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

TL;DR

This paper introduces a method for detecting deviations in trading systems by comparing formal models (colored Petri nets) with actual system logs, enabling effective validation of complex distributed trading platforms.

Contribution

The paper presents a novel approach combining control-flow and data perspectives for deviation detection in trading systems using colored Petri nets and event logs.

Findings

Successfully validated a real-life trading system

Detected various types of deviations between model and logs

Enhanced understanding of system conformance

Abstract

Trading systems are software platforms that support the exchange of securities (e.g., company shares) between participants. In this paper, we present a method to search for deviations in trading systems by checking conformance between colored Petri nets and event logs. Colored Petri nets (CPNs) are an extension of Petri nets, a formalism for modeling of distributed systems. CPNs allow us to describe an expected causal ordering between system activities and how data attributes of domain-related objects (e.g., orders to trade) must be transformed. Event logs consist of traces corresponding to runs of a real system. By comparing CPNs and event logs, different types of deviations can be detected. Using this method, we report the validation of a real-life trading system.