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Название: Big Data Computing: Advances in Technologies, Methodologies, and Applications
Автор: Tanvir Habib Sardar, Bishwajeet Kumar Pandey
Издательство: CRC Press
Серия: Computational Intelligence Techniques
Год: 2024
Страниц: 397
Язык: английский
Формат: pdf (true)
Размер: 15.0 MB

This book primarily aims to provide an in-depth understanding of recent advances in Big Data computing technologies, methodologies, and applications along with introductory details of Big Data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and Big Data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in Big Data computing applications such as Machine Learning, Deep Learning, graph processing, and many others.

Big Data has become a critical issue for businesses to leverage their data assets to drive business decisions considering the exponential growth of data in today’s age. Machine Learning (ML) and Artificial Intelligence (AI) have emerged as powerful tools to extract insights and value from Big Data. Here we will explore a few of the key applications of ML and AI in Big Data.

Predictive analytics is one of the most significant applications of ML in Big Data. It involves using existing data to predict future events or behaviors. Predictive analytics may be used to predict future sales, identify customers at risk of churning, or predict the likelihood of a customer purchasing a specific product . In predictive analytics, algorithms such as decision trees, random forests, and neural networks are commonly used.

Features:

Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.
Explains computing models using real-world examples and dataset-based experiments.
Includes case studies, quality diagrams, and demonstrations in each chapter.
Describes modifications and optimization of existing technologies along with the novel big data computing models.
Explores references to machine learning, deep learning, and graph processing.

This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.









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