Process Mining Without Perfect Data? Anne Rozinat Says Yes!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anne Rozinat has been a process mining enthusiast for more than two decades.She holds a PhD degree in process mining from the Eindhoven University of Technology (TU/ e).Together with Christian Gu nther, she is a co-founder of one of the oldest process mining tool vendors in existence: Fluxicon (since 2009). 1 Their Disco tool is used by professionals, and has a long-standing tradition of being used by research groups and teachers all over the world, thanks to their Academic Initiative.Fluxicon's Flux Capacitor blog 2 and Process Mining Cafe 3 regularly provide insights on the intersection of industry practice and academic research on process mining.Thanks to her wealth of experience on both sides of the process mining world, Anne is a perfect candidate to provide her views on the topic of our special issue related to Exploring the (Mis)-Match Between Real-World Processes and Event Data.BISE: Hello Anne, thank you for taking your time and joining us today.To start off, can you share a bit about your journey in process mining and what sparked your interest in this field?Anne: Thank you for inviting me!I came across the topic as a student in the early 2000s.Prof. Weske offered great BPM lectures and seminars at the HPI in Potsdam, Germany, where I stumbled upon process mining.Back then, it was still called workflow mining and ProM did not yet exist.I loved the idea of turning BPM on its head and automatically, magically, discovering process maps from event data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it