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| Format: | Recurso digital |
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Zenodo
2019
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| Online Access: | https://doi.org/10.5281/zenodo.19202376 |
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Table of Contents:
- <p><span lang="EN-US">The increasing digitization of financial services by the late 2010s resulted in the generation of massive volumes of transactional data across payment systems, trading platforms, digital banking applications, and regulatory reporting pipelines. These transaction logs, originally designed for auditing, reconciliation, and failure recovery, gradually emerged as a valuable source of behavioral and operational insight. However, the scale, velocity, and structural heterogeneity of transactional logs posed significant challenges to traditional analytical techniques, which were often optimized for static datasets or narrowly defined reporting use cases. As a result, organizations began exploring systematic approaches to mine patterns from transaction logs in order to better understand system behavior, detect anomalies, and improve decision-making. Pattern mining from transaction logs refers to the process of discovering recurring structures, sequences, correlations, and deviations within recorded transactional events. By September 2019, this practice was informed by a combination of data mining research, distributed systems logging techniques, and operational analytics developed in large-scale production environments. Unlike conventional business intelligence queries, pattern mining emphasizes the identification of latent relationships and temporal structures that are not explicitly encoded in application logic. These patterns may reflect normal operational workflows, emergent system behaviors, or early indicators of faults, fraud, or performance degradation. In financial systems, transaction logs capture more than simple state changes; they encode regulatory-relevant actions such as authorization decisions, settlement progressions, risk evaluations, and ledger mutations. Mining patterns from these logs enables institutions to analyze end-to-end transaction lifecycles, correlate technical events with business outcomes, and identify systemic inefficiencies or vulnerabilities. Importantly, such analysis must operate within strict constraints related to data privacy, auditability, and regulatory compliance, distinguishing transaction log mining in financial domains from analogous practices in less regulated environments. This paper examines pattern mining from transaction logs as understood and applied by September 2019, situating it within the broader evolution of logging, distributed systems observability, and data mining research. It synthesizes academic literature and industry practices to propose a conceptual and architectural framework for extracting meaningful patterns from transactional data at scale. The analysis focuses on methodological considerations, architectural layering, and practical challenges encountered in regulated, high-throughput systems, while avoiding retrospective interpretations based on post-2019 technologies or techniques.</span></p>