Paper of the week

[PotW] Multi-instance data behavior in BPMN

Maxim Vidgof

Nov 19, 2023 — 1 min read

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 left out.The authors especially highlight issues when single process instance has to deal with multiple instances of data objects,especially at different states.

To this end, the authors propose semantics for multi-instance data objects and data object collections (DOC) and their translation to colored Petri nets. Authors define 4 operations for DOCs:
- State transitions
- Splitting
⁃ Merging
⁃ Creation

For instance, splitting a DOC into 2 collections of the same data objects in different states can be realized as shown below.
(See helpful explanations and the running example in the paper)

The authors see this work as starting point for future research on multi-instance behavior in process data flow. Further steps include more complex constructs,data locking mechanisms and derivation of BPMN modeling guidelines from the newly defined behavior. So go read the paper!

Personally,I have seen a lot of attempts to improve data flow support in BPMN,some of them even independently proposed by different authors.Still,they are not widely agreed upon neither adopted by major tools.Should we work towards BPMN 2.1 or 3.0?

[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

Coming soon

This is Maxim Vidgof's Blog, a brand new site by Maxim Vidgof that's just getting started. Things will be up and running here shortly, but you can subscribe in the meantime if you'd like to stay up to date and receive emails when new

[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

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

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

Read more

[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

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