Process mining

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Process mining is a family of techniques for analyzing event data to understand and improve operational processes. Part of the fields of data science and process management, process mining is generally built on logs that contain case id, a unique identifier for a particular process instance; an activity, a description of the event that is occurring; a timestamp; and sometimes other information such as resources, costs, and so on. [1] [2]

Contents

There are three main classes of process mining techniques: process discovery, conformance checking, and process enhancement. In the past, terms like workflow mining and automated business process discovery (ABPD) [3] were used.

Overview

Process mining techniques are often used when no formal description of the process can be obtained by other approaches, or when the quality of existing documentation is questionable. [4] For example, application of process mining methodology to the audit trails of a workflow management system, the transaction logs of an enterprise resource planning system, or the electronic patient records in a hospital can result in models describing processes of organizations. [5] Event log analysis can also be used to compare event logs with prior model(s) to understand whether the observations conform to a prescriptive or descriptive model. It is required that the event logs data be linked to a case ID, activities, and timestamps. [6] [7]

Contemporary management trends such as BAM (business activity monitoring), BOM (business operations management), and BPI (business process intelligence) illustrate the interest in supporting diagnosis functionality in the context of business process management technology (e.g., workflow management systems and other process-aware information systems). Process mining is different from mainstream machine learning, data mining, and artificial intelligence techniques. For example, process discovery techniques in the field of process mining try to discover end-to-end process models that are able to describe sequential, choice relation, concurrent and loop behavior. Conformance checking techniques are closer to optimization than to traditional learning approaches. However, process mining can be used to generate machine learning, data mining, and artificial intelligence problems. After discovering a process model and aligning the event log, it is possible to create basic supervised and unsupervised learning problems. For example, to predict the remaining processing time of a running case or to identify the root causes of compliance problems.

The IEEE Task Force on Process Mining was established in October 2009 as part of the IEEE Computational Intelligence Society. [8] This is a vendor-neutral organization aims to promote the research, development, education and understanding of process mining, make end-users, developers, consultants, and researchers aware of the state-of-the-art in process mining, promote the use of process mining techniques and tools and stimulate new applications, play a role in standardization efforts for logging event data (e.g., XES), organize tutorials, special sessions, workshops, competitions, panels, and develop material (papers, books, online courses, movies, etc.) to inform and guide people new to the field. The IEEE Task Force on Process Mining established the International Process Mining Conference (ICPM) series, [9] lead the development of the IEEE XES standard for storing and exchanging event data [10] [11] , and wrote the Process Mining Manifesto [12] which was translated into 16 languages.

History and place in data science

The term "process mining" was coined in a research proposal written by the Dutch computer scientist Wil van der Aalst. By 1999, this new field of research emerged under the umbrella of techniques related to data science and process science at Eindhoven University. In the early days, process mining techniques were often studied with techniques used for workflow management. In 2000, the first practical algorithm for process discovery, "Alpha miner" was developed. The next year, research papers introduced "Heuristic miner" a much similar algorithm based on heuristics. More powerful algorithms such as inductive miner were developed for process discovery. 2004 saw the development of "Token-based replay" for conformance checking. Process mining branched out "performance analysis", "decision mining" and "organizational mining" in 2005 and 2006. In 2007, the first commercial process mining company "Futura Pi" was established. In 2009, the IEEE task force on PM governing body was formed to oversee the norms and standards related to process mining. Further techniques for conformance checking led in 2010 to alignment-based conformance checking". In 2011, the first process mining book was published. About 30 commercially available process mining tools were available in 2018.

Categories

There are three categories of process mining techniques.

Process mining software

Process mining software helps organizations analyze and visualize their business processes based on data extracted from various sources, such as transaction logs or event data. This software can identify patterns, bottlenecks, and inefficiencies within a process, enabling organizations to improve their operational efficiency, reduce costs, and enhance their customer experience.

See also

Related Research Articles

<span class="mw-page-title-main">Workflow</span> Pattern of activity often with a result

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A workflow pattern is a specialized form of design pattern as defined in the area of software engineering or business process engineering. Workflow patterns refer specifically to recurrent problems and proven solutions related to the development of workflow applications in particular, and more broadly, process-oriented applications.

<span class="mw-page-title-main">XPDL</span>

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Business process discovery (BPD) related to business process management and process mining is a set of techniques that manually or automatically construct a representation of an organisations' current business processes and their major process variations. These techniques use data recorded in the existing organisational methods of work, documentations, and technology systems that run business processes within an organisation. The type of data required for process discovery is called an event log. Any record of data that contains the case id, activity name, and timestamp. Such a record qualifies for an event log and can be used to discover the underlying process model. The event log can contain additional information related to the process, such as the resources executing the activity, the type or nature of the events, or any other relevant details. Process discovery aims to obtain a process model that describes the event log as closely as possible. The process model acts as a graphical representation of the process. The event logs used for discovery could contain noise, irregular information, and inconsistent/incorrect timestamps. Process discovery is challenging due to such noisy event logs and because the event log contains only a part of the actual process hidden behind the system. The discovery algorithms should solely depend on a small percentage of data provided by the event logs to develop the closest possible model to the actual behaviour.

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<span class="mw-page-title-main">Wil van der Aalst</span> Dutch computer scientist and professor

Willibrordus Martinus Pancratius van der Aalst is a Dutch computer scientist and full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. His research and teaching interests include information systems, workflow management, Petri nets, process mining, specification languages, and simulation. He is also known for his work on workflow patterns.

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The α-algorithm or α-miner is an algorithm used in process mining, aimed at reconstructing causality from a set of sequences of events. It was first put forward by van der Aalst, Weijters and Măruşter. The goal of Alpha miner is to convert the event log into a workflow-net based on the relations between various activities in the event log. An event log is a multi-set of traces, and a trace is a sequence of activity names. Several extensions or modifications of it have since been presented, which will be listed below.

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<span class="mw-page-title-main">Conformance checking</span>

Business process conformance checking is a family of process mining techniques to compare a process model with an event log of the same process. It is used to check if the actual execution of a business process, as recorded in the event log, conforms to the model and vice versa.

Mathias Weske is a German computer scientist, and Professor of Business Process Technology at the University of Potsdam, known for his contributions in the field of business process management and as a founder of the business Signavio.

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The IEEE STANDARD 1849-2016, IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams, is a technical standard developed by the IEEE Standards Association. It standardizes "a language to transport, store, and exchange event data ". In 2023, the standard has been revised in and superseded by the IEEE Standard 1849-2023.

The IEEE Task Force on Process Mining (TFPM) is a non-commercial association for process mining. The IEEE Task Force on Process Mining was established in October 2009 as part of the IEEE Computational Intelligence Society at the Eindhoven University of Technology.

Inductive miner belongs to a class of algorithms used in process discovery. Various algorithms proposed previously give process models of slightly different type from the same input. The quality of the output model depends on the soundness of the model. A number of techniques such as alpha miner, genetic miner, work on the basis of converting an event log into a workflow model, however, they do not produce models that are sound all the time. Inductive miner relies on building a directly follows graph from event log and using this graph to detect various process relations.

Process mining is a technique used to turn event data into insights and actions. Techniques used in process mining such as Process discovery and Conformance checking depend only on the order of activities executed in the operations. The event log not only contains the activity details, but also timestamps, resources and data accompanied with process execution. Careful analysis of the external details from the event log can reveal useful information that can be used for making predictions on decisions that might be taken in the future, efficiency and working dynamics of the team, and performance analysis.

Streaming conformance checking is a type of doing conformance checking where the deviation is reported directly when it happens. Instead of event log, streaming conformance checking techniques take event stream and process model as input and for each received event from the stream, it will be compared with the model.

References

  1. ↑ van der Aalst, Wil (2016). Process Mining: Data Science in Action.
  2. ↑ van der Aalst, Wil (2011). Process Mining: Data Science in Action.
  3. ↑ "Automated Business Process Discovery (ABPD)". Gartner.com. Gartner, Inc. 2015. Retrieved 6 January 2015. Gartner Definition.
  4. ↑ "Gartner Top 10 Strategic Technology Trends for 2020". Gartner.
  5. ↑ Kirchmer, M., Laengle, S., & Masias, V. (2013). Transparency-Driven Business Process Management in Healthcare Settings [Leading Edge]. Technology and Society Magazine, IEEE, 32(4), 14-16.
  6. ↑ Luis M. Camarinha-Matos, Frederick Benaben, Willy Picard (2015). Risks and Resilience of Collaborative Networks
  7. ↑ Symeon Christodoulou, Raimar Scherer (2016). eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2016
  8. ↑ "IEEE Task Force on Process Mining". Home page of the task force on process mining. IEEE Task Force on Process Mining. Retrieved 10 January 2021.
  9. ↑ "International Process Mining Conference (ICPM) series". Home page of the ICPM conference series. IEEE Task Force on Process Mining. Retrieved 10 January 2021.
  10. ↑ IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. ieee. 11 November 2016. doi:10.1109/IEEESTD.2016.7740858. ISBN   978-1-5044-2421-9 . Retrieved 10 January 2021.
  11. ↑ "eXtensible Event Stream (XES)". eXtensible Event Stream (XES). IEEE Task Force on Process Mining. 11 November 2016. Retrieved 10 January 2021.
  12. ↑ "Process Mining Manifesto". Process Mining Manifesto. IEEE Task Force on Process Mining. 2011. Retrieved 10 January 2021.
  13. 1 2 Aalst, W. van der, Weijters, A., & Maruster, L. (2004). Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16 (9), 1128–1142.
  14. ↑ Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining Process Models from Workflow Logs. In Sixth international conference on extending database technology (pp. 469–483).
  15. ↑ Cook, J., & Wolf, A. (1998). Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology, 7 (3), 215–249.
  16. ↑ Datta, A. (1998). Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research, 9 (3), 275–301.
  17. ↑ Weijters, A., & Aalst, W. van der (2003). Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering, 10 (2), 151–162.
  18. ↑ Aalst, W. van der, Beer, H., & Dongen, B. van (2005). Process Mining and Verification of Properties: An Approach based on Temporal Logic. In R. Meersman & Z. T. et al. (Eds.), On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2005 (Vol. 3760, pp. 130–147). Springer-Verlag, Berlin.
  19. ↑ Rozinat, A., & Aalst, W. van der (2006a). Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models. In C. Bussler et al. (Ed.), BPM 2005 Workshops (Workshop on Business Process Intelligence) (Vol. 3812, pp. 163–176). Springer-Verlag, Berlin.

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