Processing Large Volumes Of Data Research Articles

An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. This study focuses on predicting whether a person is at risk of diabetes, considering the individual’s health and socio-economic conditions while mitigating the challenges posed by imbalanced data. We employ several data augmentation techniques, such as oversampling (Synthetic Minority Over Sampling for Nominal Data, i.e.SMOTE-N), undersampling (Edited Nearest Neighbor, i.e. ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms to minimize the impact of imbalanced data. Our study sheds light on the significance of carefully utilizing data augmentation techniques without any data leakage to enhance the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to machine learning prediction, enabling them to make informed decisions.

Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency

Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.

The potential of artificial intelligence in early diagnosis and personalized treatment: Advances and challenges in modern medicine

Carrying out this study is justified by its academic, scientific and social relevance, based on presenting technological advances involving artificial intelligence in healthcare. Therefore, the objective of this research focuses on highlighting the applications and benefits of Artificial Intelligence in Medicine. This study was carried out through an integrative literature review, with an exploratory approach, whose institute was limited to investigating, through already published articles, relevant information that answered the guiding question. Thus, data collection took place in scientific bases: SCIELO and LILACS. One of the main advantages of using AI in early diagnosis is the ability to process large volumes of data quickly and accurately, identifying patterns and signals that may be imperceptible to human healthcare professionals. This makes it possible to detect serious medical conditions early, such as cancer and heart disease, when treatment is most effective and the chances of recovery are greatest. Furthermore, AI can significantly contribute to personalized treatment, adapting therapeutic approaches based on the individual characteristics of each patient. By analyzing genetic data, medical history, and response to previous treatments, AI algorithms can help doctors develop treatment plans that are more effective and have fewer side effects.

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

Applications of Artificial Intelligence in the Medical Field: A Survey

Artificial intelligence (AI) has revolutionized numerous industries, including healthcare. With its ability to process large volumes of data and make predictions, AI has shown significant potential in improving diagnostics, treatment planning, and patient care. This paper presents a comprehensive survey of recent research and applications of AI in the medical field. The survey covers various AI techniques such as machine learning, deep learning, and natural language processing, and their applications in areas such as disease diagnosis, image analysis, drug discovery, and personalized medicine. Furthermore, this paper discusses challenges and ethical considerations associated with the adoption of AI in healthcare, highlighting the need for careful integration to ensure patient safety and privacy.

BIG DATA ANALYTICS: A REVIEW OF ITS TRANSFORMATIVE ROLE IN MODERN BUSINESS INTELLIGENCE

In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era.
 Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.

Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption

Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.

Road safety impact assessment practices and recommendations

Abstract This article reviews the practices for conducting Road Safety Impact Assessment (RIA). An overview of current research and methodologies related to this field is presented, and various aspects of the preparation of RSIA are analysed. The DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on road infrastructure safety management (RISM Directive) defines the procedure “road safety impact assessment” as a strategic comparative analysis of the impact of a new road or a substantial modification to the existing network on the safety performance of the road network”; The RISM Directive provides only the basic framework for conducting such analysis by just pointing out its basic elements without providing specific methodology for it. Some countries have developed their own methodologies, others not. Although the RIA is a procedure, that was included in the initial RISM Directive (2008), Bulgaria has not yet had much experience with it, nor has a developed methodology or recommendations for it. The main aim of this article is to review and analyse international experience in conducting road safety impact assessment and to present some recommendations about it for such procedures from national teams. The article draws attention to the methods and tools used in the preparation of the RIA. This includes the collection and analysis of road infrastructure and traffic data, the study of previous safety analyses, modelling and simulations of the proposed measures, and the assessment of social and economic aspects. One of the main aspects discussed in the article is the possibility of using computer models and artificial intelligence (AI) in the preparation of the RIA. AI can be applied to process large volumes of data and perform complex risk and scenario analyses. This allows practitioners to obtain more accurate and predictable results to serve as a basis for making informed road safety decisions. In conclusion, the article emphasizes the importance of preparing Road safety impact assessment and presents different practices and methodologies that can be used in this process. The use of computer models and AI can improve the efficiency and accuracy of RSIA and contribute to achieving higher standards of road safety.

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Abstract Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.

Industrial Product Quality Analysis Based on Online Machine Learning.

During industrial production activities, industrial products serve as critical resources whose performance is subject to various external factors and usage conditions. To ensure uninterrupted production processes and to guarantee the safety of the production personnel, a real-time analysis of the industrial product quality and subsequent decision making are essential. Conventional detection methods have inherent limitations in meeting the real-time demands of processing large volumes of data and achieving high response speeds. For instance, the regular inspection and maintenance of cars can be time-consuming and labor-intensive if performed manually. Furthermore, monitoring the damage situation of bearings in real time through a manual inspection may lead to delays and may hinder production efficiency. Therefore, this paper presents online machine-learning-based methods to address these two practical problems and simulates them on various datasets to meet the requirements of efficiency and speed. Prior to being fed into the network for training, the data undergo identity parsing to transform them into easily identifiable streaming data. The training process demonstrates that online machine learning ensures timely model updates as small batches of data are sent to the network. The test results indicate that the online learning method exhibits highly stable and effective performance, optimizing the training process.

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНОЇ МАТРИЧНОЇ ФАКТОРИЗАЦІЇ В ПРОМИСЛОВИХ СИСТЕМАХ

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

Overcoming traditional ETL systems architectural problems using a service‐oriented approach

AbstractDeveloping analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data are a determinant factor in the success of analytical and decision‐making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today's users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on extract‐transform‐load (ETL) systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up‐to‐date approaches, having real application evidence. In this paper, we present a service‐oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service‐oriented architecture, to demonstrate the application and benefits that this kind of approach will provide to ETL systems project development.

ANÁLISE DE DADOS EDUCACIONAIS: COMO A TECNOLOGIA PODE SER USADA PARA OBTER INSIGHTS SOBRE O DESEMPENHO DOS ALUNOS

Abstract
 Esta pesquisa tem como objetivo investigar a importância da análise de dados educacionais como uma ferramenta poderosa para obter insights valiosos sobre o desempenho dos alunos. Com o avanço da tecnologia e o aumento da disponibilidade de sistemas de gerenciamento de aprendizagem e plataformas online, as instituições de ensino têm acesso a uma quantidade significativa de dados educacionais. A análise desses dados oferece a oportunidade de identificar padrões, tendências e correlações, fornecendo informações úteis para melhorar a qualidade da educação. Ao analisar os dados educacionais, é possível obter insights sobre o progresso acadêmico dos alunos, identificar áreas de melhoria e personalizar o ensino de acordo com as necessidades individuais de cada aluno. Através da análise preditiva, é possível identificar quais alunos estão em risco de baixo desempenho e intervir precocemente, oferecendo suporte e recursos adicionais para melhorar seus resultados. Além disso, a análise de dados pode ajudar a identificar quais métodos de ensino são mais eficazes e quais estratégias podem ser implementadas para promover a retenção e o engajamento dos alunos. A tecnologia desempenha um papel fundamental na análise de dados educacionais, fornecendo as ferramentas e os recursos necessários para coletar, armazenar e processar grandes volumes de dados. O uso de algoritmos e técnicas de aprendizado de máquina permite uma análise mais sofisticada, identificando padrões complexos e insights que seriam difíceis de serem percebidos de forma manual. Além disso, a visualização de dados é uma ferramenta poderosa para comunicar informações complexas de forma clara e compreensível. No entanto, é importante destacar que a análise de dados educacionais também apresenta desafios e questões éticas. A privacidade e a segurança dos dados dos alunos são preocupações fundamentais, exigindo que sejam implementadas políticas e práticas de proteção de dados adequadas. Além disso, é necessário garantir que a análise de dados seja realizada de forma transparente e responsável, levando em consideração os aspectos éticos e respeitando a privacidade dos alunos.

DATA-DRIVEN SMART MANUFACTURING: CASE STUDY OF WORKFORCE MANAGEMENT PROCESS IN AN ITALIAN LEATHER GOODS COMPANY

AbstractDigitalization is one of the fundamental pillars of Industry 4.0. Within smart factories, Big Data Analytics systems play a key role in supporting the decision-making process of various stages of business processes. In this context, this research aims to identify solutions able to process large volumes of data from digital business processes with the final goal of adding value to the organisation. More specifically, the research deals with the implementation of a digital manufacturing tool able to digitize the workforce management process. The research has been applied in the case study of an Italian manufacturing company operating in the leather goods sector through the digitalization of the workforce management by a cloud-based platform. The implementation of the tool increases the efficiency of the production process, provides efficient management and integrates workforce data into one system. The implemented tool generates a large volume of data, the final goal is to make data user-friendly to support business decisions. Digitisation provides an exchange of information to support managers to make confident decisions.

A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root.

Recent clinical studies have suggested that introducing 3D patient-specific aortic root models into the pre-operative assessment procedure of transcatheter aortic valve replacement (TAVR) would reduce the incident rate of peri-operative complications. Tradition manual segmentation is labor-intensive and low-efficient, which cannot meet the clinical demands of processing large data volumes. Recent developments in machine learning provided a viable way for accurate and efficient medical image segmentation for 3D patient-specific models automatically. This study quantitively evaluated the auto segmentation quality and efficiency of the four popular segmentation-dedicated three-dimensional (3D) convolutional neural network (CNN) architectures, including 3D UNet, VNet, 3D Res-UNet and SegResNet. All the CNNs were implemented in PyTorch platform, and low-dose CTA image sets of 98 anonymized patients were retrospectively selected from the database for training and testing of the CNNs. The results showed that despite all four 3D CNNs having similar recall, Dice similarity coefficient (DSC), and Jaccard index on the segmentation of the aortic root, the Hausdorff distance (HD) of the segmentation results from 3D Res-UNet is 8.56 ± 2.28, which is only 9.8% higher than that of VNet, but 25.5% and 86.4% lower than that of 3D UNet and SegResNet, respectively. In addition, 3D Res-UNet and VNet also performed better in the 3D deviation location of interest analysis focusing on the aortic valve and the bottom of the aortic root. Although 3D Res-UNet and VNet are evenly matched in the aspect of classical segmentation quality evaluation metrics and 3D deviation location of interest analysis, 3D Res-UNet is the most efficient CNN architecture with an average segmentation time of 0.10 ± 0.04 s, which is 91.2%, 95.3% and 64.3% faster than 3D UNet, VNet and SegResNet, respectively. The results from this study suggested that 3D Res-UNet is a suitable candidate for accurate and fast automatic aortic root segmentation for pre-operative assessment of TAVR.

Prediction of Daily Temperature Based on the Robust Machine Learning Algorithms

Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions necessitates thoroughly comprehending the underlying factors influencing climate patterns. The study utilized two models, LSTM and DLSTM, to forecast daily air temperature using 1097 data points gathered from central and southern regions of Tabriz city of Iran in Asia from 2017 to 2019. The results indicated that the proposed model had a high accuracy rate for predicting daily air temperature for test data, with RMSEDLSTM = 0.08 °C and R-SquareDLSTM = 0.99. The DLSTM algorithm is known for its high speed, accuracy, time series prediction, noise reduction capabilities for data, the large volume of data processing, and improved performance of predicted data. In summary, while both LSTM and DLSTM are used for predicting data points, DLSTM is a more advanced version that includes multiple layers of memory cells and is better suited for handling complex sequences of events.

A Survey on Machine Learning Algorithms for Risk-Controlled Algorithmic Trading

Machine learning algorithms have emerged as powerful tools for risk control in algorithmic trading, enabling traders to analyze vast amounts of market data, detect patterns, and make informed trading decisions. In today's fast-paced and data-driven financial markets, effective risk management is essential to navigate market uncertainties and optimize trading performance. Traditional risk control methods often struggle to capture complex market dynamics and adapt to rapidly changing conditions, leading to the adoption of machine learning algorithms. These algorithms excel in processing large volumes of data, uncovering hidden patterns, and making accurate predictions, enabling traders to develop proactive risk management strategies. Machine learning algorithms offer several advantages in risk control for algorithmic trading. They can analyze diverse data sources such as historical price data, news sentiment, and economic indicators, providing valuable insights for risk assessment and decision-making. Additionally, these algorithms can handle time series data, capturing temporal dependencies and adapting to dynamic market conditions. They provide real-time risk monitoring and early warning capabilities, enabling traders to respond quickly to emerging risks and implement risk mitigation measures. Furthermore, machine learning algorithms offer the potential to optimize portfolio management, dynamically adjusting portfolio weights based on risk-return profiles and optimizing asset allocation strategies. Machine learning algorithms have revolutionized risk control in algorithmic trading by providing advanced analytics, predictive capabilities, and real-time monitoring. These algorithms enhance risk management strategies, improve decision-making processes, and enable traders to navigate the complexities of financial markets.

FPGA-Based Accelerator for AI-Toolbox Reinforcement Learning Library

In Reinforcement Learning (RL) an agent interacts with the environment based on sequential decisions. This agent receives a reward from the environment according to decisions and tries to maximize the reward. RL is used in several domains such as production, autonomous driving, business management, education, games, healthcare, natural language processing and robotics, among others. RL methodologies require processing large volumes of data and computational power. To speed up these applications, field-programmable gate array (FPGA) are widely employed in the literature. This paper proposes an accelerator for the Markov Decision Process (MDP) implemented in the AI-Toolbox public library using high-level synthesis tools, using the tiger-antelope problem as use case. Our approach shows an acceleration greater than 7x compared to the original version.

Prevention of corruption offenses by public officials: Experience from European Union countries

The article analyzes the effectiveness of preventive anti-corruption measures in the countries of the European Union EU. The study involved comparison and forecasting methods. The results showed that the EU is leading its efforts to develop anti-corruption legislative initiatives and their implementation at national and international level. Whistle-blower protection laws adopted in EU countries are important tools for exposing illegal activities committed in organizations. Transparency of public administration in Denmark and Finland contributes to the maintenance of moral and legal standards in society. The Danish Code of Conduct in the Public Sector and the Finnish Anti-Corruption Guide for Small and Medium-Sized Enterprises have become documents that help promote corruption-free business relationships. The Finnish Harmaa (gray) project is an example of how data analytics helps law enforcement agencies process large volumes of data to prevent corruption offenses. It is concluded that the initiatives of EU countries to prevent corruption of officials can become an example for Ukraine during post-war recovery.

ADHE: A Tool to Characterize Higher Education Dropout Phenomenon

The field of academic analytics emerged in higher education institutions (HEI) because of developments in database technologies and the generalization of data-mining practices and business intelligence tools. We have designed and implemented a dashboard called ADHE (Academic Analytical Dashboard in Higher Education) for a Colombian higher education institution. The purpose of ADHE is to help administrators of academic programs in their decision-making process regarding the analysis of the phenomenon of student dropout. We used the pipeline methodology for processing large volumes of data was used, which is based on five phases: data acquisition, integration, cleaning, transformation, and visualization. All phases were carried out in the R programming language using academic information sources from the Faculty of Engineering of the Universidad de Antioquia and the Colombian Institute for the Evaluation of Education. The dashboard ADHE is open for free and can be consulted at: https://fhernanb.shinyapps.io/AppPermanencia/. The main findings were that social stratum, gender, and type of high school are associated with the student dropout phenomenon. Furthermore, in social stratum 1, male students and public high schools tend to have a higher student dropout proportion. Additionally, we conclude that admission to engineering programs requires a balance of qualitative and quantitative abilities. The dashboard ADHE should be used to help students, parents, teachers, and administrators understand student dropout dynamics.