Di Francescomarino et al. — Clustering-Based PPM

Framework for outcome prediction that addresses the runtime-efficiency problem of prefix-specific classifiers: cluster prefixes by control-flow similarity offline, train one classifier per cluster, then map running cases to clusters at inference time.

Contribution

Offline phase — cluster completed prefixes (k-means, DBSCAN, model-based variants); train one classifier per cluster.
Online phase — match running case to its nearest cluster; apply that classifier.
Predicates generalised — temporal logic (LTL) constraints, time constraints, or any boolean function over completed traces.

Significance

An early framework paper contrasting universal vs clustered PPM. Its offline/online decomposition pattern reappears in the Apromore Nirdizati PPM tool (sources/2018-verenich-apromore-ppm).

Connections

Concepts: concepts/outcome-prediction · concepts/trace-encoding Authors: entities/chiara-di-francescomarino · entities/marlon-dumas · entities/fabrizio-maggi · entities/irene-teinemaa