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2026-03-09
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Abstract
As data generation accelerates, the field of big data has become vital for capturing, processing, and interpreting massive volumes of information. This chapter provides an introduction to big data analytics, emphasizing core concepts and techniques essential for analyzing vast, complex datasets. This chapter begins by defining big data through its primary characteristics, often summarized as the “3Vs”—volume, velocity, and variety— and examines additional attributes like veracity and value that further shape its analysis. Key stages in the big data analytics pipeline are outlined, including data collection, storage architectures (e.g., NoSQL databases, distributed systems), preprocessing, and feature engineering tailored for high-dimensional data. Fundamental analytical techniques such as parallel computing, distributed processing frameworks (e.g., Hadoop and Spark), and scalable machine learning methods are discussed.
The chapter also explores the role of mathematical modeling within big data analytics, showing how models can be adapted to operate efficiently within big data environments. Examples demonstrate how big data concepts integrate with modeling for tasks like clustering, classification, and predictive analysis, making this chapter a foundational resource for readers looking to bridge mathematical theory with real-world data applications. Ultimately, this chapter establishes a framework for understanding the big data landscape, preparing readers to proceed into advanced analytics methods for extracting meaningful insights from complex, large-scale data as explained in detail in the rest of the book chapters.
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Mathematical Modeling for Big Data Analytics