This book constitutes the refereed proceedings of the Second International Workshop on Semantics, Analytics, Visualization,- Enhancing Scholarly Data,- SAVE-SD 2016, held in Montreal, QC, Canada, in April 2016. The 5 full papers, 6 demo and poster papers and 2 position papers, were carefully reviewed and selected from 16 submissions. The papers are organized in two topical sections: "Extracting Knowledge from Research Publications" and "Semantic Technologies for Citation and Topic Analysis".

This book constitutes the refereed proceedings of the 3rd International Workshop, SAVE-SD 2017, held in Perth, Australia, in April 2017, and the 4th International Workshop, SAVE-SD 2018, held in Lyon, France, in April 2018. The 6 full, 2 position and 4 short papers were selected from 16 submissions. The papers describe multiple ways in which scholarly dissemination can be approved: Creating structured data, providing methods for semantic computational analysis and designing systems for navigating. This allows a variety of stakeholders to understand research dynamics, predict trends and evaluate the quality of research.

Dealing with visualization of the second-generation Web, this text presentsresearch on topics such as: visualization of semantic information and metadata;querying XML documents; topic map visualization; visual modelling of XML/RDFontologies; e-commerce and Web search applications; and more.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

In scholarly digital editing, the established practice for semantically enriching digital texts is to add markup to a linear string of characters. Graph data-models provide an alternative approach, which is increasingly being given serious consideration. Labelled-property-graph databases, and the W3c's semantic web recommendation and associated standards (RDF and OWL) are powerful and flexible solutions to many of the problems that come with embedded markup. This volume explores the combination of scholarly digital editions, the graph data-model, and the semantic web from three perspectives: infrastructures and technologies, formal models, and projects and editions.

As libraries transition into the digital age, they encounter a pressing challenge: outdated information systems hinder their ability to meet the diverse needs of patrons. Traditional library management systems struggle to cope with the demands of modern users, resulting in inefficient resource allocation, limited accessibility, and disjointed user experiences. This disconnect between antiquated systems and evolving user expectations poses a significant barrier to libraries striving to remain relevant in an increasingly digital world. Improving Library Systems with AI: Applications, Approaches, and Bibliometric Insights presents a comprehensive solution to this pressing problem. By integrating modern digital tools and technologies, libraries can revolutionize their information systems, enhancing accessibility, efficiency, and user satisfaction. This book offers practical insights and strategies for modernizing library services and operations, from digitizing physical resources to implementing advanced search algorithms and data analytics. Librarians, administrators, and technology providers will find invaluable guidance on navigating the complexities of digital transformation and maximizing the impact of their efforts.

Mathematical Modelling of Gas-Phase Complex Reaction Systems: Pyrolysis and Combustion, Volume 45, gives an overview of the different steps involved in the development and application of detailed kinetic mechanisms, mainly relating to pyrolysis and combustion processes. The book is divided into two parts that cover the chemistry and kinetic models and then the numerical and statistical methods. It offers a comprehensive coverage of the theory and tools needed, along with the steps necessary for practical and industrial applications. - Details thermochemical properties and "ab initio" calculations of elementary reaction rates - Details kinetic mechanisms of pyrolysis and combustion processes - Explains experimental data for improving reaction models and for kinetic mechanisms assessment - Describes surrogate fuels and molecular reconstruction of hydrocarbon liquid mixtures - Describes pollutant formation in combustion systems - Solves and validates the kinetic mechanisms using numerical and statistical methods - Outlines optimal design of industrial burners and optimization and dynamic control of pyrolysis furnaces - Outlines large eddy simulation of turbulent reacting flows

With higher education turning towards data analytics as the next big advance in technology, it is important to look at how information is gathered and visualized for accurate comprehension, analysis, and decision-making. Packaging Digital Information for Enhanced Learning and Analysis: Data Visualization, Spatialization, and Multidimensionality brings together effective practices for the end-to-end capture and web based presentation of information for comprehension, analysis, and decision-making. This publication is beneficial for educators, trainers, instructional designers, web designers, and graduate students interested in improving analytical tools.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.