Open Access

ARTICLE

Compliance-Integrated Data Integrity Framework for Large-Scale Sensor and Measurement Systems

Chirag Devendrakumar Parikh^*

Journal on Big Data, Vol.8, pp. 11-26, 2026, DOI:10.32604/jbd.2026.077330 - 07 September 2026
Abstract Sensors and large-scale measurement systems generate continuous data streams used in industrial monitoring, IoT analytics, and decision-making. However, sensor drift, undocumented maintenance, environmental stress, and component variability often degrade the integrity and reliability of collected measurements. This study proposes a compliance-integrated data integrity framework that combines hardware qualification records, traceability documentation, lifecycle validation checkpoints, and automated anomaly detection methods. The framework introduces five integrity layers that link sensor characterization with statistical filtering and compliance-driven verification. To validate feasibility, a proof-of-concept simulation was conducted using drift-injected sensor datasets. Results show that integrating compliance metadata improve anomaly More >

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Open Access

ARTICLE

Credit Card Fraud Detection Using Variational Autoencoders

Edward Danso Ansong¹, David Adlai Nettey^1,*, Sarika S², Simon Bonsu Osei¹

Journal on Big Data, Vol.8, pp. 1-10, 2026, DOI:10.32604/jbd.2026.065126 - 12 June 2026
Abstract Credit card fraud has emerged as a pervasive threat, impacting financial institutions and individuals as online banking and payment methods become increasingly integral to daily life. Despite efforts to mitigate this problem through measures like passwords and two-factor authentication, financial institutions continue to suffer substantial losses, often amounting to millions of dollars. Traditional machine learning solutions, developed and trained as supervised learning models, have failed to address this issue effectively. In anomaly detection, such as credit card fraud detection, the available training datasets are vast but inherently imbalanced, posing a formidable obstacle for supervised learning… More >

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264

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REVIEW

An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing

Bilal Khan¹, Saifullah Jan^1,*, Wahab Khan¹, Muhammad Imran Chughtai²

Journal on Big Data, Vol.6, pp. 1-20, 2024, DOI:10.32604/jbd.2023.046223
Abstract The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL… More >

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4112

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ARTICLE

Legume Cowpea Leaves Classification for Crop Phenotyping Using Deep Learning and Big Data

Vijaya Choudhary^1,2,3,*, Paramita Guha^1,2, Giovanni Pau⁴

Journal on Big Data, Vol.7, pp. 1-14, 2025, DOI:10.32604/jbd.2025.065122
Abstract Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing More >

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1130

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ARTICLE

Optimization of Electric Vehicle Charging Station Layout Based on Point of Interest Data and Location Entropy Evaluation

Annan Yang¹, Jiawei Zhang^2,*, Haojie Yang^3,*, Keyi Tao¹, Mengna Xu¹, Yuyu Zhao¹

Journal on Big Data, Vol.6, pp. 21-41, 2024, DOI:10.32604/jbd.2024.057612
Abstract This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector. More >

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2569

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2277

Open Access

ARTICLE

Credit Card Fraud Detection Using Variational Autoencoders

Edward Danso Ansong¹, David Adlai Nettey^1,*, Sarika S², Simon Bonsu Osei¹

Journal on Big Data, Vol.8, pp. 1-10, 2026, DOI:10.32604/jbd.2026.065126
Abstract Credit card fraud has emerged as a pervasive threat, impacting financial institutions and individuals as online banking and payment methods become increasingly integral to daily life. Despite efforts to mitigate this problem through measures like passwords and two-factor authentication, financial institutions continue to suffer substantial losses, often amounting to millions of dollars. Traditional machine learning solutions, developed and trained as supervised learning models, have failed to address this issue effectively. In anomaly detection, such as credit card fraud detection, the available training datasets are vast but inherently imbalanced, posing a formidable obstacle for supervised learning… More >

View

876

Download

264

Open Access

ARTICLE

Compliance-Integrated Data Integrity Framework for Large-Scale Sensor and Measurement Systems

Chirag Devendrakumar Parikh^*

Journal on Big Data, Vol.8, pp. 11-26, 2026, DOI:10.32604/jbd.2026.077330
Abstract Sensors and large-scale measurement systems generate continuous data streams used in industrial monitoring, IoT analytics, and decision-making. However, sensor drift, undocumented maintenance, environmental stress, and component variability often degrade the integrity and reliability of collected measurements. This study proposes a compliance-integrated data integrity framework that combines hardware qualification records, traceability documentation, lifecycle validation checkpoints, and automated anomaly detection methods. The framework introduces five integrity layers that link sensor characterization with statistical filtering and compliance-driven verification. To validate feasibility, a proof-of-concept simulation was conducted using drift-injected sensor datasets. Results show that integrating compliance metadata improve anomaly More >

View

411

Download

100