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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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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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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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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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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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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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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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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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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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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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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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Distributed Computing with Python

write data processing programs in Python that are highly available, reliable, and fault tolerant * Make use of Amazon Web Services along with;

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Big Data Technologies and Applications

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data;

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Data Intensive Computing for Biodiversity

This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and;

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High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 High-Performance Modelling and Simulation for Big Data;

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Soft Computing Techniques and Applications

of applications, such as, data mining and fuzzy logic, soft computing techniques play an incomparable role, where it successfully handles contemporary;

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Elements of Statistical Computing

over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter;

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Cloud Computing and Software Services

real cases and applications of cloud computing Discusses the infrastructure cloud and Infrastructure as a Service (IaaS) Considers data- and;

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Temporal QOS Management in Scientific Cloud Workflow Systems

scale data/computation intensive e-science applications such as earthquake modelling, weather forecasting and astrophysics. During application;

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