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arXiv Machine Learning By Antony R. Lee, Peter Ti\v{n}o, Iain B. Styles

Resolution limits for process comparison from event data

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The paper examines how event logs used in process mining can fail to reveal concurrent versus sequential activities, using a hospital example where blood tests and imaging may occur simultaneously or in alternating order. It demonstrates that standard stochastic language approaches only expose the assumptions of their discovery algorithms, often misrepresenting concurrency. The authors argue that the key to distinguishing concurrent behavior lies in the choice of recorded data—such as precise start and end times or object‑centric ordering—rather than simply increasing sample size.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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