Complete and Interpretable Conformance Checking of Business Processes

García‐Bañuelos, Luciano; Beest, Nick van; Dumas, Marlon; Rosa, Marcello La; Mertens, Willem

doi:10.1109/tse.2017.2668418

Cited by 57 publications

(56 citation statements)

References 34 publications

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“…Many recall measures have been proposed in literature [1,3,5,12,13,14,15,24,25,27,31,32]. In recent years, also several precision measures have been proposed [6,29].…”

Section: Recall and Precisionmentioning

confidence: 99%

“…To avoid later confusion, we use the term recall which is commonly used in pattern recognition, information retrieval, and (binary) classification. Many conformance measures have been proposed throughout the years, e.g., [1,3,5,12,13,14,15,24,25,27,31,32].So far it remains an open question whether existing measures for recall, precision, and generalization measure what they are aiming to measure. This motivates the need for a formal framework for conformance measures.…”

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confidence: 99%

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Evaluating Conformance Measures in Process Mining Using Conformance Propositions

Syring

Tax

Aalst

2019

Lecture Notes in Computer Science

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Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much behavior a process model allows for that was never observed in the event log. Generalization is concerned with quantifying how well a process model generalizes to behavior that is possible in the business process but was never observed in the event log. Many recall, precision, and generalization measures have been developed throughout the years, but they are often defined in an ad-hoc manner without formally defining the desired properties up front. To address these problems, we formulate 21 conformance propositions and we use these propositions to evaluate current and existing conformance measures. The goal is to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures). Additionally, this paper serves as an overview of the conformance checking measures that are available in the process mining area. the years, many process discovery algorithms have been developed, producing process models in various forms, such as Petri nets, process trees, and BPMN.Event logs are often incomplete, i.e., they only contain a sample of all possible behavior in the business process. This not only makes process discovery challenging; it is also difficult to assess the quality of the process model in relation to the log. Process discovery algorithms take an event log as input and aim to output a process model that satisfies certain properties, which are often referred to as the four quality dimensions [1] of process mining: (1) recall: the discovered model should allow for the behavior seen in the event log (avoiding "non-fitting" behavior), (2) precision: the discovered model should not allow for behavior completely unrelated to what was seen in the event log (avoiding "underfitting"), (3) generalization: the discovered model should generalize the example behavior seen in the event log (avoiding "overfitting"), and (4) simplicity: the discovered model should not be unnecessarily complex. The simplicity dimension refers to Occam's Razor: "one should not increase, beyond what is necessary, the number of entities required to explain anything". In the context of process mining, this is often operationalized by quantifying the complexity of the model (number of nodes, number of arcs, understandability, etc.). We do not consider the simplicity dimension in this paper, since we focus on behavior and abstract from the actual model representation. Recall is often referred to as fitness in process min...

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“…Many recall measures have been proposed in literature [1,3,5,12,13,14,15,24,25,27,31,32]. In recent years, also several precision measures have been proposed [6,29].…”

Section: Recall and Precisionmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating Conformance Measures in Process Mining Using Conformance Propositions

Syring

Tax

Aalst

2019

Lecture Notes in Computer Science

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“…8. For the encoding of the behavior of models and logs, we reuse the transformation proposed in [14]. Generally speaking, [14] puts forward a technique for conformance checking which considers the encoding of the behavior of both models and logs as Prime Event Structures (PESs) [27], a well-known model of true concurrency.…”

Section: Setupmentioning

confidence: 99%

“…For the encoding of the behavior of models and logs, we reuse the transformation proposed in [14]. Generally speaking, [14] puts forward a technique for conformance checking which considers the encoding of the behavior of both models and logs as Prime Event Structures (PESs) [27], a well-known model of true concurrency. In fact, the mentioned formalism, PES, has been suggested as a suitable formalism for a unified behavioral representation of a log and a process model in the context of process mining [12].…”

Section: Setupmentioning

confidence: 99%

“…In the latter case, it requires as input any concurrency oracle for transforming the traces in the log into partially-ordered sets of events which are then used to build the PES. According to [14], given a pair of PESs P log , P model from a log and a model, respectively, a pair of configurations s 1 of P log and s 2 of P model are equivalent if they represent equivalent events (i.e. with abuse of notation the two configurations are set equivalent as per Def.…”

Section: Setupmentioning

confidence: 99%

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Local Concurrency Detection in Business Process Event Logs

Armas-Cervantes

Dumas

Rosa

et al. 2019

ACM Trans. Internet Technol.

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Process mining techniques aim at analysing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to overgeneralization. This paper proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.

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Improving Merging Conditions for Recomposing Conformance Checking

Lee

Muñoz-Gama

Verbeek

et al. 2019

Business Process Management Workshops

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Complete and Interpretable Conformance Checking of Business Processes

Cited by 57 publications

References 34 publications

Evaluating Conformance Measures in Process Mining Using Conformance Propositions

Evaluating Conformance Measures in Process Mining Using Conformance Propositions

Local Concurrency Detection in Business Process Event Logs

Improving Merging Conditions for Recomposing Conformance Checking

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