Abstract
When processes are executed, application systems store data about the start and end of functions in so-called log files. The management and evaluation of these data traces from business processes are referred to as process mining. The structure of log files is described using an example and the essential tasks of process mining such as process model generation and process model comparison are discussed.
Process mining usually refers to the data source log files. But the automatic recording of user activities at the front end also provides data traces for process mining. This task mining is covered at the end of the chapter.
Statements that are very specific or refer to specific systems are marked in italics. Readers who are more interested in an overview can skip these parts without losing the content guide.
The following figure establishes the connection to the lifecycle in Fig. 1.9.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
’The ARIS development group at IDS Scheer AG started analysing process instances in the mid‐1990s. Dr Helge Hess and Dr Wolfram Jost played a leading role in the creative ideas. The first process mining system was released for the market by IDS Scheer AG with the product ARIS PPM (Process Performance Manager) in 2000. The first users took it up in the same year and IDS Scheer AG concluded a partnership with SAP AG. Instance processes could be visualised and the actual process model generated. Comparisons between the actual and target model were released 2–3 years later. Software AG has continued to develop the ARIS PPM system since 2009 and still sells it worldwide.
- 2.
References
Bezerra, F., & Wainer, J. (2013). Algorithms for anomaly detection of traces in logs of process aware information systems. Information Systems, 38(1), 33–44.
Burattin, A. (2015). Process mining techniques in business environments: Theoretical aspects, algorithms, techniques and open challenges in process mining. Cham: Springer.
Ferreira, D. R. (2017). A primer on process mining: Practical skills with python and graphviz. Cham: Springer. https://doi.org/10.1007/978-3-319-56427-2.
Laue, R., Koschmider, A., & Fahland, D. (Eds.). (2021). Prozess-Management und Process-Mining: Grundlagen. Berlin Boston: De Gruyter.
Linn, C., & Werth, D. (2016). Sequential anomaly detection techniques in business processes. In W. Abramowicz, R. Alt & B. Franczyk (Eds.), Business information systems workshops. BIS 2016 international workshops, Leipzig, July 6–8. (pp. 196–208). Revised Papers.
Munoz-Gama, J. (2016). Conformance checking and diagnosis in process mining: Comparing observed and modeled processes. Cham: Springer.
Petermann, F. (2017). Einführung in Celonis Process Mining. www.celonis.com. Accessed 22 May 2018.
Peters, R., & Nauroth, M. (2019). Process-Mining: Geschäftsprozesse: smart, schnell und einfach (p. 4). Wiesbaden: Springer Gabler.
Reinkemeyer, L. (Ed.). (2020a). Process mining in action: Principles, use cases and outlook. Cham: Springer Nature.
Reinkemeyer, L. (2020b). Purpose: Identifying the right use cases. In L. Reinkemeyer (Ed.), Process mining in action: Principles, use cases and outlook (p. 21). Cham: Springer Nature.
Soffer, P., Hinze, A., Koschmider, A., Ziekow, H., Di Ciccio, C., Koldehofe, B., Kopp, O., Jacobsen, A., Sürmeli, J., & Song, W. (2017). From event streams to process models and back: Challenges and opportunities. Information Systems. https://doi.org/10.1016/j.is.2017.11.002.
Software, A. G. (2017). ARIS – Process Performance Manager (PPM)
Tsagkani, C., & Tsalgatidou, A. (2022). Process model abstraction for rapid comprehension of complex business processes. Information Systems, 103, 101818.
van der Aalst, W. M. P. (2011). Process mining – data science in action (2nd edn.). Heidelberg: Springer.
van der Aalst, W. M. P., & Weijters, A. J. (2004). Process mining: A research agenda. Computers in Industry, 53(3), 231–244.
Further Reading
Bornet, P., Barkin, I., & Wirtz, J. (2021). Intelligent automation. Hackensack: Lulu.com.
Cappiello, C., Comuzzi, M., Plebani, P., & Fim, M. (2022). Assessing and improving measurability of process performance indicators based on quality of logs. Information Systems, 103, 101874.
IEEE Task Force on Process Mining (2012). Process mining manifesto. http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto. Accessed 22 May 2018.
Kryon www.Kryonsystems.com
Mans, R. S., van der Aalst, W. M. P., & Vanwersch, R. J. B. (2015). Process mining in healthcare: Evaluating and exploiting operational healthcare processes. Cham: Springer. https://doi.org/10.1007/978-3-319-16071-9.
Nalbach, O., Linn, C., Derouet, M., & Werth, D. (2018). Predictive quality: Towards a new understanding of quality assurance using machine learning tools. In 21st International Conference. BIS 2018. (pp. 1–12). Berlin: Springer.
Polyvyanyy, A. (Ed.). (2022). Process querying methods. Cham: Springer.
Turner, C. J., Tiwari, A., Olaiya, R., & Xu, Y. (2012). Process mining: From theory to practice. Business Process Management Journal, 18(3), 493–512.
Willcocks, L., Lacity, M., & Craig, A. (2015). Robotic Process Automation at Xchanging (15/03). Outsourcing Unit Working Research Paper Series. https://eprints.lse.ac.uk/64518/1/OUWRPS_15_03_published.pdf. Accessed 9 Aug 2023.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this chapter
Cite this chapter
Scheer, AW. (2024). Insight Through Process Mining. In: The Composable Enterprise: Agile, Flexible, Innovative. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-43089-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-658-43089-4_7
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-43088-7
Online ISBN: 978-3-658-43089-4
eBook Packages: Computer ScienceComputer Science (R0)