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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

-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation;

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Intelligent Agents in Data-intensive Computing

-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation;

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Data Placement in Distributed Systems

requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application;

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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 Analytics and Management in Data Intensive Domains

extraction from text; distributed computing; data science for education.;

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Distributed Computing with Python

* Python in the Cloud * Python on an HPC cluster * Test and debug distributed applications In Detail CPU-intensive data processing 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 Analysis in the Cloud

NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book;

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Internet and Distributed Computing Systems

computing and big data; distributed scheduling and optimization; internet of things and its application; smart networked transportation and;

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Hadoop in Action

HIGHLIGHT Hadoop in Action is an example-rich tutorial that shows developers how to implement data-intensive distributed;

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Intelligent Distributed Computing XIII

This book gathers research contributions on recent advances in intelligent and distributed computing. A major focus is placed on new;

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Distributed Computing in Big Data Analytics

decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage;

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Advances in Intelligent and Distributed Computing

, Romania, October 2007. Coverage includes: autonomous and adaptive computing; data mining and knowledge discovery; distributed problem solving and;

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Cognitive Networked Sensing and Big Data

Wireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed;

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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 for Biodiversity

distributed data sources. The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous;

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Data-Intensive Computing in Smart Microgrids

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Algorithms and Architectures for Parallel Processing

; big data and its applications; parallel and distributed algorithms; applications of parallel and distributed computing; service dependability;

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Java Distributed Computing

Java's security facilities, how to write multithreaded servers, and more. It pays special attention to distributed data systems, collaboration;

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Internet and Distributed Computing Systems

distributed computing, advanced networking, smart cities and smart buildings, Big Data and social networks.;

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Data-Intensive Text Processing With MapReduce

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting;

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Distributed Computing in Java 9

how to handle large amount of data using HPC and implement distributed computing for databases. Moving on, it explains how to deploy;

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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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Parallel and Distributed Computing

This is an all-inclusive survey of the fundamentals of parallel and distributed computing. The use of parallel and distributed computing;

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Cloud Computing in Remote Sensing

remote sensing data integration across distributed data centers Covers cloud storage based remote sensing data share service Covers high;

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Design and Analysis of Distributed Algorithms

principles and techniques presented can be applied to any distributed computing environment (e.g., distributed systems, communication networks, data;

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