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Software Foundations for Data Interoperability and Large Scale Graph Data Analytics

This book constitutes refereed proceedings of the 4th International Workshop on Software Foundations for Data Interoperability, SFDI 2020;

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Large-Scale Data Analytics

mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and;

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Advanced Data Analytics for Power Systems

learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and;

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Model Management and Analytics for Large Scale Systems

transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings;

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Real-Time Data Analytics for Large Scale Sensor Data

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the;

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Large Scale and Big Data

currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses;

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Large Scale and Big Data

currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses;

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Large-Scale Graph Processing Using Apache Giraph

describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at;

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Data Science and Big Data Analytics in Smart Environments

variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science;

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Big Data Analytics for Large-Scale Multimedia Search

data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for;

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Apache Spark Graph Processing

Build, process and analyze large-scale graph data effectively with Spark About This Book * Find solutions for every stage of data;

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Graph Kernels

, software plus data resources, and an empirical comparison of state-of-the-art graph kernels. It is divided into two parts: the first part focuses;

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Handbook of Graph Drawing and Visualization

present a large collection of algorithms for constructing drawings of graphs, including tree, planar straight-line, planar orthogonal and polyline;

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Handbook of Graph Drawing and Visualization

present a large collection of algorithms for constructing drawings of graphs, including tree, planar straight-line, planar orthogonal and polyline;

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Spark GraphX in Action

implementations. Along the way, it details practical techniques for enhancing applications and applying machine learning algorithms to graph data;

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Biomedical Data Management and Graph Online Querying

This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data;

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV

- Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud;

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Transactions on Computational Science XXXIV

theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners;

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Machine Learning for Intelligent Multimedia Analytics

for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.;

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Service-Oriented Distributed Knowledge Discovery

unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures;

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Service-Oriented Distributed Knowledge Discovery

unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures;

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Big Data Analytics for Sustainable Computing

Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore;

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Web and Big Data APWeb WAIM 2020 International Workshops

International Joint Conference on Web and Big Data, APWeb-WAIM 2020: The Third International Workshop on Knowledge Graph Management and Applications;

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Graph Algorithms Practical Examples in Apache Spark and Neo4j

and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data;

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Data Science and Data Analytics

a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many;

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Big Data Analytics with Spark

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster;

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Advances in High Performance Computing

computing. The topics of interest included into this volume are: HP software tools, Parallel Algorithms and Scalability, HPC in Big Data analytics;

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