Sato, de Freitas, Barddal & Scalabrin 2021 — A Survey on Concept Drift in Process Mining

37-page systematic literature review from the Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR) and IFPR, Brazil. Submitted to ACM Computing Surveys (December 2021). The wiki's anchor for concept drift specifically within process mining — distinct from but complementary to Riess 2022 (which surveys metaheuristics for drift adaptation across ML broadly).

What the survey does

PRISMA-style SLR over four digital libraries + ICPM workshops:

Source	Hits
ACM Digital Library	20
IEEE Xplore	13
Scopus	56
Springer Link	140
ICPM Workshops	2
Total	231

After inclusion/exclusion criteria (IC1 peer-reviewed empirical drift-PM papers + IC2 comparison papers; ECs filtering duplicates, papers with <5 citations pre-2015, perspective-incomplete papers, weakly-documented experimental papers) and backward snowballing of 3 surveys → 45 papers analysed.

Three research questions: (RQ1) what drift-detection approaches; (RQ2) what tools; (RQ3) how validated.

The taxonomy (Figure 3 in the paper)

Concept drift in PM organised along five axes:

Axis	Categories
Type	Sudden · Gradual · Recurring · Incremental
Duration	Momentary · Permanent
Dynamic	Single-order · Multi-order (changes at different time granularities)
Perspective	Control-flow · Time · Resource · Data · Multi-order
Analysis mode	Offline · Online

13 structural change patterns are referenced (Weber et al. 2008; citation [77] in the survey) for imperative process models: adding/deleting fragments, moving/replacing fragments, adding/removing sub-process levels, adapting control dependencies, changing transition conditions.

Five challenges in drift handling

Drift detection — has a process changed?
Change-point detection — when did it change?
Change localisation — which region of the model changed?
Change characterisation — what type of drift, in which perspective?
Unravelling change-process evolution — discovering the drift process itself (meta-discovery).

Branches and breakdown

Approach	Papers
Concept drift detection	38
Online PM dealing with evolving environments	7

Open problems flagged

Evaluation crisis: F-score is the dominant metric but TP/FP/FN definitions vary across 14 of the surveyed papers; no shared benchmark dataset; no agreed detection-delay protocol → comparing techniques is "cumbersome."
Perspective imbalance: control-flow drift dominates; time, resource, and data drift are under-studied.
Online PM is anaemic: only 7 papers; online PM techniques rarely treat drift as a first-class concern.
Drift-type coverage: many methods detect only sudden drift; gradual, recurring, incremental drift remain hard.

Why this matters for the wiki

Provides the canonical taxonomy + open-problem inventory for concepts/concept-drift in PM contexts.
Complementary to sources/2022-riess-metaheuristics-concept-drift-survey — Sato surveys detection; Riess surveys adaptation.
Complementary to sources/2020-baier-handling-concept-drift-bpm — Sato is the meta-level survey; Baier is the empirical industrial case.
Sets baseline expectations against which any future drift-aware PPM/LLM-PPM work should be evaluated.

Limitations

2021 cut-off — post-2021 work on Transformer-based drift detection, LLM-as-drift-detector, online stream-PM with foundation models is not covered.
Excludes some industrial / non-peer-reviewed work.
The SLR's own exclusion criterion EC3 (approaches not considering changes in a complete perspective of the model) excludes some PPM-specific drift work where the target is a process property rather than a model — relevant given that PPM is often property-targeted.

Connections

Concepts:

concepts/concept-drift — anchor reference.
concepts/predictive-process-monitoring — drift handling is a deployment requirement for PPM.

Methods:

methods/process-discovery-methods — drift detection sits adjacent to discovery; the 7 "online PM" papers are discovery papers handling streams.
methods/process-mining-basics — survey scope is broader than discovery (also conformance + enhancement).

Sources:

sources/2022-riess-metaheuristics-concept-drift-survey — sibling survey on adaptation.
sources/2020-baier-handling-concept-drift-bpm — empirical industrial case study.
[13] (Bose, van der Aalst, Žliobaitė, Pechenizkiy 2011 / 2013) "Dealing with concept drifts in process mining" — priority referenced source; not in raw/ but the foundational drift-in-PM paper.
[16] (Bifet & Gavaldà 2007) ADWIN — referenced ML-side foundation.

Syntheses:

syntheses/concept-drift-in-pm — anchor source; pairs with Riess metaheuristics + Baier industrial case.
syntheses/ppm-landscape — augments the open-problem list in §6.