Process Mining: Data Science in Action (2nd edition, 2016)

The second foundational BPM textbook in this wiki (alongside sources/2018-dumas-fundamentals-of-bpm). Van der Aalst's single-author treatise — the definitive reference for the process mining tradition that he founded. 16 chapters across 6 parts, ~460 pages. First edition appeared in 2011 under the title Process Mining: Discovery, Conformance and Enhancement of Business Processes; the 2016 second edition expands it into a data-science framing, introduces process trees and alignments, and adds a full chapter on process mining at scale.

This is the raw file in raw/ — despite the filename referencing 2011, the PDF content is the 2nd edition (2016). Both editions are conventionally referenced as "Van der Aalst's Process Mining book".

Why it matters here

This book is the data-driven counterpart to Dumas et al. (2018): where Dumas organises the BPM life-cycle model-first, Van der Aalst organises it log-first. Together they form the operational-frame baseline of this wiki. It is also the textbook expansion of the Process Mining Manifesto (2012) — same author/community, same vocabulary, but from short position paper to full pedagogy.

It anchors the entire raw/Predictive process monitoring/ corpus: PPM extends this book's chapter on operational support (Ch. 10) from offline diagnostics into prospective prediction of running cases.

Ingest strategy

Meta-ingest per the textbook two-step pattern (see CLAUDE.md). This page summarises the whole book and its novel abstractions; chapter-level deep-dives are filed on demand as separate source pages with slug sources/2011-vanderaalst-process-mining-ch<N>-<topic>.md back-linking here.

Author

entities/wil-van-der-aalst — single author; founding figure of process mining.

Chapter index (coarse resolution)

Part	Ch	Title	Pages	Key abstractions
I Introduction	1	Data Science in Action	3–23	Internet of Events; data scientist; process science vs data science; Moore's law
I	2	Process Mining: The Missing Link	25–52	Three mining types (discovery/conformance/enhancement); BPM life-cycle; four perspectives (control-flow/org/case/time); Petri-net running example; positioning vs BPM, data mining, Lean Six Sigma, BPR, CEP, BI, GRC, ABPD/BPI/WM, Big Data
II Preliminaries	3	Process Modeling and Analysis	55–88	Transition systems; Petri nets; workflow nets; YAWL; BPMN; EPCs; causal nets; process trees; model-based verification and performance analysis
II	4	Data Mining	89–118	Instances/variables; supervised (decision trees, regression); unsupervised (k-means, association rules); sequence & episode mining; quality (cross-validation, Occam)
III From Logs to Models	5	Getting the Data	125–160	Data sources; event logs; XES standard; data quality (trust, completeness, granularity); flattening reality into logs
III	6	Process Discovery: An Introduction	163–192	Problem statement; α-algorithm; rediscoverability; representational bias; noise/incompleteness; four quality criteria (fitness, precision, generalisation, simplicity); 2-D slice of 3-D reality
III	7	Advanced Process Discovery Techniques	195–239	Heuristic miner; genetic process mining; region-based miners (state-based, language-based); inductive miner; historical perspective
IV Beyond Discovery	8	Conformance Checking	243–273	Business alignment; token replay; alignments; footprint comparison; applications (repair, evaluation, connecting log to model)
IV	9	Mining Additional Perspectives	275–298	Org perspective (social network analysis, role discovery); time/probability; decision mining; bringing it all together
IV	10	Operational Support	301–322	Refined process mining framework (cartography, auditing, navigation); online mining; detect, predict, recommend; non-stationarity & concept drift; full process mining spectrum
V Putting PM to Work	11	Process Mining Software	325–352	Tool taxonomy; ProM (open-source); commercial (Celonis, Disco, myInvenio, …); outlook
V	12	Process Mining in the Large	353–385	Big event data; N=all; case-based & activity-based decomposition; process cubes; streaming mining
V	13	Analyzing "Lasagna Processes"	387–409	Characterisation of structured/well-behaved processes; 5-stage methodology; applications by sector
V	14	Analyzing "Spaghetti Processes"	411–429	Characterisation of unstructured/high-variability processes; fuzzy mining; examples
VI Reflection	15	Cartography and Navigation	431–445	Business process maps; map quality; aggregation/abstraction; seamless zoom; process mining as "TomTom for business processes"
VI	16	Epilogue	447–451	Mining as bridge between data mining and BPM; challenges; actionable next steps

Novel abstractions & why they matter

Relative to methods/process-mining-basics (already seeded from Dumas 2018 and the Manifesto), this book introduces or deepens:

Alignments (§8.3) — cost-optimal mapping between log traces and model paths; has largely supplanted token replay as the state-of-the-art conformance technique.
Footprint comparison (§8.4) — log-vs-model comparison at the direct-succession-matrix level.
Inductive miner (§7.5) — the dominant modern discovery algorithm; guarantees soundness and handles incomplete logs via log splitting.
Heuristic miner (§7.2) — robust to noise via dependency thresholds.
Genetic process mining (§7.3) and region-based miners (§7.4) — alternative discovery families.
Process trees (§3.2.8) — hierarchical representation with composition operators; the target language for inductive mining.
Causal nets (C-nets) (§3.2.7) — representation combining direct succession with AND/XOR bindings.
Four quality criteria (§6.4.3) — fitness, precision, generalisation, simplicity — Van der Aalst's crisp formalisation of the over/under-fitting trade-off.
Refined process mining framework (§10.1) — three activities (cartography, auditing, navigation) × two regimes (offline/online) = the Process Mining Spectrum.
Operational support: detect / predict / recommend (§10.3–10.5) — the runtime side of mining; the conceptual foundation of PPM.
Concept drift (§10.6) — non-stationary processes; sub-chapter that grounds later PPM work on drift handling.
Lasagna vs Spaghetti processes (Chs. 13–14) — a widely-used shorthand characterising process regularity.
Data-quality taxonomy (§5.4) — classification of event-log issues; drives log-preparation practice.
Process cubes & streaming mining (Chs. 12) — scaling mining to big event data.

New shared pages created from this meta-ingest:

concepts/conformance-checking — covering alignments, token replay, footprints, four quality dimensions.
concepts/operational-support — detect/predict/recommend; concept drift.
concepts/lasagna-spaghetti-processes — characterising process regularity.
concepts/process-mining-spectrum — refined framework (cartography/auditing/navigation × offline/online).

Deferred for chapter deep-dives: inductive miner internals (Ch. 7), alignments math (Ch. 8), full operational-support formalism (Ch. 10), process cubes (Ch. 12).

Relation to the Process Mining Manifesto (2012)

The book is the pedagogical counterpart to van der Aalst et al. 2012. Same author, same IEEE Task Force community, same three-type taxonomy (discovery / conformance / enhancement). The Manifesto is a ~25-page rallying call; this book is the ~460-page textbook grounding. They cross-reference each other as position paper ↔ definitive reference and can be treated as canonical pair.

Connections

Concepts: concepts/business-process · concepts/bpm-lifecycle · concepts/process-discovery · concepts/process-model-quality · concepts/conformance-checking · concepts/operational-support · concepts/lasagna-spaghetti-processes · concepts/process-mining-spectrum · concepts/predictive-process-monitoring · concepts/declarative-process-modelling

Frameworks: frameworks/bpmn · frameworks/declare

Methods: methods/process-mining-basics · methods/process-discovery-methods

Entities: entities/wil-van-der-aalst

Related sources: sources/2018-dumas-fundamentals-of-bpm (model-first sibling) · sources/2012-vanderaalst-process-mining-manifesto (manifesto precursor) · sources/2021-dumas-process-mining-2-from-insights-to-action (next-generation evolution) · the entire raw/Predictive process monitoring/ corpus builds on Ch. 10.

Open questions / future deep-dive candidates

Chapter 7 (advanced discovery) — warrants dedicated ingest for the inductive miner (dominant algorithm today).
Chapter 8 (conformance) — warrants a deep-dive on alignment cost functions and their applications.
Chapter 10 (operational support) — the conceptual parent of the whole PPM literature; deserves a deep-dive to ground the PPM synthesis.
Chapter 12 (process mining in the large) — connects to OCEL 2.0 work (sources/2023-berti-ocel-2-specification).