Managing Uncertainty in Big Data: Fuzzy Logic and Active Learning Strategies for Imprecise Data
Introduction Big data processing involves managing vast volumes of data that are often incomplete, imprecise, or uncertain due to diverse sources, rapid generation, and varying quality. Uncertainty in big data can arise from missing values, noisy measurements, ambiguous classifications, or incomplete datasets. Traditional deterministic approaches struggle to handle such uncertainties effectively, leading to inaccurate analyses or unreliable models. This chapter explores how fuzzy logic and active learning provide robust frameworks for addressing incomplete or imprecise data in big data processing, enabling more accurate and adaptive solutions. We discuss their theoretical foundations, practical applications, and integration, with examples and implementation strategies. Understanding Uncertainty in Big Data Sources of Uncertainty Uncertainty in big data stems from several factors: Incomplete Data : Missing values due to sensor failures, incomplete records, or data integration...