A Technique for Determining Relevance Scores of Process Activities using
Graph-based Neural Networks
- URL: http://arxiv.org/abs/2008.03110v2
- Date: Wed, 3 Feb 2021 08:57:52 GMT
- Title: A Technique for Determining Relevance Scores of Process Activities using
Graph-based Neural Networks
- Authors: Matthias Stierle, Sven Weinzierl, Maximilian Harl, Martin Matzner
- Abstract summary: We develop a technique to determine the relevance scores for process activities with respect to performance measures.
Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process.
We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Process models generated through process mining depict the as-is state of a
process. Through annotations with metrics such as the frequency or duration of
activities, these models provide generic information to the process analyst. To
improve business processes with respect to performance measures, process
analysts require further guidance from the process model. In this study, we
design Graph Relevance Miner (GRM), a technique based on graph neural networks,
to determine the relevance scores for process activities with respect to
performance measures. Annotating process models with such relevance scores
facilitates a problem-focused analysis of the business process, placing these
problems at the centre of the analysis. We quantitatively evaluate the
predictive quality of our technique using four datasets from different domains,
to demonstrate the faithfulness of the relevance scores. Furthermore, we
present the results of a case study, which highlight the utility of the
technique for organisations. Our work has important implications both for
research and business applications, because process model-based analyses
feature shortcomings that need to be urgently addressed to realise successful
process mining at an enterprise level.
Related papers
- A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches) [4.499009117849108]
We perform a systematic review of academic literature to investigate the integration of AI/ML in business process management.
In business process management and process map, AI/ML has made significant improvements using operational data on process metrics.
arXiv Detail & Related papers (2024-07-07T18:26:00Z) - Natural Language Processing for Requirements Traceability [47.93107382627423]
Traceability plays a crucial role in requirements and software engineering, particularly for safety-critical systems.
Natural language processing (NLP) and related techniques have made considerable progress in the past decade.
arXiv Detail & Related papers (2024-05-17T15:17:00Z) - Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation [3.16536213610547]
Existing approaches to behavioral pattern mining suffer from two limitations.
First, they show limited scalability as incremental computation is incorporated only in the generation of pattern candidates.
Second, process analysis based on mined patterns shows limited effectiveness due to an overwhelmingly large number of patterns obtained in practical application scenarios.
arXiv Detail & Related papers (2024-02-05T11:41:37Z) - QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights.
We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
arXiv Detail & Related papers (2023-11-06T00:21:44Z) - The WHY in Business Processes: Discovery of Causal Execution Dependencies [2.0811729303868005]
Unraveling causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions.
This work offers a systematic approach to the unveiling of the causal business process by leveraging an existing causal discovery algorithm over activity timing.
Our methodology searches for such discrepancies between the two models in the context of three causal patterns, and derives a new view in which these inconsistencies are annotated over the mined process model.
arXiv Detail & Related papers (2023-10-23T14:23:15Z) - Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization [0.0]
We introduce a novel mathematically sound method that integrates theoretical process models with interrelated minimal Hidden Markov Models.
Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection.
arXiv Detail & Related papers (2022-10-03T16:19:27Z) - Extending Process Discovery with Model Complexity Optimization and
Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
The paper presents an approach to process mining providing semi-automatic support to model optimization.
A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity.
We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
arXiv Detail & Related papers (2022-06-10T16:20:59Z) - Process-BERT: A Framework for Representation Learning on Educational
Process Data [68.8204255655161]
We propose a framework for learning representations of educational process data.
Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data.
We apply our framework to the 2019 nation's report card data mining competition dataset.
arXiv Detail & Related papers (2022-04-28T16:07:28Z) - Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
The paper studies existing methods in this field via a Systematic Literature Review ( SLR)
The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
arXiv Detail & Related papers (2021-12-03T08:06:24Z) - Feature Recommendation for Structural Equation Model Discovery in
Process Mining [0.0]
We propose a method for finding the set of (aggregated) features with a possible effect on the problem.
We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs.
arXiv Detail & Related papers (2021-08-13T12:23:01Z) - Process Discovery for Structured Program Synthesis [70.29027202357385]
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
In this paper, we propose to use (block-) structured programs directly as target process models.
We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
arXiv Detail & Related papers (2020-08-13T10:33:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.