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Erschienen in:
The Composable Enterprise as a New Paradigm Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

7. Insight Through Process Mining

verfasst von : August-Wilhelm Scheer

Erschienen in: The Composable Enterprise: Agile, Flexible, Innovative

Verlag: Springer Fachmedien Wiesbaden

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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.

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Vorheriges Kapitel Execution and Operational Performance Support (Case Management)
Nächstes Kapitel From Insight to Action: Robotic Process Automation
Fußnoten
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
This was already included in an early version of the product ARIS PPM and was continued in the literature (Ferreira, 2017; van der Aalst, 2011).
 
Literatur
Bezerra, F., & Wainer, J. (2013). Algorithms for anomaly detection of traces in logs of process aware information systems. Information Systems, 38(1), 33–44.CrossRef
Burattin, A. (2015). Process mining techniques in business environments: Theoretical aspects, algorithms, techniques and open challenges in process mining. Cham: Springer.CrossRef
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.CrossRefMATH
Peters, R., & Nauroth, M. (2019). Process-Mining: Geschäftsprozesse: smart, schnell und einfach (p. 4). Wiesbaden: Springer Gabler.CrossRef
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.CrossRef
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.CrossRef
van der Aalst, W. M. P. (2011). Process mining – data science in action (2nd edn.). Heidelberg: Springer.CrossRef
van der Aalst, W. M. P., & Weijters, A. J. (2004). Process mining: A research agenda. Computers in Industry, 53(3), 231–244.CrossRef
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.CrossRef
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.CrossRef
Metadaten
Titel
Insight Through Process Mining
verfasst von
August-Wilhelm Scheer
Copyright-Jahr
2024
Buch
The Composable Enterprise: Agile, Flexible, Innovative

Print ISBN: 978-3-658-43088-7

Electronic ISBN: 978-3-658-43089-4

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-658-43089-4_7

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