Essays on Predictive and Prescriptive Process Monitoring (PhD Thesis, 2023)

Mike Riess's PhD thesis at NMBU School of Economics and Business, defended 2022, published 2023. The thesis bundles four essays (three single-authored, one co-authored with supervisor Joachim Scholderer) under the umbrella of proactive decision support in business processes — predictive process monitoring and prescriptive process monitoring.

Summary

The thesis frames PPM/PrPM as a response to the insufficiency of reactive tools (SPC, Lean/Six Sigma, workforce management) once business processes are heavily digitised through process-aware information systems (PaIS). Value creation is measured along the devil's quadrangle dimensions (time, cost, quality, flexibility; concepts/devils-quadrangle). Machine learning on event-log data is the principal vehicle.

Four research objectives:

Understand how temporal weighting of loss functions affects earliness performance of remaining-time models.
Propose a simulation framework enabling researchers to specify and generate synthetic event logs for controlled-variable PPM evaluation.
Understand how customer loyalty can be influenced by predictive queue prioritisation in customer service.
Review how prior literature has handled automated adaptation of ML models under concept drift.

Essay index (with mapping to separately ingested papers)

#	Title	Status in thesis	Stand-alone ingested source
I	Remaining cycle time prediction: Temporal loss functions and prediction consistency	Submitted to Nordic Machine Intelligence (published 2023)	sources/2023-riess-temporal-loss-remaining-cycle-time
II	A parametric simulation framework for the generation of event-log data	Submitted to SIMULATION (published 2024 as SynBPS)	sources/2024-riess-synbps-simulation-framework
III	Customer-service queuing based on predicted loyalty outcomes (with J. Scholderer)	Submitted to Decision Support Systems	Not in this ingest batch — referenced-only
IV	Automating model management: A survey on metaheuristics for concept-drift adaptation	Revised version of JDIM 2022 paper	sources/2022-riess-metaheuristics-concept-drift-survey

Paper III is referenced through the thesis summary only; the underlying manuscript is not present in raw/ and is therefore filed as referenced-not-ingested.

Structural themes

Methodological commitments

Preference for publicly available event logs as benchmarks (Sepsis, Helpdesk, BPIC Traffic fines, Hospital billing) — with explicit criticism of this practice in Paper II.
LSTM as the PPM workhorse, following Tax et al. 2017 and Navarin et al. 2017.
Simulation as a methodology both for evaluation (Paper II) and as a testbed for prescriptive hypotheses (Paper III).
Literature-review methodology in Paper IV based on Google Scholar with explicit inclusion/exclusion criteria (snapshot-in-time caveat acknowledged).

Connections

entities/mike-riess — author hub.
entities/nmbu — host institution; supervisor Joachim Scholderer (unverified entity page; may be created).
concepts/predictive-process-monitoring, concepts/remaining-time-prediction, concepts/concept-drift, concepts/business-process-simulation.
syntheses/riess-research-arc — synthesis across all seven Riess sources.
Cited works in this wiki: sources/2017-tax-lstm-process-prediction, sources/2017-navarin-lstm-data-aware-remaining-time, sources/2016-teinemaa-structured-unstructured-ppm, sources/2019-verenich-survey-ppm, sources/2020-rama-maneiro-deep-learning-ppm-review, sources/2018-difrancescomarino-genetic-hpo-ppm, sources/2018-dumas-fundamentals-of-bpm, sources/2011-vanderaalst-process-mining-book.