Conformance Checking and Performance Analysis Using Object-Centric Directly-Follows Graphs

Gyunam Park, Jan Niklas Adams, Wil M. P. Van Der Aalst
June, 2024
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Abstract

Traditional process mining often simplifies the multi-object reality of business processes, linking each event to a single object called “the case”. This oversimplification may lead to inaccurate analyses, caused by missing interactions between object types. In contrast, object-centric process mining permits events to be tied to multiple objects, capturing complex interactions and providing a more accurate representation of business processes. This paper introduces an approach for supporting object-centric process mining utilizing Object-Centric Directly-Follows Graphs (OC-DFGs). Despite their advantages, e.g., simplicity, OC-DFGs have been relatively untapped for essential process mining tasks, such as conformance checking and performance analysis. In order to address this, our research presents a comprehensive approach for OC-DFG-based conformance checking and performance analysis. We fully implement the proposed approach as a web application and demonstrate the use of OC-DFGs for these tasks within a case study of a real-life loan application process.

Type
Conference paper
Publication
22nd Business Process Management Forum 2024, BPM Forum2024, Krakow, Poland, 1-6 September, 2024, Proceedings

Practical problem

Process analysts in commercial tools work mostly with directly-follows graphs: simple process maps that show which activity tends to follow which, and that can be filtered interactively even on logs with millions of events. Real processes, however, involve several kinds of business objects at once, such as a loan application and the multiple offers attached to it, and forcing each event onto a single case duplicates events and hides interactions. Object-centric directly-follows graphs keep the simplicity while showing each object type as its own layer, but until now they could only be discovered, not used to check where reality deviates from the model or to locate delays. Analysts who wanted those answers had to switch to heavier formalisms such as object-centric Petri nets, which are harder to read and configure.

Research questions

  1. How can conformance checking based on alignments be performed and visualized on object-centric directly-follows graphs?
  2. How can those alignments be used to compute object-centric performance measures such as synchronization, pooling and lagging time in the context of the model?

Contributions

  • A conformance checking approach that projects an object-centric directly-follows graph per object type, translates each projection into a workflow net, aligns the projected event log, and maps log and model moves back onto the graph.
  • A visualization scheme distinguishing five kinds of log moves (dotted edges, self-loops) and model moves (double-line edges) on the object-centric graph.
  • A performance analysis approach that projects alignments onto the model, builds per-event relationships to preceding events, and computes sojourn, synchronization, flow, lagging and pooling time.
  • Explori, an open-source web application on top of the ocpa Python library (GitHub gyunamister/Explori) with log management, discovery, conformance checking and performance analysis.
  • A case study applying the approach to the BPI Challenge 2017 loan application process with applications and offers as object types.

Key findings

  • On a loan application log of 31,010 events, 2,302 applications and 5,128 offers, the alignments on the object-centric directly-follows graph reveal 548 applications skipping submit application and 325 applications needing an extra handle leads activity.
  • For offers, 274 are cancelled immediately after creation, bypassing send offer and call after offers; 261 offers show an extra return offer and 107 applications an extra call incomplete files after cancellation.
  • The average sojourn time before cancel application is about 26 days and 23 hours, with a synchronization time of 5 days and 7 hours across the three preceding activities.
  • Offers pool for 5 days and 6 hours on average while the lagging time of offers is only 4 minutes versus 5 days and 6 hours for applications, showing that applications are cancelled only after all offers are consolidated.
  • The decompose-and-recombine design can yield incoherent global alignments when object types disagree on a shared activity, and the sequential workflow-net translation misrepresents concurrency in performance figures.