Aggregating Causal Runs into Workflow Nets

Desel, Jörg; Aalst, Wil M. P. van der

doi:10.1007/978-3-642-35179-2_14

Cited by 11 publications

(7 citation statements)

References 25 publications

(10 reference statements)

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“…Finally, relaxing the orders of activities with repeat alphabets is linked to parallel blocks in a process model. Other discovery techniques [17,18] proposed the use of concurrency oracles instead of repeat alphabets to derive partial orders linked to parallel blocks in the process model.…”

Section: Patterns In Process Miningmentioning

confidence: 99%

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

Reißner¹,

Armas-Cervantes²,

Rosa³

2022

Preprint

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Automated process discovery is a tactical process mining capability that aims to describe the behavior of a business process recorded in an event log by means of a process model, in order achieve business process transparency. With a plethora of automated process discovery algorithms available, the importance of measuring the quality of a process model for a given event log has increased. One of the key quality aspects, generalization, is concerned with measuring the degree of overfitting of a process model w.r.t. an event log, since the recorded behavior is just an example of the true behavior of the underlying business process. Existing generalization measures exhibit several shortcomings that severely hinder their applicability in practice. For example, they assume the event log fully fits the discovered process model, and cannot deal with large real-life event logs and complex process models. More significantly, current measures neglect generalizations for clear patterns that demand a certain construct in the model. For example, a repeating sequence in an event log should be generalized with a loop structure in the model. We address these shortcomings by proposing a framework of measures that generalize a set of patterns discovered from an event log with representative traces and check the corresponding control-flow structures in the process model via their trace alignment. We instantiate the framework with a generalization measure that uses tandem repeats to identify repetitive patterns that are compared to the loop structures and a concurrency oracle to identify concurrent patterns that are compared to the parallel structures of the process model. In an extensive qualitative and quantitative evaluation using 74 log-model pairs using against two baseline generalization measures, we show that the proposed generalization measure consistently ranks process models that fulfil the observed patterns with generalizing control-flow structures higher than those which do not, while the baseline measures disregard those patterns. Further, we show that our measure can be efficiently computed for datasets two orders of magnitude larger than the largest dataset the baseline generalization measures can handle.

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Section: Patterns In Process Miningmentioning

confidence: 99%

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

Reißner¹,

Armas-Cervantes²,

Rosa³

2022

Preprint

View full text Add to dashboard Cite

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“…See [4] for an example of a synthesis technique using partial orders as input. There are a few process mining techniques that start from partial orders, e.g., the conformance checking technique in [9] and the process discovery technique in [8].…”

Section: Related Workmentioning

confidence: 99%

“…The main contribution is a discussion on the interplay between time and ordering and a concrete preprocessing tool implemented in ProM. Obviously, our framework can be combined with existing partial-order-based techniques such as [1,4,6,8,9].…”

Section: Related Workmentioning

confidence: 99%

May I Take Your Order? On the Interplay Between Time and Order in Process Mining

Aalst¹,

Santos²

2021

Preprint

Self Cite

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Process mining starts from event data. The ordering of events is vital for the discovery of process models. However, the timestamps of events may be unreliable or imprecise. To further complicate matters, also causally unrelated events may be ordered in time. The fact that one event is followed by another does not imply that the former causes the latter. This paper explores the relationship between time and order. Moreover, it describes an approach to preprocess event data having timestamp-related problems. This approach avoids using accidental or unreliable orders and timestamps, creates partial orders to capture uncertainty, and allows for exploiting domain knowledge to (re)order events. Optionally, the approach also generates interleavings to be able to use existing process mining techniques that cannot handle partially ordered event data. The approach has been implemented using ProM and can be applied to any event log.

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“…The resulting set of runs can be used to synthesize a process model (e.g. a Workflow net) [11]. This latter approach however inherits the limitations of the α-algorithm as a method for constructing concurrency oracles.…”

Section: Related Workmentioning

confidence: 99%

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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Aggregating Causal Runs into Workflow Nets

Cited by 11 publications

References 25 publications

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

Generalization in Automated Process Discovery: A Framework based on Event Log Patterns

May I Take Your Order? On the Interplay Between Time and Order in Process Mining

Local Concurrency Detection in Business Process Event Logs

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