WISE: Unraveling Business Process Metrics with Domain Knowledge

• URL: http://arxiv.org/abs/2410.04387v1
• Date: Sun, 6 Oct 2024 07:57:08 GMT
• Title: WISE: Unraveling Business Process Metrics with Domain Knowledge
• Authors: Urszula Jessen, Dirk Fahland,
• Abstract summary: Anomalies in complex industrial processes are often obscured by high variability and complexity of event data. We introduce WISE, a novel method for analyzing business process metrics through the integration of domain knowledge, process mining, and machine learning. We show that WISE enhances automation in business process analysis and effectively detects deviations from desired process flows.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Anomalies in complex industrial processes are often obscured by high variability and complexity of event data, which hinders their identification and interpretation using process mining. To address this problem, we introduce WISE (Weighted Insights for Evaluating Efficiency), a novel method for analyzing business process metrics through the integration of domain knowledge, process mining, and machine learning. The methodology involves defining business goals and establishing Process Norms with weighted constraints at the activity level, incorporating input from domain experts and process analysts. Individual process instances are scored based on these constraints, and the scores are normalized to identify features impacting process goals. Evaluation using the BPIC 2019 dataset and real industrial contexts demonstrates that WISE enhances automation in business process analysis and effectively detects deviations from desired process flows. While LLMs support the analysis, the inclusion of domain experts ensures the accuracy and relevance of the findings.

Related papers

• Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives [40.62773366902451]
  This paper advocates a specific viewpoint on the field of process mining. We first summarize the framework of process mining, common industrial applications, and the latest advances combined with artificial intelligence. This particular perspective aims to revolutionize process mining by leveraging artificial intelligence to offer sophisticated solutions for complex, multi-organizational data analysis.
  arXiv  Detail & Related papers  (2024-07-15T23:30:34Z)
• 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)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Extracting Process-Aware Decision Models from Object-Centric Process Data [54.04724730771216]
  This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA) IODDA is able to discover how a decision is structured as well as how a decision is made.
  arXiv  Detail & Related papers  (2024-01-26T13:27:35Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• 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)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• Metrics reloaded: Recommendations for image analysis validation [59.60445111432934]
  Metrics Reloaded is a comprehensive framework guiding researchers in the problem-aware selection of metrics. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics.
  arXiv  Detail & Related papers  (2022-06-03T15:56:51Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring [0.0]
  This study proposes a conceptual framework sought to establish and promote understanding of decision-making environment. This study defines the local regions from the validation dataset by using the intermediate latent space representations. The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94.
  arXiv  Detail & Related papers  (2020-09-04T10:28:56Z)
• A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks [0.0]
  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.
  arXiv  Detail & Related papers  (2020-08-07T12:15:30Z)

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.