High Performance Computing for Big Data: Methodologies and Applications, Wang Chao
Автор: Ahmed S. Ejaz Название: Big and Complex Data Analysis: Methodologies and Applications ISBN: 3319823876 ISBN-13(EAN): 9783319823874 Издательство: Springer Рейтинг: Цена: 10976.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data.
Автор: Management Association Information Reso Название: Big Data: Concepts, Methodologies, Tools, and Applications, VOL 1 ISBN: 1668427990 ISBN-13(EAN): 9781668427996 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 90288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Management Association Information Reso Название: Big Data: Concepts, Methodologies, Tools, and Applications, VOL 4 ISBN: 1668428024 ISBN-13(EAN): 9781668428023 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 90288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Management Association Information Reso Название: Big Data: Concepts, Methodologies, Tools, and Applications, VOL 2 ISBN: 1668428008 ISBN-13(EAN): 9781668428009 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 90288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Management Association Information Reso Название: Big Data: Concepts, Methodologies, Tools, and Applications, VOL 3 ISBN: 1668428016 ISBN-13(EAN): 9781668428016 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 90288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Wang Название: High Performance Computing for Big Data ISBN: 1498783996 ISBN-13(EAN): 9781498783996 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It covers fundamental issues in Big Data research, including emerging architectures for data-intensive applications, novel analytical strategies to boost data processing, and cutting-edge applications.
Автор: Arora Название: Conquering Big Data with High Performance Computing ISBN: 3319337408 ISBN-13(EAN): 9783319337401 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.
Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.
Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.
Автор: Lucio Grandinetti; Seyedeh Leili Mirtaheri; Reza S Название: High-Performance Computing and Big Data Analysis ISBN: 3030334945 ISBN-13(EAN): 9783030334949 Издательство: Springer Рейтинг: Цена: 11220.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes revised and selected papers from the Second International Congress on High-Performance Computing and Big Data Analysis, TopHPC 2019, held in Tehran, Iran, in April 2019.
The 37 full papers and 2 short papers presented in this volume were carefully reviewed and selected from a total of 103 submissions. The papers in the volume are organized acording to the following topical headings: deep learning; big data analytics; Internet of Things.- data mining, neural network and genetic algorithms; performance issuesand quantum computing.
Описание: 1. Data Center for Smart Issues: Energy and Sustainability Issue.- 2 Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Process.- 3. An Empirica Study on Teleworking among Slovakia's Office-Based Academics.- 4. DSS for Pro-Active Flood Management of Water Reservoir Systems.- 5. exhiSTORY: Small Self-Organizing Exhibits.- 6. IoT Cloud Design Patterns.- 7. Cloud-based mHealth Streaming for IoT Processing.- 8. A System for Monitoring Water Quality Parameters in Rivers: Challenges and Solutions. 9. A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments.- 10. Online Real-time Monitoring and Processing of Medical Parameters: A Cloud-based Solution.- 11. Cloud-Enabled Modeling of Sensor Networks in Educational Settings.- 12. Methods and Techniques for Automatic Identification System Data Reduction.- 13. Energy Efficiency of Arduino Sensors Platform based on Mobile-Cloud: A Bicycle Lights Use-Case.- 14. Process Control Using Cloud Computing Architectures.
Описание: This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. presents techniques for machine learning for big data, and identifying duplicate records in data repositories;
Автор: Consoli Sergio, Reforgiato Recupero Diego, Saisana Michaela Название: Data Science for Economics and Finance: Methodologies and Applications ISBN: 3030668908 ISBN-13(EAN): 9783030668907 Издательство: Springer Цена: 4877.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Описание: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL
This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications.
Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.
Audience
Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
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