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REVIEW 3 major objections 6 minor 80 references

Process Mining on Distributed Data Sources

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that process mining must move from centralized event logs to distributed, online analysis of sensor data.

desk verdict A solid, honest roadmap for distributed and sensor-driven process mining, but the load-bearing event-ID correlation step is asserted rather than argued and should be named as an open problem. read the letter →

arxiv 2506.02830 v1 pith:FQY6L22V submitted 2025-06-03 cs.ET cs.DBcs.DC

Maximilian Weisenseel , Julia Andersen , Samira Akili , Christian Imenkamp , Hendrik Reiter , Christoffer Rubensson , Wilhelm Hasselbring , Olaf Landsiedel
show 5 more authors
Xixi Lu Jan Mendling Florian Tschorsch Matthias Weidlich Agnes Koschmider
This is my paper · ORCID
classification cs.ETcs.DBcs.DC
keywords processminingdistributeddatasourcessensoronlineedgeprocessingprivacyeventabstractionresearchagenda
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces the concept of process mining on distributed data sources as a new class of process analysis. It argues that the classical assumption of a single, centralized, static, totally ordered event log no longer holds in logistics, healthcare, and smart manufacturing, where data arrives as continuous, heterogeneous, uncertain streams from many distributed sensors. The authors identify three shifts—offline to online, centralized to distributed, and event logs to sensor data—and define six interconnected research fields: edge processing, scalability, abstraction, privacy, explainability, and visualization. The contribution is a research agenda and a methodology, grounded in algorithm engineering, for building process mining techniques that compute partial results near the data source and merge them into global process models.

What carries the argument

The central object is the distributed data-flow graph built from process fragments. Each sensor node locally generates a fragment—an aggregate of locally observed events and their relationships to events shared with nearby sensors, matched via event IDs—and forwards it to neighbors or edge devices, where fragments merge stepwise into compound fragments and finally a complete process graph available for discovery and conformance checking. This fragment-merging mechanism carries the argument because it maps process mining onto physically distributed IoT, edge, and cloud devices and provides the basis for locality-aware, resource-efficient, adaptive mining.

What would settle it

Run the proposed fragment-merging pipeline on a real sensor stream, such as hospital real-time location traces, while deleting or scrambling an increasing fraction of event IDs, and compare the resulting merged data-flow graph with the model obtained from the same data collected centrally; if small rates of missing or ambiguous IDs cause large divergence, the correlation premise fails.

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Extended reading notes

Core claim

The paper's central claim is that process mining can and should be re-founded on distributed, online analysis of sensor data rather than retrospective analysis of centrally collected event logs. Concretely, it envisions each sensor node building a local event log and computing partial mining results—process fragments—that are matched with nearby nodes via event IDs and merged stepwise into compound fragments and finally a complete distributed data-flow graph at the edge or cloud. The authors do not present algorithms with empirical validation; instead they establish the conceptual and methodological foundations, formulating research questions for each field and arguing that infrastructure, data, and user perspectives must be treated jointly rather than in isolation. If this is right, process mining shifts from asking how to mine efficiently to asking where to mine efficiently, with privacy, scalability, and real-time responsiveness emerging from processing data close to its source.

Load-bearing premise

The roadmap depends on the assumption that low-level observations from many heterogeneous sensors can be reliably correlated into coherent process instances, typically by matching event IDs, when the data is noisy, unordered, and incomplete.

Editorial extensions

If this is right

  • Process mining research should start from fragmented, unordered, and uncertain sensor streams rather than a single trustworthy log.
  • Mining algorithms should be split and hierarchically mapped across sensor, edge, and cloud layers according to the resources of each device.
  • Privacy protection moves from publication-time anonymization to collection-time mechanisms embedded in abstraction and data exchange.
  • New privacy metrics and new visual representations are needed because no single entity observes a full process case.
  • Evaluation of these techniques requires algorithm engineering, combining formal analysis with empirical simulation and benchmarking under distributed constraints.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial: The fragment-based approach faces its hardest test when event IDs are absent or ambiguous, as in raw location streams; a practical system may need a separate correlation layer that infers case identity from spatiotemporal co-occurrence.
  • Editorial: The six research fields are coupled, so privacy-preserving abstraction will directly constrain what process models can be discovered; the utility-privacy trade-off could be formalized as an optimization problem over abstraction and exchange choices.
  • Editorial: A concrete benchmark would be to run the distributed fragment pipeline on existing centrally collected sensor datasets and compare the merged graph against the centrally computed graph, thereby isolating the cost of decentralization.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper is a position and research-agenda paper for process mining on distributed data sources. It motivates the topic with three use cases (logistics, healthcare, and smart manufacturing), identifies three shifts (offline to online, centralized to distributed, event logs to sensor data), and proposes six research fields: edge processing, scalability, abstraction, privacy, explainability, and visualization. The paper argues for an algorithm-engineering methodology and for treating infrastructure, data, and user perspectives as interwoven. It does not present experiments, algorithms, or formal models; its contribution is a synthesis and roadmap for future research.

Significance. The paper addresses a genuine and timely gap: classical process mining assumes centralized, structured event logs, while IoT and distributed sensor deployments violate this assumption. The three use cases are concrete and well referenced, and the paper is appropriately careful to phrase most claims as aims and open questions. Its main value is in structuring the problem space and in making the case that privacy, scalability, and abstraction must be considered jointly. If the distributed fragment-merging vision is to be realized, however, the paper must confront cross-source event correlation; the current roadmap omits this as an explicit research field. As a roadmap, the paper is useful for orienting the community, but its central technical premise currently rests on an unanalyzed assumption.

major comments (3)
  1. [§3.1 and Table 2] The only concrete architectural mechanism proposed in the paper is fragment merging across sensors 'matched, for example, via event IDs.' This assumes that events observed by different sensors can be correlated into coherent process instances and that fragments can be merged stepwise into a global data-flow graph. The paper neither analyzes the conditions under which such matching is possible nor lists cross-source event correlation among the six research fields in Table 2. The motivating scenarios involve noisy and uncertain data (e.g., RTLS transponder readings with ~3-second resolution in §2.2, triangulated flight positions in §2.1), where event IDs may be missing, ambiguous, or unordered. Since this correlation step is a prerequisite for the fragment-based pipeline, it should be made an explicit research question, or the paper should state that fragment merging is only one of several conjectural paths.
  2. [§4 and §3 (first paragraph)] The proposed step-wise research strategy begins with the simplifying assumption that event data are 'fully ordered and reliably grouped per case,' yet §3 opens by stating that in distributed sensor settings 'the traditional assumptions on data in process mining ... no longer hold.' The paper does not say which of the six research fields is responsible for relaxing this grouping assumption, nor how the simplification relates to the partial, unordered data that motivates the agenda. This internal tension should be resolved by treating case and event grouping across sources as a first-class open problem rather than as a starting assumption.
  3. [§3.1 and §3.3] The relationship between local abstraction and fragment merging is unspecified. If abstraction to activities happens locally at each sensor before fragments are built, then the event IDs and relationships in a fragment depend on local abstraction decisions; if abstraction happens after merging, fragments must be built from raw low-level events. The paper discusses abstraction as a research field (§3.3) and fragment merging as an edge-processing mechanism (§3.1), but never states which ordering is intended or why. This ordering affects the privacy, scalability, and explainability trade-offs that the paper claims to address, so it should be clarified or explicitly left as an open design choice.
minor comments (6)
  1. [§2.2] 'The data first needs to be abstract to be interpreted' should be 'abstracted'.
  2. [§3.5] The research question heading contains a typo: 'throught out' should be 'throughout'.
  3. [§3.3] 'May comprises changes' should be 'may comprise changes'.
  4. [§3.5] 'Nodes are usually equipped with low batches for storing' is unclear; 'low bandwidth' or 'limited storage' was likely intended.
  5. [§3.4] The research question 'What are relevant adversary models and respective privacy threats when process mining on distributed data sources?' is grammatically incomplete; insert 'applied to' or rephrase.
  6. [§1 and §3.1] The novelty claim in the introduction ('we introduce the concept') should be reconciled with the citation to EdgeMiner [40], which is described as distributed process mining at the data sources; the paper should clarify what is new relative to this prior work.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a research roadmap whose agenda is supported by external literature and domain cases, with no fitted inputs or derived predictions.

full rationale

This paper is a position and roadmap paper, not a derivation. It contains no equations, no fitted parameters, and no quantity that is first fit to a subset of data and then 'predicted.' The central assertion—that traditional centralized event-log assumptions must be replaced by decentralized, online, sensor-driven process mining—is supported by domain scenarios (logistics, healthcare, manufacturing) and by literature citations, including several works by the authors (e.g., EdgeMiner [40], re-identification risk [36], unstructured-data challenges [48]). These citations are used descriptively, as examples of existing techniques or open challenges; none is invoked as a uniqueness theorem or as a premise that forces the roadmap's conclusions. The Edge Processing sketch in Section 3.1 assumes local fragments can be matched 'via event IDs' and merged stepwise, but this assumption is an unvalidated design premise, not a result derived from the paper's inputs; its fragility is a correctness or feasibility gap, not circularity. Likewise, the 'three shifts' and six research fields are a synthesis of the cited literature, not a renamed empirical pattern presented as a derivation. No circular step can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper uses no fitted numbers and introduces no physical or formal entities. Its 'process fragments' and 'compound fragments' are organizational concepts for a proposed architecture, not independently evidenced entities. The supporting assumptions are the ones listed above.

assumptions (4)
  • domain assumption Traditional process mining assumes a central, static, totally ordered, and accurate event log, and this assumption is no longer sufficient for sensor-rich distributed environments.
    This is the framing of the whole paper, stated in the opening of Section 3: 'A fundamental assumption in traditional process mining is the existence of a central event log.'
  • domain assumption Low-level sensor data can be abstracted into process-level events without losing the information needed for meaningful mining.
    The abstraction research field in Section 3.3 relies on this; the paper cites prior work for individual methods but does not prove that abstraction across distributed sources preserves case and activity semantics.
  • ad hoc to paper Events observed by different sensors can be correlated, for example via event IDs, and merged into a global process graph.
    Section 3.1's edge-processing proposal assumes this correlation and stepwise fragment merging; no mechanism for handling missing or ambiguous event IDs is provided.
  • domain assumption Processing close to the data source yields privacy, scalability, and online responsiveness without unacceptable degradation of analysis quality.
    The motivation in the Section 3 introduction states these benefits; trade-offs are acknowledged, but the assumption is not tested.

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Cite this review

Pith. "Pith review of Process Mining on Distributed Data Sources." pith.science (2026). https://pith.science/paper/FQY6L22V

@misc{pith2026250602830,
  author       = {Pith},
  title        = {Pith review of: Process Mining on Distributed Data Sources},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FQY6L22V}},
  note         = {Machine review of arXiv:2506.02830}
}
read the original abstract

Major domains such as logistics, healthcare, and smart cities increasingly rely on sensor technologies and distributed infrastructures to monitor complex processes in real time. These developments are transforming the data landscape from discrete, structured records stored in centralized systems to continuous, fine-grained, and heterogeneous event streams collected across distributed environments. As a result, traditional process mining techniques, which assume centralized event logs from enterprise systems, are no longer sufficient. In this paper, we discuss the conceptual and methodological foundations for this emerging field. We identify three key shifts: from offline to online analysis, from centralized to distributed computing, and from event logs to sensor data. These shifts challenge traditional assumptions about process data and call for new approaches that integrate infrastructure, data, and user perspectives. To this end, we define a research agenda that addresses six interconnected fields, each spanning multiple system dimensions. We advocate a principled methodology grounded in algorithm engineering, combining formal modeling with empirical evaluation. This approach enables the development of scalable, privacy-aware, and user-centric process mining techniques suitable for distributed environments. Our synthesis provides a roadmap for advancing process mining beyond its classical setting, toward a more responsive and decentralized paradigm of process intelligence.

Figures

Figures reproduced from arXiv: 2506.02830 by the authors.

Figure 1
Figure 1. Process mining on distributed data sources. [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Cross-cutting research fields in process mining on distributed sources, and induced [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗

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Pith tools

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