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This book constitutes refereed proceedings of the 4th International Workshop on Software Foundations for Data Interoperability, SFDI 2020;
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mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and;
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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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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 covers the theory and applications of hardware platforms and architectures, the;
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currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses;
Vergelijkbare producten zoals 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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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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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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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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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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, 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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present a large collection of algorithms for constructing drawings of graphs, including tree, planar straight-line, planar orthogonal and polyline;
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present a large collection of algorithms for constructing drawings of graphs, including tree, planar straight-line, planar orthogonal and polyline;
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implementations. Along the way, it details practical techniques for enhancing applications and applying machine learning algorithms to graph data;
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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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- Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud;
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theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners;
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for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.;
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unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures;
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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 consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore;
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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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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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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 is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster;
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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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