Analytics Architecture Research Articles

The Lakehouse: State of the Art on Concepts and Technologies

In the context of data analytics, so-called lakehouses refer to novel variants of data platforms that attempt to combine characteristics of data warehouses and data lakes. In this way, lakehouses promise to simplify enterprise analytics architectures, which often suffer from high operational costs, slow analytical processes and further shortcomings resulting from data replication. However, different views and notions on the lakehouse paradigm exist, which are commonly driven by individual technologies and varying analytical use cases. Therefore, it remains unclear what challenges lakehouses address, how they can be characterized and which technologies can be leveraged to implement them. This paper addresses these issues by providing an extensive overview of concepts and technologies that are related to the lakehouse paradigm and by outlining lakehouses as a distinct architectural approach for data platforms. Concepts and technologies from literature with regard to lakehouses are discussed, based on which a conceptual foundation for lakehouses is established. In addition, several popular technologies are evaluated regarding their suitability for the building of lakehouses. All findings are supported and demonstrated with the help of a representative analytics scenario. Typical challenges of conventional data platforms are identified, a new, sharper definition for lakehouses is proposed and technical requirements for lakehouses are derived. As part of an evaluation, these requirements are applied to several popular technologies, of which frameworks for data lakes turn out to be particularly helpful for the construction of lakehouses. Our work provides an overview of the state of the art and a conceptual foundation for the lakehouse paradigm, which can support future research.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

Exploring Analytics in SAP S/4HANA Cloud: Capabilities, Integration, and Business Value

With the accelerated adoption of modern cloud- based enterprise resource management (ERP) systems, organizations recognize the importance of integrated analytics capabilities to drive data driven business insights and gain a competitive edge. This study explores the real-time analytics architecture of SAP S/4HANA Cloud, a leading cloud ERP system, and its potential to improve decision-making and enhance operational excellence. Existing research has primarily focused on analytics in the conventional S/4HANA system. Thus, technical documentation, industry blogs, and implementation expertise are used to provide an overview of SAP S/4HANA Cloud’s analytics capabilities, including architecture, data modeling, extensibility, business value, and external integrations. Research shows that the SAP S/4HANA Cloud provides applications for monitoring KPIs, generating reports, visualizing data using embedded SAP Analytics Cloud (SAC), and seamless OData integration. Technology leaders will gain valuable perspectives on how modern cloud ERPs like S/4HANA Cloud, with their unified analytics capabilities drive faster response times to market changes and optimize costs through insights influencing the evolution of intelligent enterprise systems. Key Words: SAP S/4HANA Cloud, embedded analytics, embedded SAC, Cloud ERP, SAP HANA, S/4HANA Cloud analytics

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

Racialization as a Strategic Orientation: Arab Organizations and the Construction of Ethnic Identity in the Cold War Era

Arab Americans are fertile ground for scholars interested in studying processes of racialization and race-making. The ambiguous, or “in-between,” racial status of this population has caused some obstacles for scholars attempting to theorize the source and persistence of discrimination against this group. This article attempts to address these paradoxes by examining the history of Arab ethnic and racial activism in the civil rights/Cold War period and uses this empirical case to argue for an historical interpretation of Arab racialization. Specifically, this article asks two questions: “What international and historical contexts shaped the development of Arab ethnic identity in the United States?” and “How do these historical mechanisms inform and amend current theories of Arab racialization?” To answer these, the article employs a “theoretical frontier” analytic architecture to analyze archival sources documenting Arab ethnic advocacy and organizing strategies during the critical civil rights/Cold War period. The article finds that prominent Arab organizations and their leaders navigated a hostile American public that levied both politically and ethnically motivated attacks against their advocacy, and argues that this historical context in turn shaped later Arab organizations’ approach to formal recognition as an ethnic and racial group. Ultimately, the article argues that racialization—in this case, the decision by Arab organizations beginning in the mid-1970s to pursue a project of Arab ethnic advocacy disarticulated from its political origins—was an historical development that requires reckoning with within the theoretical literature on Arab ethnic formation.

Educational Data Analytics and Fog Computing in Education 4.0

Universities are generating massive amounts of educational data. Most universities are now focusing on how to harness that data to optimize and visualize it to provide better and more extended education services. Given this scenario, a literature review was used to conduct this study guided by the following objectives: (1) Assess suitable fog computing and educational data analytics architectures; (2) Examine the opportunities offered by fog computing and educational data analytics; (3) Investigate fog computing and educational data analytics challenges; and (4) Examine disruptions and future directions of these technologies in Education 4.0. The study concludes that institutions must use integrated data analytics techniques and distributed technology systems to make decisions about administration, resource allocation, student retention, performance, and improvement strategies. The study also identified the challenges of using fog computing and educational data analytics and concludes that education 4.0 is a learning style that is aligned with the fourth industrial revolution, requiring transformational learning readiness.

An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

Perturbed‐analytic direct transcription for optimal control (PADOC)

AbstractThe herein coined PADOC (perturbed‐analytic direct transcription for optimal control) stands for a new transcription method for direct trajectory optimization. We construct PADOC on a novel segmented decomposition method that provides series solutions for nonlinear problems. To transcribe the infinite dimensional problem into a finite one, PADOC comes along with a new solution approach, namely, the herein coined average nonlinear programming (aNLP). The aNLP theorem suggests generating a staircase optimal solution (low resolution) and turning it into a distributed optimal solution (high resolution) by exploiting the Hamiltonian of the problem. The analytic architecture provides PADOC with an analytic connection of a truncated order between the discrete nodes of the solution. This renders stability, accuracy within an analytic resolution, robustness, and a cut‐down in the number of the decision variables in the frame of NLP. We also prove that the multipliers associated with the Karush–Kuhn–Tucker optimality conditions in the frame of NLP (as transcribed by PADOC) correspond to a backward analytic solution for the costate equations. We finally show distinct features of PADOC through some examples.

A Distributed Data Mesh Paradigm for an Event-based Smart Communities Monitoring Product

The recent pandemic events in Thailand, Covid-19 in 2018, demonstrated the need for an event-based smart monitoring system. While a distributed multi-level architecture has emerged as an architecture of choice for a larger-scale smart event-based system that requires better latency, security, scalability, and reliability, a recently introduced data mesh paradigm can add a few additional benefits. The paradigm enables each district to become an event-based smart monitoring mesh and handle its analytics and monitoring workload. Districts can form a set of domains in a network of event-based smart community monitoring systems and provide data products for others during a crisis. This paper presents a distributed data mesh paradigm for an event-based smart monitoring product in a given community with predefined domains. The paper presents smart monitoring as a data product between domains. Key considerations for designing an event-based smart monitoring data product are given. The author introduces three possible domains necessary for creating a smart monitoring system in each community. Each domain creates a data product for a given domain and shares data between domains. Finally, a three-layer analytics architecture for a smart monitoring product in each domain and a use case is presented.

A New Big Data and Logistic Regression-Based Approach for Small and Medium-Sized Enterprises

Businesses are being asked to assess an expanding volume of actual semi-structured and unstructured statistics to address the obstacles of internationalization and deal more effectively with the uncertainties of international integration. Big Data (BD) analytics can therefore play a strategic role in promoting the international expansion of Small and Medium-Sized Enterprises (SMEs). The exact connection between BD Analytics and globalization has, however, only been sporadically examined in the existing literature. In this study, a quantitative analysis using a Logistic Regression (LR) concept revealed that the interaction effects between BD Analytics architecture and BD Analytics functionality are both helpful and significant but the connection between the management of BD Analytics architecture and the Degree of Internationalization (DI) is not required for internationalization development. This shows that increasing internationalization in SMEs requires more than BD Analytics governance alone. Instead, this study emphasizes the importance of building particular BD Analytics abilities and the availability of a beneficial interaction between management of BD Analytics architecture and BD Analytics abilities that could take advantage of the new information gained via BD Analytics in SME global expansion.

System design for using multimodal trace data in modeling self-regulated learning

Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how such data relate to learning and performance. Analyzing and interpreting multimodal data about learners' SRL processes as they work in real-time is conceptually and computationally challenging for researchers. In this paper, we discuss recommendations for building a multimodal learning analytics architecture for advancing research on how researchers or instructors can standardize, process, analyze, recognize and conceptualize (SPARC) multimodal data in the service of understanding learners' real-time SRL and productively intervening learning activities with significant implications for artificial intelligence capabilities. Our overall goals are to (a) advance the science of learning by creating links between multimodal trace data and theoretical models of SRL, and (b) aid researchers or instructors in developing effective instructional interventions to assist learners in developing more productive SRL processes. As initial steps toward these goals, this paper (1) discusses theoretical, conceptual, methodological, and analytical issues researchers or instructors face when using learners' multimodal data generated from emerging technologies; (2) provide an elaboration of theoretical and empirical psychological, cognitive science, and SRL aspects related to the sketch of the visionary system called SPARC that supports analyzing and improving a learner-instructor or learner-researcher setting using multimodal data; and (3) discuss implications for building valid artificial intelligence algorithms constructed from insights gained from researchers and SRL experts, instructors, and learners SRL via multimodal trace data.

A field test of a federated learning/federated analytic blockchain network implementation in an HPC environment

The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.

ICT Enabled Disease Diagnosis, Treatment and Management-A Holistic Cost-Effective Approach Through Data Management and Analysis in UAE and India.

This concept paper addresses specific challenges identified in the UN 2030 Agenda Sustainable Development Goals (SDG) as well as the National Health Policy of India (NHP-India) and the Ministry of Health Policy of UAE (MHP-UAE). This policy calls for a digital health technology ecosystem. SDG Goal 1 and its related objectives are conceptualized which serves as the foundation for Virtual Consultations, Tele-pharmacy, Virtual Storage, and Virtual Community (VCom). SDG Goals 2 and 3 are conceptualized as Data Management & Analytical (DMA) Architecture. Individual researchers and health care professionals in India and the UAE can use DMA to uncover and harness PHC and POC data into practical insights. In addition, the DMA would provide a set of core tools for cross-network initiatives, allowing researchers and other users to compare their data with DMA data. In rural, urban, and remote populations of the UAE and India, the concept augments the PHC system with ICT-based interventions. The ICT-based interventions may improve patient health outcomes. The open and flexible design allows users to access various digital materials. Extendable data/metadata format, scalable architecture for petabyte-scale federated discovery. The modular DMA is designed using existing technology and resources. Public health functions include population health assessment, policy development, and monitoring policy implementation. PHC and POC periodically conduct syndromic surveillance to identify population risk patterns. In addition, the PHC and POC deploy medical and non-medical preventive measures to prevent disease outbreaks. To assess the impact of social and economic factors on health, epidemiologists must first understand diseases. Improved health due to compliance with holistic disease treatment plans and access to scientific health information.

Estimation and Monitoring of Operating Room Utilization by a Distributed Streaming and Analytics Architecture Deployed at Heidelberg University Hospital's Medical Data Integration Center.

Operating rooms are a major cost factor in a hospital's budget. Therefore, there is a need for process optimization related to the operating rooms (OR). However, the collection of key figures for process optimization is often done manually by medical staff. This can be erroneous, inaccurate, time consuming, and incomplete. Automated, data-driven approaches are intended to address these problems and help to get the most precise picture possible of what is happening within the OR. At Heidelberg University Hospital (UKHD), a distributed AI based streaming analytics architecture was set up and integrated into the Medical Data Integration Center (MeDIC). This architecture can process, store, and visualize heterogeneous data from different sources. Data from medical devices and the video stream of the wall mounted cameras of four integrated operating rooms are ingested into our system. Aggregated and analyzed in real-time computed key figures including OR state and utilization numbers are visualized in a dashboard for monitoring and decision support. Because of high data protection hurdles the proposed system, especially the video analytics, was trained and tested with statists and did not run during real procedures. Studies to evaluate and test the system during live surgeries are planned.

A Fog-Based Multi-Purpose Internet of Things Analytics Platform

Data analytics is an important component for the benefit and growth of the Internet of Things (IoT). The utilization of data generated by a variety of heterogeneous smart devices offers the possibility of gaining meaningful insights into various aspects of the daily lives of end consumers, the environment and weather, but also into value-added processes of business and industry. The potential benefits derived from analyzing IoT data can be further enhanced by advancing developments in streaming and machine learning technologies. A critical factor in the application of these technologies are the underlying analytics architectures. These must overcome a variety of different challenges that are influenced by technical, but also legal or personal constraints and differ in importance and impact depending on the IoT application domain in which such an architecture is to be deployed. Solutions presented by previous research address only a handful of these challenges. An important capability to address the variety of challenges that arise from this situation is the ability to support the hybrid deployment of analytics pipelines at different network layers. Consequently, in this work, we propose an architectural solution that enables hybrid analytics pipeline deployments, addresses the challenges described in previous scientific literature and can be deployed in various IoT application domains. Finally, we experimentally evaluate the proposed solution.

Data Integration Challenges for Machine Learning in Precision Medicine.

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

IoT data analytics architecture for smart healthcare using RFID and WSN

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff–Parkinson–White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.

Soft-templating routes for the synthesis of mesoporous tantalum phosphates and their catalytic activity in glycerol dehydration and carbonylation reactions

The craftsmanship in catalyst synthesis becomes very fascinating when the catalytic activity of the material can be enhanced by tuning the synthesis conditions. The underlying fundamental strategy is presented here in tailoring the structural and chemical behavior of the tantalum phosphates (TaP) by using different structure directing agents (SDAs). We found that by employing appropriate template assisted pathway it is possible to tune the morphology and the nature of active sites of TaP. The synthesised catalysts were comprehensively characterised by N2-physisorption, FESEM, HRTEM, powder XRD, FTIR, UV–Vis, XPS, NH3 and CO2-TPD, Py-adsorbed IR, elemental analysis and 31P{1H} NMR techniques. The FESEM and HRTEM analysis revealed the transition of non-spherical to rod-shaped particles upon moving from ionic to non-ionic surfactants. The TPD and Py-IR studies revealed the variation in amount, strength and nature of the acidic-basic sites with the change in SDA. The resultant TaP catalysts were investigated for gas phase glycerol dehydration to acrolein and liquid phase glycerol carbonylation to glycerol carbonate. Among these catalysts, TaPC synthesized by using CTAB as SDA exhibited promising catalytic activity in both gas and liquid phase reactions. The active site elucidation in TaPC catalyst was done by adopting basic probe molecule 2,6-lutidine. The deactivation kinetic study was carried out for the gas phase glycerol dehydration reaction. The investigation on the spent catalyst showed it is stable under both gas-phase and liquid-phase reaction conditions. This study hence provides a pathway for analytical architecture and construction of highly efficient TaP catalysts for glycerol transformation reactions.

FEVA: A Federated Video Analytics Architecture for Networked Smart Cameras

Video analytics using networked smart cameras has become a core function for many applications including surveillance, object detection, AR/VR, and so on. In recent years, a number of architectures have been proposed to organize the computing and networking resources of cloud and edge cameras to collectively complete an analytics task (e.g., 3D reconstruction, multi-view re-identification). Unfortunately, in many applications, image sharing can lead to privacy con-cerns. One example is the high definition map (HD map) for autonomous driving. An HD map has a highly dynamic layer of real-time objects. Vehicles can collectively contribute videos from their onboard cameras to construct such a layer, but the video images can contain private information (e.g., the license plate numbers of front cars). In this article, we propose FEVA, a new federat-ed video analytics architecture. Intrinsically, FEVA keeps the video image data local to the edge for analytics and transmits the analytics results to the cloud for aggregation. FEVA partitions the video analytics computing tasks in a way that is priva-cy-preserving and maximizes the overall analytics accuracy under the computing and communication resource constraints of the edge devices. We show how FEVA can be used in practice by a case study using FEVA to support a video analyt-ics application on multi-view vehicles 3D reconstruction. We implement FEVA by extending the open source platform TensorFlow Federated from Google. We deploy our case in an environment with four Amazon OeepLens cameras. Our eval-uation shows that FEVA can protect privacy while effectively increasing the accuracy of the video analytics application.

A Perceptron Based Neural Network Data Analytics Architecture for the Detection of Fraud in Credit Card Transactions in Financial Legacy Systems

Credit card fraud, a significant and growing problem in commerce that costs the global economy billions of dollars each year, has kept up with technological advancements as criminals devise new and innovative methods to defraud account holders, merchants, and financial institutions. While traditional fraudulent methods involved card cloning, skimming, and counterfeiting during transactional processes, the rapid adoption and evolution of Internet technologies aimed at facilitating trade has given rise to new digitally initiated illegitimate transactions, with online credit card fraud beginning to outpace physical world transactions. According to the literature, the financial industry has used statistical methods and Artificial Intelligence (AI) to keep up with fraudulent card patterns, but there appears to be little effort to provide neural network architectures with proven results that can be adapted to financial legacy systems. The paper examines the feasibility and practicality of implementing a proof-of-concept Perceptron-based Artificial Neural Network (ANN) architecture that can be directly plugged into a legacy paradigm financial system platform that has been trained on specific fraudulent patterns. When using a credit checking subscription service, such a system could act as a backup.