Resolution limits for process comparison from event data

Abstract:One hospital runs bloods and imaging at the same time. Another runs them one after the other, in either order, equally often. Knowing which actually happened, and how it is recorded in data, is critical for all operational managers. In process mining, the standard approach is to construct an event log, and attempt to discover concurrent and sequential processes in a data-driven way. We show this standard approach, built on the stochastic language of an event log, reports only the assumptions of its discovery algorithm, because every such log is explained equally well by a model with no concurrency at all. Further, before any data is acquired, we characterise when data can and cannot distinguish concurrent behaviour. Where it cannot, the distinction is recoverable from evidence the stochastic language discards, such as the times at which activities start and end, or object-centric records that fix an order within an execution. The remedy is therefore a choice of what is recorded, rather than a larger sample. This impacts decision making, as planning resource for truly concurrent services is very different from sequential services.

Databases (cs.DB); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.20489 [cs.DB]

(or arXiv:2609.20489v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2609.20489 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

supplementary.pdf

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