Data Visualization and Knowledge Engineering
Data Visualization and Knowledge Engineering

Author: Jude Hemanth

Publisher: Springer

Published: 2019-08-09

Total Pages: 319

ISBN-13: 3030257975

DOWNLOAD EBOOK

This book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns. A variety of scalable data visualization techniques are required to deal with constantly increasing volume of data in different formats. Knowledge engineering deals with the simulation of the exchange of ideas and the development of smart information systems in which reasoning and knowledge play an important role. Presenting research in areas like data visualization and knowledge engineering, this book is a valuable resource for students, scholars and researchers in the field. Each chapter is self-contained and offers an in-depth analysis of real-world applications. It discusses topics including (but not limited to) spatial data visualization; biomedical visualization and applications; image/video summarization and visualization; perception and cognition in visualization; visualization taxonomies and models; abstract data visualization; information and graph visualization; knowledge engineering; human–machine cooperation; metamodeling; natural language processing; architectures of database, expert and knowledge-based systems; knowledge acquisition methods; applications, case studies and management issues: data administration issues and knowledge; tools for specifying and developing data and knowledge bases using tools based on communication aspects involved in implementing, designing and using KBSs in cyberspace; Semantic Web.

Knowledge Engineering
Knowledge Engineering

Author: S. C. Mehrotra

Publisher: Alpha Science International Limited

Published: 2011

Total Pages: 328

ISBN-13: 9788184871234

DOWNLOAD EBOOK

KNOWLEDGE ENGINEERING (KE) and data mining are areas of common interest to researchers in AI, Pattern Recognition, Statistics, Databases, Knowledge Acquisition, Data Visualization, high performance computing, and expert systems. This book is divided in to seven major parts. Part one has focused on document and multi-document reconstruction and summarization, Medical Imaging, Opinion Mining, PCA & LDA, Cross co-relation and phase based matching. Whereas the Part two covers application areas of Data Mining like Data Cleaning, Weather forecasting and Web Mining. Part three covers HCI, ECG, Direct Manipulation Interface, Face Recognition in crowd, Gesture recognition for Mobile, Chaotic dynamics, epilepsy and Alzheimer's diagnosis, CAL, Devanagri character recognition and Speech Databases. Web Mining related areas like Clustering, Web usage Mining, Web log analysis, BI, Web indexing, Crawlers and Link Mining are covered in part four. The algorithms of Data Mining related to Decision Trees, Association Rules and Tries base Apriori algorithm, Decision support and GIS are covered in Part five. The sixth number part covers aspects of Security like density based approach, intrusion detection in Oracle, unbalanced datasets and dark block extraction. The last part number seven contains the other allied areas of Data Mining for the applications like customer review, SOA-Governance & planning, Mobile Ad-Hoc networks, KE Framework for technical education institutes, time series analysis, extraction of genetic features, KD in Agriculture crop production, Earthquake prediction and Credit Card fraud detection.

From Data and Information Analysis to Knowledge Engineering
From Data and Information Analysis to Knowledge Engineering

Author: Myra Spiliopoulou

Publisher: Springer Science & Business Media

Published: 2006-04-20

Total Pages: 780

ISBN-13: 3540313141

DOWNLOAD EBOOK

This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.

Knowledge Graphs
Knowledge Graphs

Author: Aidan Hogan

Publisher: Morgan & Claypool Publishers

Published: 2021-11-08

Total Pages: 257

ISBN-13: 1636392369

DOWNLOAD EBOOK

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Data Visualization
Data Visualization

Author: Frits H. Post

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 445

ISBN-13: 1461511771

DOWNLOAD EBOOK

Data visualization is currently a very active and vital area of research, teaching and development. The term unites the established field of scientific visualization and the more recent field of information visualization. The success of data visualization is due to the soundness of the basic idea behind it: the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes, and simulations involving data sets from diverse scientific disciplines and large collections of abstract data from many sources. These concepts are extremely important and have a profound and widespread impact on the methodology of computational science and engineering, as well as on management and administration. The interplay between various application areas and their specific problem solving visualization techniques is emphasized in this book. Reflecting the heterogeneous structure of Data Visualization, emphasis was placed on these topics: -Visualization Algorithms and Techniques; -Volume Visualization; -Information Visualization; -Multiresolution Techniques; -Interactive Data Exploration. Data Visualization: The State of the Art presents the state of the art in scientific and information visualization techniques by experts in this field. It can serve as an overview for the inquiring scientist, and as a basic foundation for developers. This edited volume contains chapters dedicated to surveys of specific topics, and a great deal of original work not previously published illustrated by examples from a wealth of applications. The book will also provide basic material for teaching the state of the art techniques in data visualization. Data Visualization: The State of the Art is designed to meet the needs of practitioners and researchers in scientific and information visualization. This book is also suitable as a secondary text for graduate level students in computer science and engineering.

Innovative Approaches of Data Visualization and Visual Analytics
Innovative Approaches of Data Visualization and Visual Analytics

Author: Huang, Mao Lin

Publisher: IGI Global

Published: 2013-07-31

Total Pages: 464

ISBN-13: 1466643102

DOWNLOAD EBOOK

Due to rapid advances in hardware and software technologies, network infrastructure and data have become increasingly complex, requiring efforts to more effectively comprehend and analyze network topologies and information systems. Innovative Approaches of Data Visualization and Visual Analytics evaluates the latest trends and developments in force-based data visualization techniques, addressing issues in the design, development, evaluation, and application of algorithms and network topologies. This book will assist professionals and researchers working in the fields of data analysis and information science, as well as students in computer science and computer engineering, in developing increasingly effective methods of knowledge creation, management, and preservation.

Knowledge Engineering for Modern Information Systems
Knowledge Engineering for Modern Information Systems

Author: Anand Sharma

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2022-01-19

Total Pages: 282

ISBN-13: 3110713691

DOWNLOAD EBOOK

Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.

Data Science, Data Visualization, and Digital Twins
Data Science, Data Visualization, and Digital Twins

Author: Sara Shirowzhan

Publisher: BoD – Books on Demand

Published: 2022-02-02

Total Pages: 118

ISBN-13: 1839629436

DOWNLOAD EBOOK

Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development.

Knowledge Engineering and Knowledge Management
Knowledge Engineering and Knowledge Management

Author: Patrick Lambrix

Publisher: Springer

Published: 2015-04-20

Total Pages: 239

ISBN-13: 3319179667

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of Satellite Events held at the 19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014 in November 2014. EKAW 2014 hosted three satellite workshops: VISUAL 2014, International Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics, EKM1, the First International Workshop on Educational Knowledge Management and ARCOE-Logic 2014, the 6th International Workshop on Acquisition, Representation and Reasoning about Context with Logic. This volume also contains the accepted contributions for the EKAW 2014 tutorials, demo and poster sessions.

Linked Data Visualization
Linked Data Visualization

Author: Laura Po

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 143

ISBN-13: 3031794907

DOWNLOAD EBOOK

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.