APPLICATIONS FOR BIG DATA BY USING OPTIMIZATION TECHNIQUES

Abstract

The accelerating digitization of business, science, and society generates data at a volume, velocity, and variety that overwhelms traditional data-management systems, motivating distributed platforms such as Hadoop and its MapReduce processing engine. While a rich ecosystem of tools now exists for storing and computing over such data, processing big data in an optimized manner—so that overall performance, accuracy, and responsiveness do not degrade as data grows—remains a central research challenge. This paper surveys recent optimization technologies and their applications for big data. We characterize big data through its defining dimensions (volume, variety, veracity, and velocity), examine the scalability and data-quality challenges that constrain conventional techniques, and organize the big data analytics landscape into descriptive, predictive, and prescriptive layers. We then review contemporary optimization approaches spanning parameter calibration and combiner-level enhancement of Hadoop MapReduce, Bayesian and clustering-based optimization on distributed frameworks, and data-reduction and dataselection strategies that lower processing cost while preserving analytical value. Finally, we illustrate application domains—business process management, network optimization, fraud detection, healthcare data lakes, and telecom data monetization—where optimized big data pipelines deliver measurable business value. The survey highlights that performance optimization, ease of use, and cost-effectiveness constitute complementary objectives, and identifies open issues that must be addressed for mature, well-governed big data analytics systems.