahIlas Adriansyah

2012, … Process Management Journal

https://doi.org/10.1108/14637151211232669
visibility

…

description

24 pages

link

1 file

Abstract

Purpose -This paper presents a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry suggesting future directions for this practice.

Key takeaways
sparkles

AI

  1. This paper compares business process mining tools, marking a shift from theory to practical industry application.
  2. Business process mining encompasses various techniques including Process Discovery, Conformance Checking, and Extension.
  3. Three perspectives—process, organizational, and case—are critical for effective business process mining analysis.
  4. Noise in event logs remains the biggest challenge, affecting approximately 40% of the tools evaluated.
  5. The paper suggests future research into integrating business process mining tools with cloud-based services.
Figures (5)
Table 1: Mapping process-mining challenges to techniques in current research (adapted and updated from Tiwari et al. (2008))
Table 1: Mapping process-mining challenges to techniques in current research (adapted and updated from Tiwari et al. (2008))
Figure 1: Methodology for comparative analysis of business process mining tools
Figure 1: Methodology for comparative analysis of business process mining tools

Related papers

Process Mining: A Database of Applications (2016)
Philippe Gonella, Serena Dal Molin

2016

A database containing the most comprehensive list of process mining applications (#1 worldwide) in different industries, including banking, insurance, telco, manufacturing, retail, healthcare, government. The research was presented at the CIS IEEE Task Force on Process Mining annual meeting, during 14th International Conference on BPM in Rio de Janeiro. • Scope: cases studies from 100+ companies, and 6 industries, between 2005-2016, • Target audience: executives, decision makers, consultants and researchers

View PDFchevron_right
Towards a method and a guiding tool for conducting process mining projects
Eduardo Santos

2019

Due to the increased use of information systems by organizations, information on the execution of processes is recorded. This enables using process mining as a tool for improving process performance. Process mining allows gaining insights regarding actual processes by extracting and processing data from existing systems. Many projects have been conducted for process discovery, conformance checking, etc. Despite of the existence of general methods for data analysis, there’s a lack of specific methods to support process mining projects. Thus, completions of such projects are often dependent on expertise of the analysts. This paper presents a detailed method for conducting process mining projects and a tool for supporting its execution and retaining the outcomes of each step. A case is analysed for evaluating them. Organizations seeking process performance improvement can get benefit from a method that states how process mining techniques can be used in process mining projects.

View PDFchevron_right
Business Process Modelling vs. Process Mining: A Comparative Study of Methods and Software Tools
Lucija Ivančić

19. hrvatska konferencija o kvaliteti i 10. znanstveni skup Hrvatskog društva za kvalitetu: Book of Proceedings, 2019

In the last three decades, the importance of business process improvement and related techniques in organizational design has been widely stressed. Business process management and process analysis, with their significant impact on process and organizational excellence, continue to remain intriguing topics. Recently, a shift away from traditional business process management methods, such as value-chain approach modelling and process simulation, has occurred, alongside the prominence of process mining that has emerged due to data mining and big data opportunities. Hence, this article investigates characteristics of a more traditional technique of process modelling in well-known methodologies, characteristic of a process-mining technique, and offers a comparison of the two approaches. Additionally, a presentation of one business process modelling tool and one process-mining tool is delivered. In the end, the suitability of techniques presented is discussed, also in relation to the organizational quality and excellence.

View PDFchevron_right
Definition and Validation of Process Mining Use Cases
mozhde T, Albert Eckert

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.

View PDFchevron_right
PM 2 : a Process Mining Project Methodology
Wil van der Aalst

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.

View PDFchevron_right
Development of the Process Mining Discipline
Wil van der Aalst

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.

View PDFchevron_right
Process Mining – New era in process management - A Survey
JACOTECH The Research Group

Process Mining is extraction of process models from event logs recorded by information systems. It acts as a bridge between data mining and business process. It is possible to use process mining to monitor deviations (e.g., comparing the observed events with predefined models or business rules in the context of SOX). . This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine The paper does a survey of important process mining aspects- its perspective, types, tools available for it, advantages and the fundamental algorithm used in Process Mining- Alpha Miner Algorithm.

View PDFchevron_right
Process mining: a research agenda
Wil van der Aalst

Computers in Industry, 2004

View PDFchevron_right
Opportunities and Challenges for Process Mining in Organisations -Results of a Delphi Study
Marlon Dumas, Georgi Kerpedzhiev, Kanika Goel, Moe Thandar Kyaw Wynn, Dominik Fischer, Wil van der Aalst

Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using Process Mining in organisations. Such an understanding has the potential to guide research by highlighting barriers for Process Mining adoption and, thus, can contribute to successful Process Mining initiatives in practice. In this respect, this paper provides a holistic view of opportunities and challenges for Process Mining in organisations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of Process Mining, both as a research field and regarding its application in organisations.

View PDFchevron_right
The Importance of Process Mining in Enhancing Process Performance in Organisation
Joos Gaghenggang

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.

View PDFchevron_right

References (60)

  1. Agrawal, R., Gunopulos, D. and Leymann, F. (1998), "Mining process models from workflow logs", in Schek, H. J. (Ed.), Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology, Springer Verlag, Heidelberg, pp 469-490.
  2. Alves de Medeiros, A. K., van Dongen, B. F., van der Aalst, W. M. P. and Weijters, A. J. M. M. (2004), "Process mining: Extending the a-algorithm to mine short loops", BETA Working Paper Series, WP 113, Eindhoven University of Technology, Eindhoven.
  3. Alves de Medeiros, A. K., Weijters, A.J.M.M. and van der Aalst, W. M. P. (2005). "Genetic process mining: A basic approach and its challenges" In: Business Process Management Workshops (BPM), Nancy, France, September 5, 2005.
  4. Alves de Medeiros, A. K., Guzzo, A., Greco, G., van der Aalst, W. M. P., Weijters, A. J. M. M., van Dongen, B. F. and Sacca, D. (2007), "Process mining based on Clustering: A quest for precision", in: Business Process Management Workshops 2007, Brisbane, Australia, 25th-28th September 2007.
  5. Burratin, A. and Sperduti, A. (2010a), "Heuristics miner for time intervals", European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 28-30 April.
  6. Burratin, A. and Sperduti, A. (2010b), "Automatic determination of parameters values for heuristics miner++", World Congress on Computational Intelligence (WCCI) incorporating Congress on Evolutionary Computation (CEC) 2010, Barcelona, Spain, 18-23 July.
  7. Cattafi, M., Lamma, E., Riguzzi, F. And Storari, S. (2010), "Incremental declarative process mining", in Szczerbicki, E. et al. (Eds) Smart Information and knowledge management, SCI 260, Springer, Berlin, pp. 103-127.
  8. Chang, J.F. (2006), "Business process management systems: Strategies and implementation", Vol. 19, Auerbach Publications, Taylor and Francis Group, Florida, USA.
  9. Cook, J. E., Du, Z., Liu, C. and Wolf, A. L. (2004), "Discovering models of behaviour for concurrent workflows", Computers in Industry, Vol. 53, No. 3, pp. 297-319.
  10. Cook, J. E. and Wolf, A. L. (1998a), "Discovering models of software processes from event-based data", ACM transactions on software engineering and methodology, Vol. 7, No. 3, pp. 215-249.
  11. Dustdar, S., Hoffmann, T. and van der Aalst, W. (2004), "Mining of ad-hoc business processes with teamlog", Technical University of Vienna, Vienna.
  12. Dustdar, S, Hoffmann, T, and van der Aalst, W. (2005), "Mining of ad-hoc business Processes with TeamLog", Data Knowledge and Engineering, Vol. 55, No. 2, pp. 129-158.
  13. Ferreira, D. R., (2009), "Applied Sequence Clustering Techniques for Process Mining", in: Cardoso, J., van der Aalst, W.M.P. (Eds), Handbook of Research on Business Process Modelling, IGI Global, pp. 492-513.
  14. Ferreira, H. and Ferreira, D. R., (2006), "An integrated life cycle for workflow management based on learning and planning". International Journal of Cooperative Information Systems, Vol. 15, No. 4, pp. 485-505.
  15. Ferreira, D., Zacarias, M., Malheiros, M. and Ferreira, P. (2007), "Approaching process mining with sequence clustering: Experiments and findings", in: Alonso, G., Dadam, P. and Rosemann, M. (Eds), Business Process Management (BPM 2007), Springer Verlag, Heidelberg, pp. 360-374.
  16. Fluxicon Process Laboratories (2010), "Process Mining Technology", available at: http://fluxicon.com/technology/. Accessed on (23/06/2010).
  17. Fujitsu (2010), "Automated Business Process Discovery", available at:http://www.fujitsu.com/global/services/software/interstage/. Accessed on (23/06/2010).
  18. Gaaloul, W., Gaaloul, K., Bhiri, S., Haller, A. and Hauswirth, M. (2009), "Log-based transactional workflow mining", Distributed Parallel Databases, Vol. 25, pp. 193- 240.
  19. Gaaloul, W. and Godart, C. (2005), "Mining workflow recovery from event based logs", in: van der Aalst, W. M. P., Benatallah, B., Casati, F., et al. (Eds), Business Process Management (BPM 2005), Springer Verlag, Heidelberg, pp. 169-185.
  20. Gabriel M. Veiga and Diogo R. Ferreira. (2009), "Understanding spaghetti models with sequence clustering for ProM", in: Rinderle-Ma, S. et al. (Eds), BPM 2009 Workshops, LNBIP 43, Springer-Verlag, Berlin.
  21. Gartner (2009), "Cool vendors in business process management", Gartner Inc. ID:G00165554
  22. Gartner (2008), "Automated business process discovery improves BPM outcomes", Gartner Inc. ID:G00164422
  23. Goedertier, S., Martens, D., Vanthienen, J. and Baesens, B. (2009), "Robust process discovery with artificial negative events", Journal of Machine Learning Research, Vol. 10, pp. 1305-1340.
  24. Golani, M. and Pinter, S. (2003), "Generating a process model from a process audit log", in: van der Aalst, W. M. P., ter Hofstede, A. H. M. and Weske, M. (Eds), Business Process Management (BPM 2003), Springer Verlag, Heidelberg, pp. 1020.
  25. Greco, G, Guzzo, A, and Pontieri, L. (2006), "Discovering Expressive Process Models by Clustering Log Traces", IEEE Transactions on Knowledge and Data Engineering, Vol. 18, pp.1010-1027.
  26. Gunther, C. W. and van der Aalst, W. M. P. (2007), "Fuzzy mining: Adaptive process simplification based on multi perspective metrics", in: Alonso, G., Dadam, P. and Rosemann, M. (Eds), Business Process Management (BPM 2007), Springer Verlag, Heidelberg, pp. 328-343.
  27. Hammori, M., Herbst, J. and Kleiner, N. (2004), "Interactive workflow mining". in: Desel, B. P. and Weske, M. (Eds), Business Process Management (BPM 2004), Springer Verlag, Heidelberg, pp. 211.
  28. Herbst, J. and Karagiannis, D., (2004), "Workflow mining with InWoLvE", Computers in Industry, Vol. 53, No. 3, pp. 245-264.
  29. Hwang, S. Y. and Yang, W. S. (2002), "On the discovery of process models from their instances", Decision Support Systems, Vol. 34, No. 1, pp. 41-57.
  30. IDS Scheer (2010), "ARIS Process Performance Manager", available at: http://www.ids- scheer.com/us/en/ARIS/ARIS_Platform/ARIS_Process_Performance_Manager/ 32600.html. Accessed on (23/06/2010).
  31. Iontas (2010), "Iontas Focus Software Suite", available at: http://www.iontas.com/pages/products/products.php. Accessed on (23/06/2010).
  32. Li, J., Wang, H. J., Zhang, Z., Zhao, J. L. (2010). "A policy-based process mining framework: Mining business policy texts for discovering process models", Information Systems and E-Business Management, Vol. 8, pp. 169-188.
  33. Li, Y. and Feng, Y. (2007), "Design of automatic business process modelling method based on process logs", Computer Integrated Manufacturing Systems, Vol. 13, No. 1, pp. 24-30.
  34. Lassen, K.B, van Dongen, B.F, and van der Aalst W.M.P. (2007), "Translating Message Sequence Charts to other Process Languages Using Process Mining", Beta Working Paper WP207, Eindhoven University of Technology, Eindhoven.
  35. Lamma, E., Mello, P., Riguzzi, F. and Storari, S. (2007), "Applying inductive logic programming to process mining", in: Blockeel, H., Ramon, J., Shavlik, J., et al. (Eds) Inductive Logic Programming, Springer Verlag, Heidelberg.
  36. Nikovski, D. and Baba, A. (2007), "Workflow trees for representation and mining of implicitly concurrent business processes", TR2007-072, Mitsubishi Electric Research Laboratories, Cambridge.
  37. OpenConnect (2010), "Comprehend", available at: http://www.oc.com/technology/index.jsp. Accessed on (23/06/2010).
  38. Pallas Athena (2010), "Process Mining", available at: http://www.pallas- athena.com/process-mining. Accessed on (23/06/2010).
  39. ProM (2010) "ProM Process Mining Framework", available at: http://prom.win.tue.nl/research/wiki/prom/start. Accessed (23/06/2010).
  40. Rozinat, A. and van der Aalst, W. M. P. (2006), "Decision mining in ProM", in: Dustdar, S., Fiadeiro, J. L. and Sheth, A. (Eds), Business Process Management (BPM 2006), Springer Verlag, Heidelberg, pp. 420-425.
  41. Schimm, G. (2003). "Mining most specific workflow models from event-based data", in: van der Aalst, W. M. P., ter Hofstede, A. H. M. and Weske, M. (Eds), Business Process Management (BPM) 2003, Springer Verlag, Heidelberg, pp. 1021.
  42. Schimm, G. (2004). "Mining exact models of concurrent workflows", Computers in Industry, Vol. 53, No. 3, pp. 265-281.
  43. Tiwari, A., Turner, C. J. and Majeed, B. (2008). "A review of business process mining: State of the art and future trends", Business Process Management Journal (BPMJ), Vol. 14, No. 1, pp. 5-22.
  44. Turner, C.J, and Tiwari, A. Mehnen, J, (2008), "A genetic programming approach to business process mining", in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 12-16 July 2008, Atlanta, USA, pp 1307- 1314.
  45. van Dongen, B. F. and van der Aalst, W. M. P. (2005), "Multi-Phase process mining: Aggregating instance graphs into EPCs and petri nets", In: 2nd International Workshop on Applications of Petri Nets to Coordination, Workflow and Business Process Management (PNCWB) at the ICATPN 2005, pp. 35-58.
  46. van Dongen, B. F., Alves de Medeiros, A. K., Verbeek, H. M. W., Weijters, A. J. M. M. and van der Aalst, W. M. P. (2005), "The ProM Framework: A new era in process mining tool support", in: Ciardo, G. and Darondeau, P. (Eds), 26th International Conference on Applications and Theory of Petri Nets (ICATPN 2005), Springer Verlag, Heidelberg, pp. 444-454.
  47. van Dongen, B.F. and Adriansyah, A., (2009), "Process mining: Fuzzy clustering and performance visualisation", in: Rinderle-Ma, S. et al. (Eds): BPM 2009
  48. Workshops, LNBIP 43, Springer-Verlag Berlin pp. 158-169,
  49. van der Aalst, W.M.P. (2007), "Trends in business process analysis", in: Proceedings of the 9th International Conference on Enterprise Information Systems (ICEIS) 2007, Madeira, Institute for Systems and Technologies of Information, Control and Communication, INSTICC, Portugal, p. 12-22.
  50. van der Aalst, W. M. P., Weijters, A. J. M. M. and Maruster, L. (2002), "Workflow mining: Which processes can be rediscovered?", Beta Working Paper WP74, Eindhoven University of Technology, Eindhoven.
  51. van der Aalst, W. M. P. and Song, M. (2004), "Mining social networks: Uncovering interaction patterns in business processes", in: Desel, J., Pernici, B. and Weske, M. (Eds), Business Process Management (BPM 2004), Springer Verlag, Berlin, pp. 244-260.
  52. van der Aalst, W.M.P, and Weijters, A.J.M.M. (2004), "Process Mining: a Research Agenda", Computers in Industry, Vol. 53, pp. 231-244.
  53. van der Aalst, W. M. P., Rubin, V., Verbeek, H. M. W., van Dongen, B. F., Kindler, E. and Gunther, C. W. (2010), "Process mining: A two step approach to balance between underfitting and overfitting", Software Systems Modelling, Vol. 9, pp. 87-111.
  54. van der Aalst, W. M. P. and Alves de Medeiros, A. K. (2005), "Process mining and security: Detecting process executions and checking process conformance", Electronic Notes in Theoretical Computer Science, Vol. 121, pp. 3-21.
  55. Vergidis, K., Tiwari, A., Majeed, B. and Roy, R. (2007), "Optimisation of business process designs: An algorithmic approach with multiple objectives", International Journal of Production Economics, Vol. 109, pp. 105-121.
  56. Wen, L., Wang, J., van der Aalst, W.M.P., Wang, Z., Sun, J. (2009), "A novel approach for process mining based on event types", Journal of Intelligent Information Systems, Vol. 32, pp. 163-190.
  57. Weijters, A.J.M.M, and van der Aalst, W.M.P. (2003), "Rediscovering workflow models from event-based data using little thumb", Integrated Computer-Aided Engineering, Vol. 10, pp.151-162.
  58. Weijters, A.J.M.M, and van der Aalst, W.M.P. (2001), "Process mining: Discovering workflow models from event-based data", in: Krose, B, de Rijke, M, Schreiber, G, van Someren, M. (Eds), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001), pp. 283-290.
  59. Weijters, A.J.M.M, van der Aalst, W.M.P, van Dongen, B, Günther, C, Mans, R, Alves de Medeiros , A.K, Rozinat, A, Song M, and Verbeek E. (2007), "Process Mining with ProM", Dastani, M, and de Jong, E. (Eds), in: Proceedings of the 19th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC).
  60. Wen, L, van der Aalst, W.M.P, Wang, J, and Sun, J. (2007), "Mining Process Models with Non Free Choice Constructs", Data Mining and Knowledge Discovery, Vol. 15, pp.145-180.

Related papers

Business Process Mining Approaches: A Relative Comparison
Saiqa Aleem

ArXiv, 2015

Recently, information systems like ERP, CRM and WFM record different business events or activities in a log named as event log. Process mining aims at extracting information from event logs to capture business process as it is being executed. Process mining is an important learning task based on captured processes. In order to be competent organizations in the business world; they have to adjust their business process along with the changing environment. Sometimes a change in the business process implies a change into the whole system. Process mining allows for the automated discovery of process models from event logs. Process mining techniques has the ability to support automatically business process (re)design. Typically, these techniques discover a concrete workflow model and all possible processes registered in a given events log. In this paper, detailed comparison among process mining methods used in the business process mining and differences in their approaches have been prov...

View PDFchevron_right
Business process mining: An industrial application
Wil van der Aalst

Information Systems, 2007

View PDFchevron_right
A review of business process mining: state-of-the-art and future trends
Christopher Turner

Purpose – This paper seeks to examine the area of business process mining, providing an overview of state-of-the-art techniques. An outline of the main problems experienced in the practice of process mining is given along with reference to work that addresses the most challenging issues experienced in this field. This paper also aims to examine the application of soft computing techniques to process-mining problems. Design/methodology/approach – This paper is based on a comprehensive review of literature covering more than 50 research papers. These papers are analysed to identify current trends and future research directions in the field. Findings – Process-mining techniques are now becoming available as graphical interface-driven software tools, where flow diagram representations of processes may be manipulated as part of the mining task. A significant number of papers employ mining heuristics to aid in the task of process discovery. Soft computing algorithms are increasingly being investigated to aid the accuracy and speed of mining algorithms. Many papers exist that address common mining problems such as noise and mining loops. However, problems such as duplicate tasks, mining perspectives and delta analysis require further research. Originality/value – The contribution of this paper is to provide a summary of the current trends in process-mining practice and point out future research directions. A review of the work in this new and expanding area has been provided in the form of illustrative graphs and tables that identify the trends in this area. This is the most comprehensive and up-to-date review of business process-mining literature.

View PDFchevron_right
Comparative Evaluation of Process Mining Tools
Musie Kebede

2015

Process mining is relatively young research area that meets the gap between businesses processes and various IT systems. Event logs are the primary sources for a process mining project and they are captured by different data sources including databases, ERP systems, CRM systems, audit trails, hospital information systems, bank transaction logs, etc. The extracted knowledge from this log enable us to discover the actual process and existing process model for further analysis, evaluation and continuous improvement in their quality. This way, various process mining tools have been developed in the market. Nevertheless, there is a lack of sufficent and comprehensive evaluation frameworks that assist users in selecting the right tool. This thesis proposes a framework that enables the comparison of process mining tools in terms of their functional features. The proposed operations are linked to typical problems reported in existing process mining use cases. Using this framework, the thesis compares three process mining tools, namely ProM, Disco and Celonis The comparison shows that while these tools provide comparable functionality they differ in terms of the way the functionality is provided.

View PDFchevron_right
Business process mining for industry: successes and caveats
Jorn Mehnen

2010

Business Process Mining (BPM) is a powerful technique which aims at mapping the complex structure of industrial processes into human interpretable graph structures by analysing business process traces automatically. The transfer of an innovative idea into an industrially viable product is a challenging task in its own rights. First, this paper introduces the concept of business process mining and an innovative Genetic Programming (GP) approach. Second, this paper addresses the principal caveats and solutions that come with transferring new academic solutions into real-world applications. A real BPM transfer project serves a background for this discussion.

View PDFchevron_right

Cited by

No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness
Julia Eggers

Business & Information Systems Engineering

In recent years, process mining has emerged as the leading big data technology for business process analysis. By extracting knowledge from event logs in information systems, process mining provides unprecedented transparency of business processes while being independent of the source system. However, despite its practical relevance, there is still a limited understanding of how organizations act upon the pervasive transparency created by process mining and how they leverage it to benefit from increased process awareness. Addressing this gap, this study conducts a multiple case study to explore how four organizations achieved increased process awareness by using process mining. Drawing on data from 24 semi-structured interviews and archival sources, this study reveals seven sociotechnical mechanisms based on process mining that enable organizations to create either standardized or shared awareness of sub-processes, end-to-end processes, and the firm’s process landscape. Thereby, this...

View PDFchevron_right
Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study
Marlon Dumas

Business & Information Systems Engineering

Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the percei...

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved