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Advanced Data Analytics for Power Systems

applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical;

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Software Foundations for Data Interoperability and Large Scale Graph Data Analytics

, and 2nd International Workshop on Large Scale Graph Data Analytics, LSGDA 2020, held in Conjunction with VLDB 2020, in September 2020. Due to;

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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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Spark GraphX in Action

is the graph processing module that is part of the distributed in-memory computing framework Apache Spark.;

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Graph Algorithms Practical Examples in Apache Spark and Neo4j

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine;

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Link Reversal Algorithms

. The common thread in these algorithms is that the distributed system is viewed as a graph, with vertices representing the computing nodes and;

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Guide to Graph Algorithms

comparative analysis of sequential, parallel and distributed graph algorithms - including algorithms for big data - and an investigation into the;

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Data Analytics

and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.;

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Web-Age Information Management

temporal databases, recommender systems, graph data management, information retrieval, privacy and trust, query processing and optimization, social;

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Web-Age Information Management

temporal databases, recommender systems, graph data management, information retrieval, privacy and trust, query processing and optimization, social;

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Biological Network Analysis

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene;

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Big Data Analytics

and agricultural domains; graph analytics; pattern mining; and machine learning.;

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Euro-Par 2016: Parallel Processing Workshops

This book constitutes the proceedings of the workshops of the 23rd International Conference on Parallel and Distributed Computing, Euro-Par;

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Large-Scale Data Analytics

technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data;

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Web Technologies and Applications

Graph Analytics and Query Processing, GAP 2016, and the First International Workshop on Spatial-temporal Data Management and Analytics, SDMA;

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Distributed Graph Analytics

This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph;

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Distributed Graph Analytics

This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph;

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Euro-Par 2015: Parallel Processing Workshops

- PADAPS: Third Workshop on Parallel and Distributed Agent-Based Simulations - PELGA: Workshop on Performance Engineering for Large-Scale Graph;

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WALCOM: Algorithms and Computation

methodologies, graph algorithms, graph drawing, parallel and distributed algorithms, network optimization.;

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Cooperative Control of Multi-Agent Systems

Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the;

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Cooperative Control of Multi-Agent Systems

Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the;

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Large-Scale Graph Processing Using Apache Giraph

This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power;

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Cooperative and Graph Signal Processing

, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the;

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Link Reversal Algorithms

. The common thread in these algorithms is that the distributed system is viewed as a graph, with vertices representing the computing nodes and;

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Database Systems for Advanced Applications

embedding; recommendation; graph and network processing; social network analytics; sequence and temporal data processing; trajectory and streaming;

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Database Systems for Advanced Applications

embedding; recommendation; graph and network processing; social network analytics; sequence and temporal data processing; trajectory and streaming;

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The Practitioner's Guide to Graph Data Applying Graph Thinking and Graph Technologies to Solve Complex Problems

together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique;

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