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Handbook of Data Intensive Computing

Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers;

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Data Intensive Distributed Computing

Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management focuses on the challenges;

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Intelligent Agents in Data-intensive Computing

for data intensive applications. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi;

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Intelligent Agents in Data-intensive Computing

for data intensive applications. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi;

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Data-Intensive Computing

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates;

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Data-Intensive Computing in Smart Microgrids

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Handbook of Parallel Computing and Statistics

cumulants, data mining, econometrics, and Bayesian computation. Expert contributors summarize recent results and explore new directions in these;

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Intelligent Data Communication Technologies and Internet of Things

This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application;

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Cloud Computing

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis;

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Cloud Computing

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis;

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The Fourth Paradigm

This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide;

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Enterprise Operations Management Handbook, Second Edition

data center. Topics include business issues, technology issues, and operational issues. This current, practical reference also reviews such;

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Enterprise Operations Management Handbook

data center. Topics include business issues, technology issues, and operational issues. This current, practical reference also reviews such;

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Data Analysis in the Cloud

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital;

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Data Analytics and Management in Data Intensive Domains

This book constitutes the post-conference proceedings of the 21st International Conference on Data Analytics and Management in Data;

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Non-volatile In-memory Computing by Spintronics

Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main;

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Non-Volatile In-Memory Computing by Spintronics

Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main;

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Soft Computing

Three discusses the intensive developing of Soft Computing systems especially Wavelet-Neuro-Fuzzy Systems (WNFS) in Dynamic Data Mining tasks;

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Handbook of Energy-Aware and Green Computing - Two Volume Set

, mobile computing, and data centers. It also discusses up-to-date research on many aspects of power-aware computing at the component, software;

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Handbook of Energy-Aware and Green Computing - Two Volume Set

, mobile computing, and data centers. It also discusses up-to-date research on many aspects of power-aware computing at the component, software;

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Urban Computing

congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science;

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Mastering Cloud Computing

computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures;

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Computational Social Science

Selected papers from the International Conference on New Computational Social Science, focusing on the following five aspects: Big data;

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Data Placement in Distributed Systems

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific;

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Cloud Computing Security

solutions to a wide range of cloud computing security issues. With more organizations using cloud computing and cloud providers for data operations;

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ReRAM-based Machine Learning

such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications;

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Handbook of Big Geospatial Data

This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various;

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