Continuous query engine to detect anomalous electronic transactions patterns using bank cards

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Student

Martín Canfrán, Fernando

Document type

Master thesis

Date

2025

rights

Open AccessOpen Access

Publisher

Universitat Politècnica de Catalunya



Abstract

Nowadays data are in motion, change continuously and are –potentially– unbounded implying data sources that are also in constant evolution. From the point of view of data persistence, this reality breaks the usual paradigm of dynamic although stable data sources. Besides, the number of applications to help critical decision making in real time is also rapidly increasing. These two scenarios raise the need of re-thinking both the data and the query models to fit these new requirements. So that, under these circumstances, it seems that a continuously evolving data graph is a suitable data model to use and therefore to study and analyze. Thus, in this work, we tackled the problem of querying continuously evolving data graphs in a specific context: the context of ATM1 transactions, in particular anomalous ones. Under this context, evaluating continuous queries corresponds to recognizing patterns –usually associated with anomalous behaviors– in the volatile (evolving) subgraph of ATM transactions. To do so, we propose an evaluation process based on the so called dynamic pipeline computational model, a stream processing technique that facilitates the emission of alerts as soon as anomalous patterns are identified. Stream based bank applications that monitor ATM transactions are direct beneficiaries of our proposal since they can continuously query data graphs to get “fresh” data as they are produced, avoiding the computational overhead of having to discard non-valid data, as current systems work.
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