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arxiv: 2412.00393 · v2 · submitted 2024-11-30 · 💻 cs.DB

Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations

Shahrzad Khayatbashi , Najmeh Miri , Amin Jalali This is my paper

Pith reviewed 2026-05-23 08:38 UTC · model grok-4.3

classification 💻 cs.DB
keywords processoperationsobject-centricdatagranularitymininganalystsevent
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The pith

Defines and implements four granularity-adjusting operations for object-centric event logs, validated on educational and BPI challenge datasets with reported gains in model precision and fitness.

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

Process mining turns event logs into models of how work happens. Object-centric versions track multiple objects and their links, which makes models more realistic but harder to read at one fixed level of detail. The paper defines four operations that let users zoom in (drill-down), zoom out (roll-up), or reshape the view (unfold and fold) while keeping the object links intact. The operations are written down formally and coded in an open Python library. When tested on four years of university learning-system data for about 400 students, the resulting models matched the recorded events better and were more precise. The same operations stayed fast on large public business datasets. This gives analysts a practical way to move between coarse overviews and fine-grained views without rebuilding the log each time.

Core claim

The four operations enable analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction, with demonstrated improvements in the precision and fitness of the discovered models on real-world OCEL data.

Load-bearing premise

That the formal definitions of drill-down, roll-up, unfold, and fold correctly preserve the semantics and object interactions of the original OCEL when granularity is changed, and that observed improvements in precision and fitness are caused by these operations rather than by other modeling choices or data properties.

Figures

Figures reproduced from arXiv: 2412.00393 by Amin Jalali, Najmeh Miri, Shahrzad Khayatbashi.

Figure 1
Figure 1. Figure 1: The use of Drill-down and Unfold operations to enable identifying more detailed [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of student and group distributions [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of precision and fitness for discovered object-centric Petri nets [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The relation between students and two groups with the lowest fitness score show [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
read the original abstract

Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among events and multiple objects, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis prevents users from leveraging the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four operations: drill-down, roll-up, unfold, and fold, which enable analysts to change the granularity of analysis when working with Object-Centric Event Logs (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We formally define these operations and implement them in an open-source Python library. To validate their utility, we applied the approach to real-world OCEL data extracted from a learning management system, covering a four-year period and approximately 400 students, as a case of object-centric educational process mining. This case study shows significant improvements in the precision and fitness of the discovered models after applying the operations. In addition, we evaluate the scalability of the operators on large, publicly available OCELs derived from the Business Process Intelligence Challenge datasets, demonstrating that the operations remain computationally feasible on industrial-scale event logs. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through flexible granularity adjustments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal rests on the domain assumption that object-centric event logs admit well-defined hierarchical granularity changes without loss of object-interaction semantics; no free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption Object-centric event logs admit well-defined hierarchical granularity changes via the four named operations without loss of object-interaction semantics.
    Invoked when the paper states that the operations allow seamless transitions between detailed and aggregated models.

pith-pipeline@v0.9.0 · 5799 in / 1308 out tokens · 33796 ms · 2026-05-23T08:38:04.248874+00:00 · methodology

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

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