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Federated Conformance Checking

Majid Rafiei, Mahsa Pourbafrani, Wil M. P. van der Aalst

TL;DR

This work addresses conformance checking in federated, inter‑organizational settings under privacy constraints. It proposes a privacy‑aware federated conformance checking framework built on private open nets and public models, using local alignments, local communication costs, and a collaborative event log to compute federated alignment costs. The approach is formalized with definitions of event logs, Petri nets, and alignments, and demonstrated through a running supply‑chain example and synthetic experiments that inject miscommunications to reveal cross‑organizational issues. The results show that local analyses expose internal misalignments, while federated analyses uncover cross‑organizational miscommunications such as asynchronous messaging, enabling confidential yet effective process improvement across partners.

Abstract

Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN. Conformance checking enables identifying deviations, anomalies, or non-compliance instances. It offers different perspectives on problems in processes, bottlenecks, or process instances that are not compliant with the model. Performing conformance checking in federated (inter-organizational) settings allows organizations to gain insights into the overall process execution and to identify compliance issues across organizational boundaries, which facilitates process improvement efforts among collaborating entities. In this paper, we propose a privacy-aware federated conformance-checking approach that allows for evaluating the correctness of overall cross-organizational process models, identifying miscommunications, and quantifying their costs. For evaluation, we design and simulate a supply chain process with three organizations engaged in purchase-to-pay, order-to-cash, and shipment processes. We generate synthetic event logs for each organization as well as the complete process, and we apply our approach to identify and evaluate the cost of pre-injected miscommunications.

Federated Conformance Checking

TL;DR

This work addresses conformance checking in federated, inter‑organizational settings under privacy constraints. It proposes a privacy‑aware federated conformance checking framework built on private open nets and public models, using local alignments, local communication costs, and a collaborative event log to compute federated alignment costs. The approach is formalized with definitions of event logs, Petri nets, and alignments, and demonstrated through a running supply‑chain example and synthetic experiments that inject miscommunications to reveal cross‑organizational issues. The results show that local analyses expose internal misalignments, while federated analyses uncover cross‑organizational miscommunications such as asynchronous messaging, enabling confidential yet effective process improvement across partners.

Abstract

Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN. Conformance checking enables identifying deviations, anomalies, or non-compliance instances. It offers different perspectives on problems in processes, bottlenecks, or process instances that are not compliant with the model. Performing conformance checking in federated (inter-organizational) settings allows organizations to gain insights into the overall process execution and to identify compliance issues across organizational boundaries, which facilitates process improvement efforts among collaborating entities. In this paper, we propose a privacy-aware federated conformance-checking approach that allows for evaluating the correctness of overall cross-organizational process models, identifying miscommunications, and quantifying their costs. For evaluation, we design and simulate a supply chain process with three organizations engaged in purchase-to-pay, order-to-cash, and shipment processes. We generate synthetic event logs for each organization as well as the complete process, and we apply our approach to identify and evaluate the cost of pre-injected miscommunications.
Paper Structure (18 sections, 4 equations, 10 figures, 7 tables)

This paper contains 18 sections, 4 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Federated Process Mining
  4. Privacy-Aware Federated Process Mining
  5. Preliminaries
  6. Event Log
  7. Petri Nets
  8. Conformance Checking
  9. Approach
  10. Local Conformance Checking
  11. Local Communication Costs
  12. Communication Between Public Process Models
  13. Running Example
  14. Privacy Considerations
  15. Experimental Results
  16. ...and 3 more sections

Figures (10)

  • Figure 1: A system net that models the behavior seen in Table \ref{['tbl:sample_event_log']}. $P{=}\{start,p1,p2,p3,p4,p5,end\},T{=}\{t1,t2,t3,t4,t5\}$, $F{=}\{(start,t1),(t1,p1),(p1,t2),(t2,p2),(t2,p3),(p2,t3),(t3,p4), (p3,t4),(t4,p5),(p4,t5),(p5,t5),(t5,end) \}, M_{init} {=} [start],\\ M_{final}=[end]$.
  • Figure 2: General conceptual framework of federated conformance checking.
  • Figure 3: A private process model corresponding to the private event log shown in Table \ref{['tbl:private_event_log']}. $I = \{io2\}$, $O = \{ io1,io3 \}$, $T_{int} = \{ m1\_t1,m1\_t3 \}$, and $T_{com} = \{ m1\_t2,m1\_t4,m1\_t5 \}$.
  • Figure 4: The system net obtained from Figure \ref{['fig:private_process_model']} by replacing the label of communication transition $m1\_t2$ with $\tau_{out}$ as an output label and applying the $inner(.)$ function.
  • Figure 5: A private process model corresponding to the private event log shown in Table \ref{['tbl:private_event_log_supplier']}. $I = \{io1,io3\}$, $O = \{ io2 \}$, $T_{int} = \{ s1\_t2,s1\_t3,s1\_t4 \}$, and $T_{com} = \{ s1\_t1,s1\_t5,s1\_t6 \}$.
  • ...and 5 more figures

Theorems & Definitions (19)

  • definition thmcounterdefinition: Event
  • definition thmcounterdefinition: Event Log
  • definition thmcounterdefinition: Trace, Simple Event Log
  • definition thmcounterdefinition: Applying Functions to Sequences
  • definition thmcounterdefinition: Petri Net
  • definition thmcounterdefinition: Labeled Petri Net
  • definition thmcounterdefinition: System Net
  • definition thmcounterdefinition: System Net Traces
  • definition thmcounterdefinition: Legal Moves
  • definition thmcounterdefinition: Alignment
  • ...and 9 more