Highlight

[HL] Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study

Maxim Vidgof

Sep 11, 2023 — 1 min read

My personal highlight of the day at BPM 2023: "Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study" by Alessandro Berti, Daniel Schuster & Wil van der Aalst. Go read it at https://arxiv.org/pdf/2307.02194.pdf

Their paper presents different event log abstractions enabling LLM-aided business process analytics.
This approach yields inspirng results, especially for descriptive questions (see pic).
They also integrated it in PM4Py, so we all can benefit from their work already today!

[HL] Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Luise Pufahl presented her SLR on resource allocation in business processes at Weizenbaum Institute today! Resource allocation is a complex problem as resources can have different competences, capabilities and rights. So, together with her colleagues, she posed the question:“What is the state-of-the-art of system-initiated resource allocation approaches for business

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[PotW] Explainable and Effective Process Remaining Time Prediction Using Feature-informed Cascade Prediction Model

Paper of the week: “Explainable and Effective Process Remaining Time Prediction Using Feature-informed Cascade Prediction Model” by Na Guo, Cong Liu, Caihong Li, Qingtian Zeng, Chun Ouyang, Qingzhi Liu, and Xixi Lu. Check it out at: https://ieeexplore.ieee.org/abstract/document/10399950 Remaining time prediction is an important area

[PotW] Multi-instance data behavior in BPMN

Paper of the week: "Multi-instance data behavior in BPMN" by Maximilian König & Mathias Weske. Check it out at: https://ceur-ws.org/Vol-3618/forum_paper_4.pdf BPMN is well known for its poor support for data flow.Despite attempts to improve it,some important aspects are still

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[HL] Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Coming soon

[PotW] Explainable and Effective Process Remaining Time Prediction Using Feature-informed Cascade Prediction Model

[PotW] Multi-instance data behavior in BPMN