Polato, Sperduti, Burattin, de Leoni 2014 — Data-Aware Remaining Time Prediction

An early explicit data-aware remaining-time model, augmenting van der Aalst-style annotated transition systems with data-attribute-conditioned regression. Pre-deep-learning.

Contribution

Builds a transition system from historical log and annotates it with:
- Activity-transition probabilities (control-flow perspective)
- Regression-model time estimates conditioned on collected data (data perspective)
Remaining time = likelihood-weighted sum over future paths.

Significance

A cited baseline in subsequent PPM work including Polato et al.'s own later publications and the Tax et al. 2017 LSTM comparison. Establishes that data features matter — a conclusion confirmed by every subsequent generation of PPM models.

Connections

Concepts: concepts/remaining-time-prediction · concepts/trace-encoding · concepts/predictive-process-monitoring