Collaboration Miner: Discovering Collaboration Petri Nets (Extended Version)

TL;DR

The evaluation shows that CM achieves its design goals: no assumptions on concepts and types as well as fitting and precise models, based on 26 artificial and real-world event logs from literature.

Abstract

Most existing process discovery techniques aim to mine models of process orchestrations that represent behavior of cases within one business process. Collaboration process discovery techniques mine models of collaboration processes that represent behavior of collaborating cases within multiple process orchestrations that interact via collaboration concepts such as organizations, agents, and services. While workflow nets are mostly mined for process orchestrations, a standard model for collaboration processes is missing. Hence, in this work, we rely on the newly proposed collaboration Petri nets and show that in combination with the newly proposed Collaboration Miner (CM), the resulting representational bias is lower than for existing models. Moreover, CM can discover heterogeneous collaboration concepts and types such as resource sharing and message exchange, resulting in fitting and precise collaboration Petri nets. The evaluation shows that CM achieves its design goals: no assumptions on concepts and types as well as fitting and precise models, based on 26 artificial and real-world event logs from literature.

Figures (2)

• Figure 1: Collaboration Petri net $cPN$ with all four collaboration types.
• Figure 2: $cPN$ discovered by CM and the composed RM_WF_net discovered by CCHP on log EM.

Theorems & Definitions (5)

• definition thmcounterdefinition: Workflow Collection
• definition thmcounterdefinition: Collaboration Pattern
• definition thmcounterdefinition: Collaboration Petri Net ($cPN$)
• definition thmcounterdefinition: Event Log
• definition thmcounterdefinition: Log Projection