Pre-processing Network Messages of Trading Systems into Event Logs for Process Mining

Carrasquel, Julio Cesar; Chuburov, Sergey A.; Lomazova, Irina A.

doi:10.1007/978-3-030-71472-7_7

Cited by 3 publications

(1 citation statement)

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“…its speci cation. In this regard, we refer the reader to papers [33][34][35][36], which present our studies on the extraction of event logs and Petri net-based modeling of real-world trading systems. However, in the method presented in [32], the replay of a trace is stopped upon the occurrence of the rst deviating event, and also local diagnostics on system components are not provided.…”

Section: Related Workmentioning

confidence: 99%

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

Карраскель¹,

Мешерауи

2021

Model. anal. inf. sist.

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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.

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Section: Related Workmentioning

confidence: 99%