Concept Drift

Concept drift (Gama et al. 2013; Žliobaitė et al. 2016) refers to change in distributions and relationships within the data over time. When drift occurs, a deployed machine-learning model can no longer project learned relationships onto the new reality, and predictive performance degrades. Re-training or re-developing models is a costly, often manual operation in production ML systems (Riess 2022).

Taxonomy (Gama et al.)

Sudden — abrupt change in distribution.
Gradual — slow transition.
Incremental — continuous slow drift.
Recurring — seasonality or cyclical return.
Real drift — change in P(y|X).
Virtual drift — change in P(X) without changing P(y|X).
Feature drift — specific feature relevance/distribution changes (Barddal et al. 2017).

Drift in BPM

Bose, van der Aalst, Žliobaitė & Pechenizkiy (2011) studied drift in process mining. Maisenbacher & Weidlich (2017) explicitly addressed drift in predictive process monitoring. Riess's thesis (sources/2023-riess-phd-thesis-ppm) flags drift as the shared vulnerability of all PPM/PrPM methods: once deployed, any model is subject to drift and needs maintenance.

The PM-specific drift literature is consolidated in Sato, de Freitas, Barddal & Scalabrin 2021 — a 37-page systematic review covering 45 papers across 5 taxonomic axes (type, duration, dynamic, perspective, analysis mode) and 5 challenges (detection, change-point detection, localisation, characterisation, change-process discovery). Sato et al. flag a reproducibility crisis: F-score TP/FP/FN definitions vary across studies, no shared benchmark, no agreed detection-delay protocol. They also document that online PM remains anaemic (only 7 of 45 papers).

A complementary industrial empirical case is Baier, Reimold & Kühl 2020 — a P2P process with 70,774 transactions where drift handling (Page-Hinkley + incremental learning + last-batch retraining) lifts throughput-time prediction accuracy from 0.54 to 0.70. Their headline practical recommendation: of three retraining data-selection strategies (next / mixed / last), last consistently wins because drift detectors fire with delay and the most recent pre-alarm batch already belongs to the new concept.

See syntheses/concept-drift-in-pm for the consolidated synthesis combining Sato (survey), Baier (industrial case), and Riess (metaheuristic-adaptation survey).

Adaptation strategies (Riess 2022 survey)

Detection — Page 1954 CUSUM; Bifet & Gavaldà 2007 EWMA; Pesaranghader & Viktor 2016 Hoeffding drift detection; Ross et al. 2012.
Window-based retraining — retrain on a sliding window of recent data.
Ensemble-based — maintain an ensemble of models, weight/replace by recent performance.
Metaheuristic-driven adaptation — use population-based metaheuristics (Genetic Algorithms, Particle Swarm Optimization, Ant Colony, Harmony Search) to automate feature selection, hyper-parameter optimisation, or full model selection under drift. Riess 2022 finds these population-based methods dominate the 2012–2020 literature.

Evaluation pitfalls

Riess 2022 catalogues recurring evaluation issues in the drift-adaptation literature:

Class distributions often unreported despite accuracy as sole metric (4/17 surveyed studies).
Drift type and pattern unreported when real-world data is used.
Ground-truth-immediately-available assumption unrealistic; delayed-label settings underused.
No head-to-head comparisons of population-based metaheuristics on shared drift tasks.

Drift in customer service / LLM era

Riess & Jørgensen 2025 frame drift as the practical motivation for zero-shot LLM classification in customer-service dialogue analysis: the distribution of incoming-call topics changes as products and issues evolve, so retraining supervised classifiers is continuously costly. An LLM invoked via in-context learning with a human-annotator codebook is proposed as a drift-robust (but not yet production-grade) alternative.

concepts/predictive-process-monitoring · concepts/process-model-quality · concepts/mape-k-loop (self-adaptation)
Author: entities/mike-riess
See also sources/2018-difrancescomarino-genetic-hpo-ppm (GA-based HPO in PPM).
syntheses/concept-drift-in-pm — synthesis page combining Sato survey + Baier industrial case + Riess metaheuristics.