Open Access

ARTICLE

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

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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