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WIREs Data Mining Knowl Discov
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Process discovery from event data: Relating models and logs through abstractions

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Event data are collected in logistics, manufacturing, finance, health care, customer relationship management, e‐learning, e‐government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, and so on. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control‐flow model from the event data. This is very challenging, but in recent years, many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad‐hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both of them. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block‐structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also presented to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps broaden the view and facilitates the creation of new discovery approaches.

This article is categorized under:

  • Algorithmic Development > Spatial and Temporal Data Mining
  • Application Areas > Business and Industry
  • Technologies > Machine Learning
  • Application Areas > Data Mining Software Tools
The four basic types of process mining: process discovery (van der Aalst, ), conformance checking (van der Aalst, ), process reengineering (van der Aalst, Adriansyah, & van Dongen, ) (changing the process model), and operational support (van der Aalst et al., ) (influencing the process without reengineering it)
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Screenshots of five different process mining tools. (a) Visual inductive miner (ProM) showing a process tree, (b) ILP miner (ProM) showing a Petri net, (c) Heuristic miner (ProM) showing a C‐net, (d) Disco (Fluxicon) showing a directly follows graph, and (e) Celonis showing a directly follows graph
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Overview positioning the different types of process mining and the role of log abstractions and model abstractions
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Screenshots from Disco and Celonis to show that one should be careful to interpret results from informal models like a filtered directly‐follows graph correctly. (a) Disco showing the full directly‐follows graph, (b) Celonis showing the full directly‐follows graph, (c) Disco showing the full directly‐follows graph, and (d) Celonis showing a filtered directly‐follows graph
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A simple synthetic event log and two formal models derived from it. (a) Frequency distribution of the traces in the event log, (b) Petri net discovered using the Alpha miner, and (c) process tree (visualized in BPMN style) discovered using the inductive miner
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Approach based on abstractions
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A declarative model
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Declarative notations: Eight example constraints
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Process tree →(a, ↺(∧(b, c), →(e, f)), d)
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A Petri net with six transitions {t1, t2, …, t6} and seven places {p1, p2, …, p7}. The initial marking M init = [p1] is shown. M final = [p7] is the final marking
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Extracting an event log (right) from a collection of events (left). Each event in an event log has a case, activity, and timestamp
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The control‐flow perspective is the basis for the other process perspectives (left). Independent of the perspectives included, process mining techniques can be used in online and offline settings (right)
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Algorithmic Development > Spatial and Temporal Data Mining
Application Areas > Business and Industry
Application Areas > Data Mining Software Tools
Technologies > Machine Learning

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