---
title: "A General Framework for Predictive Business Process Monitoring"
type: source
tags: [ppm, framework, doctoral-proposal]
authors: [Verenich Ilya]
supervisors: [Dumas Marlon; La Rosa Marcello; Maggi Fabrizio Maria; ter Hofstede Arthur]
year: 2016
venue: "CAiSE 2016 Doctoral Consortium, CEUR-WS Vol. 1603"
kind: paper
raw_path: "raw/Predictive process monitoring/A general framework for predictive process monitoring Verenich 2016.pdf"
created: 2026-04-13
updated: 2026-04-13
key_claims:
  - Prior PPM techniques are ad-hoc, dataset-specific, and tackle only narrow prediction tasks.
  - A general framework is needed that handles outcome, completion-path, and instance-level predictions uniformly.
  - Evidence-based BPM benefits from predictive, not just post-mortem, evidence.
---

# Verenich 2016 — A General Framework for Predictive BPM

Doctoral-consortium paper. Sets the programmatic agenda for Ilya Verenich's thesis ([[entities/ilya-verenich]]). Argues that fragmented task-specific PPM techniques should be unified under a general framework capable of handling multiple prediction targets (outcome, activity-level, path, completion).

This programme culminated in the Apromore PPM plugin ([[sources/2018-verenich-apromore-ppm]]) and Verenich's 2019 survey ([[sources/2019-verenich-survey-ppm]]).

## Connections
**Concepts:** [[concepts/predictive-process-monitoring]]
**Authors:** [[entities/ilya-verenich]] · [[entities/marlon-dumas]] · [[entities/marcello-la-rosa]] · [[entities/fabrizio-maggi]]
**Related sources:** [[sources/2018-verenich-apromore-ppm]] · [[sources/2019-verenich-survey-ppm]]