van Dongen, Crooy, van der Aalst 2008 — Cycle Time Prediction

One of the earliest papers on remaining cycle-time prediction. Pre-dates deep learning. Uses non-parametric regression over features extracted from the partial trace.

Contribution

Five predictor variants (different feature extraction strategies) are compared on a real-life administrative log.
Establishes that the naïve baseline (average cycle time minus elapsed) is poor mid-process — a key motivating observation for subsequent PPM research.

Significance

Cited by Tax et al. 2017 as the non-parametric-regression baseline. Represents the pre-deep-learning lineage of remaining-time work that entities/wil-van-der-aalst co-led.

Connections

Concepts: concepts/remaining-time-prediction · concepts/predictive-process-monitoring Authors: entities/wil-van-der-aalst