Автор: by Laura Po; Nikos Bikakis; Federico Desimoni Название: Linked Data Visualization ISBN: 1681737256 ISBN-13(EAN): 9781681737256 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 8039.00 р. Наличие на складе: Нет в наличии.
Описание: Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains.
The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.
This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios.
The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.
Автор: Federico Desimoni, George Papastefanatos, Laura Po, Nikos Bikakis Название: Linked Data Visualization: Techniques, Tools, and Big Data ISBN: 1681737272 ISBN-13(EAN): 9781681737270 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10811.00 р. Наличие на складе: Нет в наличии.
Описание: Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains.
The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.
This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios.
The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.
Описание: Big data analytics utilizes a wide range of software and analytical tools to provide immediate, relevant information for efficient decision-making. Companies are recognizing the immense potential of BDA, but ensuring the data is appropriate and error-free is the largest hurdle in implementing BDA applications.The Handbook of Research on Organizational Transformations through Big Data Analytics not only catalogues the existing platforms and technologies, it explores new trends within the field of big data analytics (BDA). Containing new and existing research materials and insights on the various approaches to BDA; this publication is intended for researchers, IT professionals, and CIOs interested in the best ways to implement BDA applications and technologies.
Автор: Celebi Numan Название: Intelligent Techniques for Data Analysis in Diverse Settings ISBN: 1522500758 ISBN-13(EAN): 9781522500759 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data analysis forms the basis of many forms of research ranging from the scientific to the governmental. With the advent of machine intelligence and neural networks, extracting, modeling, and approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other.Intelligent Techniques for Data Analysis in Diverse Settings addresses the specialized requirements of data analysis in a comprehensive way. This title contains a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organizational aspects of the field. This book is ideally designed for IT professionals and students, data analysis specialists, healthcare providers, and policy makers.
Описание: The representation of abstract data and ideas can be a difficult and tedious task to handle when learning new concepts; however, the advances in emerging technology have allowed for new methods of representing such conceptual data.Information Visualization Techniques in the Social Sciences and Humanities is a critical scholarly resource that examines the application of information visualization in the social sciences and humanities. Featuring coverage on a broad range of topics such as social network analysis, complex systems, and visualization aesthetics, this book is geared towards professionals, students, and researchers seeking current research on information visualization.
Автор: Munish Kumar, Manish Kumar Jindal, Simpel Rani Jindal, R. K. Sharma, Anupam Garg Название: Feature Extraction and Classification Techniques for Text Recognition ISBN: 1799824063 ISBN-13(EAN): 9781799824060 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 38669.00 р. Наличие на складе: Нет в наличии.
Описание: Presents innovative research on the fusion and hybridization of various features and classifiers for document analysis and recognition. The book highlights a range of topics, including adaptive boosting, writer identification, and signature verification.
Автор: Yim Aldrin, Yu Allen, Chung Claire Название: Matplotlib for Python Developers, Second Edition ISBN: 178862517X ISBN-13(EAN): 9781788625173 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a useful resource to perform data visualization with Python using the latest version of Matplotlib (2.1.x). You will create a variety of graphs and charts, and embed your plots within different third party tools. By the end of the book, you will build attractive, insightful and powerful visualizations to make better sense of your data.
Автор: Gulati Harish Название: Hands-On SAS For Data Analysis ISBN: 178883982X ISBN-13(EAN): 9781788839822 Издательство: Неизвестно Рейтинг: Цена: 10114.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: SAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It allows for faster, easier processing of data and empowers you to get valuable business insights for effective decision-making. This book will serve as a comprehensive guide you can refer to while preparing for your SAS certification exam.
Автор: Keller Benjamin Название: Mastering Matplotlib 2.x ISBN: 1789617693 ISBN-13(EAN): 9781789617696 Издательство: Неизвестно Рейтинг: Цена: 6068.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Mastering Matplotlib covers the use cases and encounter unusual cases that requires more powerful tools. With the easy to follow examples and high end components of matplotlib, this book will enable to develop advanced and interactive plots using Python scripting and Matplotlib
Описание: To start with you will cover the basics of graph analytics, Cypher querying language, components of graph architecture, and more. You will implement Neo4j techniques to understand various graph analytics methods to reveal complex relationships in data. You will understand how machine learning can be used to perform smarter graph analytics.
This book is aimed to help instructional designers, science game designers, science faculty, lab designers, and content developers in designing interactive learning experiences using emerging technologies and cyberlearning. The proposed solutions are for undergraduate and graduate scientific communication, engineering courses, scientific research communication, and workforce training.
Reviewing across the science education literature reveals various aspects of unresolved challenges or inabilities in the visualization of scientific concepts. Visuospatial thinking is the fundamental part of learning sciences; however, promoting spatial thinking has not been emphasized enough in the educational system (Hegarty, 2014). Cognitive scientists distinguish between the multiple aspects of spatial ability and stresse that various problems or disciplines require different types of spatial skills. For example, the spatial ability to visualize anatomy cross-sections is significantly associated with mental rotation skills. The same is true for physical problems that often deal with spatial representations. However, most of the physics problems are marked by dynamicity, and visualizing dynamicity is inferred by the integrations of different participating components in the system. Therefore, what is needed for learning dynamicity is visualizing the mental animation of static episodes.
This book is a leap into designing framework for using mixed reality (XR) technologies and cyberlearning in communicating advanced scientific concepts. The intention is to flesh out the cognitive infrastructure and visuospatial demands of complex systems and compare them in various contexts and disciplines. The practical implementation of emerging technology can be achieved by foreseeing each XR technology's affordances and mapping those out to the cognitive infrastructure and visuospatial demands of the content under development.
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