Big Data Optimization

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall.

The authors of this book assert that Grid Square statistics, a method of aggregating data within a geographically defined Grid, may be an effective solution to approach geospatial data for big data integration. Grid Square statistics is a technique that allows us to collect and analyze data based on Grids and makes it easier to understand patterns and trends. Sustainability, a key concern for the future of our society, often involves balancing multiple independent objectives. These objectives and key performance indicators must be shared to solve multi-dimensional optimization problems with some constraints related to sustainability issues. However, many of these sustainability issues are challenges that are part of global systems, which are modeled as a complex system consisting of many components. Social, economic, and environmental aspects in our socio-economic systems require solutions designed for the sustainability of our society.

Learn Data Science and Data Analysis with Python and Libraries. Such as Numpy, Pandas, Matplotlib with exercises.

This book presents examples of modern optimization algorithms.

Data Lake Mastery using AWS: A Shortcut to Success in Big Data, Cloud Data Engineering and Data Architecture

Computer Science by Aboul Ella Hassanien (Editor), Nilanjan Dey (Editor), Surekha Borra (Editor)
This book addresses recent advances in mining, learning, and analysis of big volume of medical images. The book presents taxonomies, trends and issues such as veracity in distributive, dynamic, and diverse data collection, data management, data models, hypotheses testing, training, validation, model-building, optimization techniques and governance of medical big data collected from multiple, heterogeneous IoT devises, networks, platforms and systems such as private vs. public cloud

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.