# Super Variants

- Type: Conference paper
- URL: https://www.gyunam.com/publication/2024-bpm/
- Date: 2024-06-06
- Authors: Jan Niklas Adams, Emilie Hastrup-Kiil, Gyunam Park, Wil M. P. van der Aalst
- Venue: 22nd Business Process Management Conference 2024, BPM2024, Krakow, Poland, 1-6 September, 2024, Proceedings
- DOI: https://doi.org/10.1007/978-3-031-05760-1_10
- PDF: https://www.gyunam.com/publication/2024-bpm/paper.pdf
- Link (URL): https://doi.org/10.1007/978-3-031-05760-1_10
- Research lines: [Object-centric process mining](https://www.gyunam.com/research-line/object-centric-process-mining/index.md)

## Abstract

Process mining offers methods to analyze the actual controlflow behavior of a process. The two main available methods are process discovery and variant analysis. While process discovery aggregates all variants into one model, the models often suffer from high complexity and lack detailed granularity. Conversely, variants are simple and offer fine granularity but their sheer number makes them difficult to comprehend. To bridge this gap, we introduce the concept of super variants. These represent a middle ground between the complexity of discovered process models and the simplicity of variants, offering an aggregation of closely related variants. We propose a super-variant mining framework based on object-centric variants, evaluate its scalability, and demonstrate its utility through a practical use case. This new approach promises to enhance control-flow analysis by striking a new balance between complexity and aggregation.

## Practical problem

Process analysts who want to understand how a process really runs have two options, and both fail at scale. A discovered process model folds every observed behavior into one diagram, which quickly becomes too complex to read or too loose to be informative. A variant list shows each distinct sequence of activities plainly, but real logs contain hundreds or thousands of variants, far more than a person can take in. Trace clustering groups similar behavior top-down, yet the clusters cannot be traced back to the individual variants, so an analyst cannot drill from a cluster with poor performance down to the concrete behavior that causes it.

## Research questions

1. How can closely related variants be aggregated into a representation that is more compact than a variant list but simpler than a discovered process model?
2. How can such aggregations be mined automatically from object-centric variants and arranged in a hierarchy that connects behavioral clusters to individual variants?
3. Is super variant mining computationally feasible on real event logs, and does it yield meaningful aggregation on a real process?

## Contributions

- The concept of super variants: object-centric variants extended with choice (including optionality) and object multiplicity through super objects, with an equivalence notion reducible to labeled graph isomorphism.
- A super variant mining framework with intra-variant summarization (generating potential super variants and a hitting-set selection) and inter-variant summarization (a Levenshtein-based distance, a matching problem with a non-assignment penalty, recursive merging into a hierarchy).
- A visualization that extends the object-centric variant lane notation with star-marked super objects, vertically split chevrons for choices and dotted borders for optional activities.
- An open-source implementation on top of the `ocpa` library, released on GitHub (EmilieHK/Object-Centric-Super-Variants).
- A proof of concept on a public procure-to-pay OCEL 2.0 event log and a scalability evaluation that decomposes computation time by framework step.

## Key findings

- In the running order-to-cash example, ten traditional variants reduce to four object-centric variants, then to two super variants and finally one super variant with an arbitrary number of items and an optional clearance activity.
- On the procure-to-pay log (516 process executions, 314 object-centric variants, 5,128 objects of 7 types), four variants merge over three hierarchy levels into one super variant covering 20% of the control-flow behavior, adding only a material super object and one choice between approving and delegating a purchase order.
- In the scalability study (five hierarchies of five levels from 32 random variants), intra-variant summarization time is dominated by generating potential super variants and grows with the number of generated summarizations and of events per variant.
- Inter-variant distance calculation and matching cost rise with the number of super variants per level, and at the final level inter-variant summarization dominates all other steps, making it the scalability bottleneck for large logs.
- Limitations: super variants with multiplicity leave open whether objects are batched, and the added value for end users has not yet been evaluated.