Zoom In, Zoom Out: New Data Operations Make Process Mining Flexibly Granular

Every organization runs on processes, but the digital footprints those processes leave behind are notoriously difficult to interpret at the right level of detail. A hospital information system records that a test was ordered and a result registered, yet it often hides the fact that clinicians think in terms of broader categories such as an ECG test or a blood test. A learning platform logs that an assignment was submitted and a grade set, but merges students and teachers into a single undifferentiated user type. The result is that process discovery algorithms, which reconstruct workflow models from event logs, frequently produce models that are either too abstract to reveal anything useful or so fine-grained that they drown analysts in detail. A new study published in Knowledge and Information Systems by Shahrzad Khayatbashi of Linköping University, together with Najmeh Miri and Amin Jalali of Stockholm University, tackles this granularity problem head-on with a set of formally defined, open-source operations that let analysts zoom in and out of object-centric process data on demand.

The work builds on Object-Centric Event Data, a modern way of recording process behavior that captures how a single event involves multiple objects at once. In a hospital, the event of registering a test may simultaneously concern a patient, a caregiver, and a specific diagnostic procedure such as a 12-lead ECG or a fasting blood glucose test. The widely adopted Object-Centric Event Log standard, now in its 2.0 specification, stores these many-to-many relationships between events and objects, along with object-to-object relations and attributes that can change over time. This richness is precisely what allows object-centric process mining to discover far more realistic models than the traditional single-case logs that dominated the field for two decades. But it also creates a new challenge: the algorithms that transform these logs into visual models, such as Object-Centric Directly-Follows Graphs, operate at a fixed level of abstraction, deriving process logic from the sequence of event types associated with each object type over time.

The consequence of that fixed abstraction is illustrated by the researchers’ hospital running example. When a caregiver orders an ECG test, registers the result, and then orders a blood test as a standard follow-up procedure, the log faithfully records each concrete action. Yet because all tests share the same generic event types and the same Test object type, the discovered model shows only a flat loop of ordering and registering tests. The clinically meaningful pattern, in which a concerning ECG result triggers a blood test, remains invisible. The authors’ solution borrows a page from the world of Online Analytical Processing, the multidimensional analysis paradigm behind business intelligence tools like Excel pivot tables and data cubes. Van der Aalst proposed the idea of process cubes with slice, dice, drill-down and roll-up operations as early as 2013, but those early implementations predated object-centric process mining and could not handle events tied to multiple objects. The new work brings the OLAP vision fully into the object-centric era.

Four operations form the core of the contribution. Drill-down refines object types by splitting a generic type into variants based on an attribute value, so a Test object type can be decomposed into 12-lead ECG, Monitoring ECG, Fasting Blood Glucose Test and Arterial Blood Gas Test. The operation can be applied recursively and, importantly, comes in a time-aware variant that uses the historical values of object attributes, meaning an object can belong to different drilled-down types at different points in its lifecycle. Roll-up is the inverse: it merges several fine-grained object types into a coarser category, such as grouping the two ECG variants into an ECG Test category that matches how clinicians actually reason. Unfold operates on event types rather than object types, making implicit event-object associations explicit by projecting each event type into a combination of the event type and the object type it concerns, so the generic order test becomes order ECG Test or order Blood Test. Fold collapses those unfolded variants back into their original generic labels.

Each operation is accompanied by a formal algorithm that manipulates the components of the OCEL 2.0 structure, extending sets of object or event types, reassigning the type functions, and updating attribute type mappings while preserving all existing event-to-object and object-to-object relations. The structural connectivity of the log is never broken, which means analysts can chain operations freely: drill down into tests, unfold the ordering and registration events, fold selected variants back together, and roll up categories, all without reloading or reconstructing the data. The authors have implemented everything in an open-source Python library called processmining, installable with a single pip command, and released the running example, case study data and evaluation code on GitHub to support reproducibility.

To demonstrate real-world value, the team applied the approach to four years of educational process data from a Business Process Management course at Stockholm University, extracted from the Moodle learning management system following their OCPM2 methodology. The dataset covered 401 students organized into 91 groups, with between roughly 38,000 and 61,000 events per year and tens of thousands of event-to-object relations. In its raw form, the discovered process model was nearly useless: students and teachers were conflated under a single user object type, all assignments were lumped into one assign type, and no individual task could be distinguished. After drilling down on the user role and assignment name attributes and unfolding the relevant events, the model blossomed into a detailed flow of five clear milestones, revealing exactly which student in each group submitted each lab, how teachers graded individually even for group work, and how many students needed grade updates after failing a first attempt.

The case study also surfaced findings with practical consequences for course design. The final, optional process mining assignment attracted only 13 submissions compared with 23 for an earlier lab, and grading touched just 28 students against 134 for the first milestone. Follow-up investigation showed the optional module was scheduled close to the final exam, leading most students to prioritize exam preparation. More strikingly, the detailed models revealed that resubmission attempts for failed labs were entirely absent from the digital record, indicating those submissions were handled offline, an actionable gap for anyone seeking to improve the educational process. Quantitatively, the team discovered object-centric Petri nets before and after transformation and computed fitness and precision for 71 of the 91 groups, finding significant improvements for most groups after drilling down and unfolding. For the handful of groups with poor fitness, converting the logs into temporal Event Knowledge Graphs exposed the culprit: heavy rolling group membership, with one group losing 11 of its students to other groups during the course, something current object-centric techniques cannot account for because they model object relations as static.

The error analysis added a further layer of insight. Twenty groups defeated the conformance checking library entirely, and ten of those came from 2023, the year the course introduced optional tracks that made the process less structured. Point-biserial correlations showed that model complexity metrics, including the number of places, transitions, arcs, silent transitions, AND-splits and AND-joins, all correlated moderately and significantly with conformance checking failures, with synchronization-heavy parallel constructs exerting the strongest influence while XOR-based choice structures played a secondary role. This points to a broader need for more scalable and robust conformance checking algorithms for object-centric Petri nets, a limitation the authors candidly acknowledge as a threat to validity since the operations themselves applied successfully to all 91 groups.

Scalability was tested on publicly available object-centric event logs derived from the Business Process Intelligence Challenge datasets of 2014, 2016, 2017 and 2019, ranging from about seven hundred thousand to more than seven million events. The researchers drilled down on the most frequent object type using synthetic attributes with between 2 and 10 categories and unfolded all strongly associated event types. The number of categories had little effect on performance; what mattered was log size and connectivity. For the three smaller logs, unfold calls took roughly 1.5 to 3.2 seconds each, while the massive BPIC 2016 log, with its enormous number of event-to-object relations, required around 29 seconds per unfold call and about 60 seconds for drill-down. Crucially, the dominant cost for that log was simply loading it into the PM4Py library, an order of magnitude slower than the operations themselves. This finding underscores why in-place abstraction matters: being able to roll up or fold without reloading makes interactive analysis feasible on industrial-scale data.

The operations have already proven their versatility beyond education, having been applied in the insurance domain, providing a form of methodological triangulation across settings. Because object-centric event data can be transformed bidirectionally between OCEL and temporal Event Knowledge Graphs, the same OLAP operations can be combined with graph queries, centrality measures and community detection, letting analysts exploit the strengths of both representations. The authors frame their contribution as a foundation for more dynamic and detailed process mining methodologies, and they chart clear future directions: scalable conformance checking for object-centric models, better handling of dynamic object-to-object relationships during discovery, and integration of the operations into mainstream process mining tools. For organizations drowning in multi-dimensional event data, the message is simple and compelling: the right insight may already be in your log, waiting at the right level of zoom.

Subject of Research: Granularity-adjusting OLAP operations for object-centric process mining on Object-Centric Event Logs

Article Title: Advancing object-centric process mining with multi-dimensional data operations

Article References: Khayatbashi, S., Miri, N., & Jalali, A. (2026). Advancing object-centric process mining with multi-dimensional data operations. Knowledge and Information Systems, 68(1), Article 275. https://doi.org/10.1007/s10115-026-02904-0

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02904-0

Keywords: object-centric process mining, Object-Centric Event Log, OLAP operations, drill-down, roll-up, process discovery, event knowledge graphs, educational process mining, conformance checking, scalability, OCEL 2.0, business process management