Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

Abstract

Conformance checking methods diagnose to which extent a real system, whose behavior is recorded in an event log, complies with its specification model, e.g., a Petri net. Nonetheless, the majority of these methods focus on checking isolated process instances, neglecting interaction between instances in a system. Addressing this limitation, a series of object-centric approaches have been proposed in the field of process mining. These approaches are based on the holistic analysis of the multiple process instances interacting in a system, where each instance is centered on the handling of an object. Inspired by the object-centric paradigm, this paper presents a replay-based conformance checking method which uses a class of colored Petri nets (CPNs) -- a Petri net extension where tokens in the model carry values of some types (colors). Particularly, we consider conservative workflow CPNs which allow to describe the expected behavior of a system whose components are centered on the end-to-end processing of distinguishable objects. For describing a system’s real behavior, we consider event logs whose events have sets of objects involved in the execution of activities. For replay, we consider a jump strategy where tokens absent from input places of a transition to fire move from their current place of the model to the requested places. Token jumps allow to identify desire lines, i.e., object paths unforeseen in the specification. Also, we introduce local diagnostics based on the proportion of jumps in specific model components. The metrics allow to inform the severity of deviations in precise system parts. Finally, we report experiments supported by a prototype of our method. To show the practical value of our method, we employ a case study on trading systems, where orders from users are matched to trade.

Searching for Deviations in Trading Systems: Combining Control-Flow and Data Perspectives

“…Finally, we define restrictions for CPNs in order to model processes in trading systems that handle different kinds of objects such as buy/sell orders [6,7]. We call conservative-workflow CPNs the models that comply with such restrictions.…”

“…For example, new buy order has a measure of 0.5, meaning that 5 out of 10 objects processed by the activity complied with the model path. We refer to [6] for formal definitions and a further discussion about these local measures.…”

“…Deviations are found when a model cannot be executed as an event indicates. In [6] we consider deviations related only to control-flow, proposing a strategy to force the model execution when such deviations are found. In [7] we use replay to check if data attributes of objects are transformed by a real system in the same way that its model does.…”

“…This paper presents a comprehensive conformance method that integrates and extends the approaches presented in [6,7] in order to detect multiple kinds of deviations, including those related to control-flow and data attributes of objects. Notably, strategies are provided to force the execution of a CPN upon the occurrence of each kind of deviation.…”