Mining local process models

Tax, Niek; Sidorova, Natalia; Haakma, Reinder; Aalst, Wil M. P. van der

doi:10.1016/j.jides.2016.11.001

Cited by 79 publications

(96 citation statements)

References 31 publications

(46 reference statements)

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“…With a different approach, the w-find algorithm [8] uses the process model to build the patterns, checking their frequency in the logs. Extending these mining techniques, the local process mining approach of Niek Tax et al [22] discovers frequent patterns from the logs providing support to loops. Finally, in [3] tree structured patterns in the XML structure of the XES 1 logs are searched.…”

Section: Related Workmentioning

confidence: 99%

“…Output: A Boolean value indicating if the pattern is frequent or not. 1 Algorithm isFrequentPattern(pattern, T, threshold) return executed 21 Function isTraceCompliant(pattern, trace) 22 forall task ∈ trace do 23 Execute task in the process model 24 sources ← get the tasks that fired the execution of task 25 simulateExecutionInPattern(sources, task, pattern) 26 if pattern has been successfully executed then 27 return true With the current task -the fired one-and the tasks that have fired it -the firing tasks, retrieved by the simulation-, the executed tasks and arcs are saved, in order to analyse and to detect if the execution of the pattern is being disrupted before it is completed (Alg. 2:25).…”

Section: Wominementioning

confidence: 99%

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Mining frequent patterns in process models

Chapela-Campa

Mucientes

Lama

2019

Information Sciences

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Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in wellstructured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Wominementioning

confidence: 99%

Mining frequent patterns in process models

Chapela-Campa

Mucientes

Lama

2019

Information Sciences

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“…Finally, insights in the human routines can be obtained through the discovery of Local Process Models [49], which bridges process mining and sequential pattern mining by finding patterns that include high-level process model constructs such as (exclusive) choices, loops, and concurrency. However, Local Process Models, as opposed to process discovery, only give insight into frequent subroutines of behavior and do not provide the global picture of the behavior throughout the day from start to end.…”

Section: Temporal Relation Mining For Smart Home Environmentsmentioning

confidence: 99%

Generating time-based label refinements to discover more precise process models

Tax

Alasgarov²,

Sidorova

et al. 2019

AIS

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Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights into (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable the application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (i.e., the models are overgeneralizing). Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models. However, there exists no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for the automated generation of label refinements based on the time attribute of events, allowing us to distinguish behaviourally different instances of the same event type based on their time attribute. We show on a case study with real-life smart home event data that using automatically generated refined labels in process discovery, we can find more specific, and therefore more insightful, process models. We observe that one label refinement could have an effect on the usefulness of other label refinements when used together. Therefore, we explore four strategies to generate useful combinations of multiple label refinements and evaluate those on three real-life smart home event logs.

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“…The F-scores of the discovered process models is determined mostly by the precision of the models because the activity filtering impacts precision more than it impacts fitness. One exception is the BPI'12 resource 10939 log [39], where the fitness decreases to below 0.75 as a result of applying one of the two frequency-based filters, while the precision increase as an effect of applying the filter is only minor. Figure 12 shows the maximum F-score for different human behavior event logs as a function of the minimum percentage of activities that are remaining in the log.…”

Section: Evaluation Using Real Life Datamentioning

confidence: 99%

Discovering more precise process models from event logs by filtering out chaotic activities

2018

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Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen reallife event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

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Mining local process models

Cited by 79 publications

References 31 publications

Mining frequent patterns in process models

Mining frequent patterns in process models

Generating time-based label refinements to discover more precise process models

Discovering more precise process models from event logs by filtering out chaotic activities

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