Flexible Heuristics Miner (FHM)

Weijters, A. J. M. M.; Ribeiro, Joel

doi:10.1109/cidm.2011.5949453

Cited by 321 publications

(273 citation statements)

References 6 publications

(9 reference statements)

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“…Within the scope of this paper, we do not elaborate on the other heuristics of the HM [8], such as long-distance, length-two loops, and the relative-to-best. Those heuristics and improvements to the HM described by the Fodina miner [26] can be used together with the DHM.…”

Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

“…Some activities s ∈ Σ might not have a predecessor or successor in the directed graph induced by D. Intuitively, each task in a process should have a cause (predecessor) and an effect (successor) [8], all tasks in the C-net should be connected. Therefore, we propose two alternative heuristics to enforce this: -all-task-connected heuristic proposed by the HM [8], or -the accepted-task-connected heuristic, a new heuristic. Here, we describe the new accepted-task-connected heuristic.…”

Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

“…For the output binding function O(a) of an activity a ∈ Σ , we need to determine which executions of b ∈ Σ (with (a, b) ∈ D) were caused by an execution of activity a. We use the heuristic proposed by the HM [8] and repeat it for completeness. The heuristic considers activity b to be caused by activity a only if it is the nearest activity that may have caused b.…”

Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

“…As motivated in the introduction, it is likely that this event log contains noise. We applied both the Heuristic Miner (HM) [8] and the Inductive Miner (IM) [9] as representatives of discovery methods supporting noise filtering on such an event log with a controlled degree of noise 1 . Figure 2 shows the resulting process mod- els in BPMN notation.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Most of the more recent and more sophisticated process discovery methods support noise filtering [3]. Existing noise-filtering methods are based on frequencies [7,8,9,10], machine-learning techniques [11,12], genetic algorithms [13], or probabilistic models [14,15]. All of those methods focus on the controlflow perspective (i.e., the event labels) when filtering noise.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Advanced Information Systems Engineering

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Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

Section: Data-driven Discovery Of Causal Netsmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Advanced Information Systems Engineering

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Sampling business process event logs using graph‐based ranking model

Liu

Pei

Cheng

et al. 2020

Concurrency and Computation

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Summary Modern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

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Sampling business process event logs with guarantees

Su,

Liu,

Zhang

et al. 2024

Concurrency and Computation

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SummaryEvent log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high‐quality sample logs. This paper introduces a novel framework to support efficient event log sampling without compromising the quality of the sample log compared to the original one. The approach revolves around the consideration of directly‐follows relation (DFR) among business tasks as the fundamental behavior unit of an event log. By ensuring the DFR equivalence between the original and sample logs, the proposed technique addresses the challenge of sample log quality from the model discovery point of view. The framework is instantiated by seven distinct sampling strategies each has its own specialty and is fully implemented in the open‐source process mining tool platform ProM. To validate its effectiveness, we conducted a comprehensive experimental evaluation using 12 publicly available real‐life event logs against state‐of‐the‐art sampling techniques. The results clearly demonstrate that our technique significantly improves model discovery efficiency while upholding high quality of the discovered models.

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Flexible Heuristics Miner (FHM)

Cited by 321 publications

References 6 publications

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Sampling business process event logs using graph‐based ranking model

Sampling business process event logs with guarantees

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