In today's society, the utilization of social media platforms has become an abundant forum for individuals to post, share, tag, and, in some cases, overshare information about their daily lives. As significant amounts of data flood these venues, it has become necessary to find ways to collect and evaluate this information. Social Media Data Extraction and Content Analysis explores various social networking platforms and the technologies being utilized to gather and analyze information being posted to these venues. Highlighting emergent research, analytical techniques, and best practices in data extraction in global electronic culture, this publication is an essential reference source for researchers, academics, and professionals.

Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains introduces the reader to recent research activities in the field of data mining. This book covers association mining, classification, mobile marketing, opinion mining, microarray data mining, internet mining and applications of data mining on biological data, telecommunication and distributed databases, among others, while promoting understanding and implementation of data mining techniques in emerging domains.

This proceeding features papers discussing big data innovation for sustainable cognitive computing. The papers feature details on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on cognitive computing technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform. The 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019) took place in Coimbatore, India on December 12-13, 2019. Contains proceedings from 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019), Coimbatore, India, December 12-13, 2019; Features topics ranging from Data Science for Cognitive Analysis to Internet-Based Cognitive Platforms; Includes contributions from researchers, academics, and professionals from around the world.

This book provides a comprehensive introduction to the application of artificial intelligence in social computing, from fundamental data processing to advanced social network computing. To broaden readers' understanding of the topics addressed, it includes extensive data and a large number of charts and references, covering theories, techniques and applications. It particularly focuses on data collection, data mining, artificial intelligence algorithms in social computing, and several key applications of social computing application, and also discusses network propagation mechanisms and dynamic analysis, which provide useful insights into how information is disseminated in online social networks. This book is intended for readers with a basic knowledge of advanced mathematics and computer science.

This book is intended as a guide to and manual on modeling complex problems in Multi Criteria Decision Making (MCDM). It encourages practitioners to consider the practicalities of real-world scenarios when modeling, while at the same time providing tips and examples of how to incorporate these realities into the initial decision matrix. The goal is to help readers build a decision matrix that replicates reality as closely as possible. Once this matrix has been constructed, the Decision Maker (DM) can select from more than a hundred MCDM methods the one that best fits the requirements and conditions of the matrix. The book features cases taken from real-world scenarios, which deal with various fields, aspects, and characteristics, and are solved using the SIMUS (Sequential Interactive Modeling for Urban Systems) method. This book is a valuable tool for practitioners, researchers and students dealing with MCDM problems.

Recent years have witnessed giant leaps in the strength of database technologies, creating a new level of capability to develop advanced applications that add value at unprecedented levels in all areas of information management and utilization. Parallel to this evolution is a need in the academia and industry for authoritative references to the research in this area, to establish a comprehensive knowledge base that will enable the information technology and managerial communities to realize maximum benefits from these innovations. Advanced Principles for Improving Database Design, Systems Modeling, and Software Development presents cutting-edge research and analysis of the most recent advancements in the fields of database systems and software development. This book provides academicians, researchers, and database practitioners with an exhaustive collection of studies that, together, represent the state of knowledge in the field.

The book highlights new trends and challenges in research on agents and the new digital and knowledge economy. It includes papers on business process management, agent-based modeling and simulation and anthropic-oriented computing that were originally presented at the 14th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2020), being held as a Virtual Conference in June 17-19, 2020. The respective papers cover topics such as software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature inspired manufacturing, all of which contribute to the modern digital economy.

This book discusses the opportunities and conditions that digital technology provides to extend, innovate and differentiate the services offered by consulting companies. It introduces suitable artefacts like web-based consulting platforms, consulting applications, semantic technologies and tools for data mining and collaboration. Furthermore it examines concepts to evaluate the virtualization of consulting processes and showcases how solutions can be developed to blend traditional and digital consulting models. Presenting state-of-the-art research and providing a comprehensive overview of the methods and techniques needed for digital transformation in the consulting industry, the book serves as both a guide and a roadmap for innovative consulting companies.

In the last few years, there has been an increased advancement and evolution in semantic web and information systems in a variety of fields. The integration of these approaches to ontology engineering, sophisticated methods and algorithms for open linked data extraction, and advanced decision-making creates new opportunities for a bright future. Innovations, Developments, and Applications of Semantic Web and Information Systems is a critical scholarly resource that discusses integrated methods of research and analytics in information technology. Featuring coverage on a broad range of topics, such as cognitive computing, artificial intelligence, machine learning, data analysis, and algorithms, this book is geared towards researchers, academicians, and professionals seeking current information on semantic web and information systems.

This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend's saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author's Github website.

This book introduces social manufacturing, the next generation manufacturing paradigm that covers product life cycle activities that deal with Internet-based organizational and interactive mechanisms under the context of socio-technical systems in the fields of industrial and production engineering. Like its subject, the book's approach is multi-disciplinary, including manufacturing systems, operations management, computational social sciences and information systems applications. It reports on the latest research findings regarding the social manufacturing paradigm, the architecture, configuration and execution of social manufacturing systems and more. Further, it describes the individual technologies enabled by social manufacturing for each topic, supported by case studies. The technologies discussed include manufacturing resource minimalization and their socialized reorganizations, blockchain models in cybersecurity, computing and decision-making, social business relationships and organizational networks, open product design, social sensors and extended cyber-physical systems, and social factory and inter-connections. This book helps engineers and managers in industry to practice social manufacturing, as well as offering a systematic reference resource for researchers in manufacturing. Students also benefit from the detailed discussions of the latest research and technologies that will have been put into practice by the time they graduate.

This book constitutes the refereed proceedings of the 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, held virtually and in Hersonissos, Crete, Greece, in June 2021. The 50 full papers and 11 short papers presented were carefully reviewed and selected from 113 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: adaptive modeling/ neuroscience; AI in biomedical applications; AI impacts/ big data; automated machine learning; autonomous agents; clustering; convolutional NN; data mining/ word counts; deep learning; fuzzy modeling; hyperdimensional computing; Internet of Things/ Internet of energy; machine learning; multi-agent systems; natural language; recommendation systems; sentiment analysis; and smart blockchain applications/ cybersecurity. Chapter "Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-based Approaches: Challenges and Perspective" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19.

This book contains a selection of papers presented during a special workshop on Complexity Science organized as part of the 9th International Conference on GIScience 2016. Expert researchers in the areas of Agent-Based Modeling, Complexity Theory, Network Theory, Big Data, and emerging methods of Analysis and Visualization for new types of data explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.

Quantitative Intelligence Analysis describes the model-based method of intelligence analysis that represents the analyst's mental models of a subject, as well as the analyst's reasoning process exposing what the analyst believes about the subject, and how they arrived at those beliefs and converged on analytic judgments. It includes: *Specific methods of explicitly representing the analyst's mental models as computational models; *dynamic simulations and interactive analytic games; *the structure of an analyst's mental model and the theoretical basis for capturing and representing the tacit knowledge of these models explicitly as computational models detailed description of the use of these models in rigorous, structured analysis of difficult targets; *model illustrations and simulation descriptions; *the role of models in support of collection and operations; *case studies that illustrate a wide range of intelligence problems; *And a recommended curriculum for technical analysts.

Technological innovation and evolution continues to improve personal and professional lifestyles, as well as general organizational and business practices; however, these advancements also create potential issues in the security and privacy of the user's information. Innovative Solutions for Access Control Management features a comprehensive discussion on the trending topics and emergent research in IT security and governance. Highlighting theoretical frameworks and best practices, as well as challenges and solutions within the topic of access control and management, this publication is a pivotal reference source for researchers, practitioners, students, database vendors, and organizations within the information technology and computer science fields.

'Emerging Technologies of Text Mining' provides the most recent technical information related to the computational models of the TM process.

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

This book introduces new models based on R-calculus and theories of belief revision for dealing with large and changing data. It extends R-calculus from first-order logic to propositional logic, description logics, modal logic and logic programming, and from minimal change semantics to subset minimal change, pseudo-subformula minimal change and deduction-based minimal change (the last two minimal changes are newly defined). And it proves soundness and completeness theorems with respect to the minimal changes in these logics. To make R-calculus computable, an approximate R-calculus is given which uses finite injury priority method in recursion theory. Moreover, two applications of R-calculus are given to default theory and semantic inheritance networks. This book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners in the field of logic. Also it is very useful for all those who are interested in data, digitization and correctness and consistency of information, in modal logics, non monotonic logics, decidable/undecidable logics, logic programming, description logics, default logics and semantic inheritance networks.

This book uses motivating examples and real-life attack scenarios to introduce readers to the general concept of fault attacks in cryptography. It offers insights into how the fault tolerance theories developed in the book can actually be implemented, with a particular focus on a wide spectrum of fault models and practical fault injection techniques, ranging from simple, low-cost techniques to high-end equipment-based methods. It then individually examines fault attack vulnerabilities in symmetric, asymmetric and authenticated encryption systems. This is followed by extensive coverage of countermeasure techniques and fault tolerant architectures that attempt to thwart such vulnerabilities. Lastly, it presents a case study of a comprehensive FPGA-based fault tolerant architecture for AES-128, which brings together of a number of the fault tolerance techniques presented. It concludes with a discussion on how fault tolerance can be combined with side channel security to achieve protection against implementation-based attacks. The text is supported by illustrative diagrams, algorithms, tables and diagrams presenting real-world experimental results.

This book provides theoretical perspectives and practical experiences on smart governance for smart cities. It presents a balanced linkage between research, policies and practices on this area. The authors discuss the sustainability challenges raised by rapid urbanization, challenges with smart governance models in various countries, and a new governance paradigm seen as a capable approach able to overcome social, economic and environmental sustainability problems. The authors include case studies on transformation, adaption and transfers; and country, regional, municipal contextualization. Also included are best practices on monitoring and evaluating smart governance and impact assessment. The book features contributions from researchers, academics, and practitioners in the field. Analyzes smart governance for cities from a variety of perspectives and a variety of sectors - both in theory and in practice Features information on the linkage between United Nations Sustainable Development Goals and smart governance Covers the connection between research, policies and practice in smart governance for smart cities

This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

This book constitutes the refereed proceedings of the 17th IFIP WG 2.13 International Conference on Open Source Systems, OSS 2021, held virtually in May 2021.The 4 full papers and 3 short papers presented were carefully reviewed and selected from 23 submissions. The papers cover a wide range of topics in the field of free/libre open source software (FLOSS) and discuss theories, practices, experiences, and tools on development and applications of OSS systems, with a specific focus on two aspects:(a) the development of open source systems and the underlying technical, social, and economic issue, (b) the adoption of OSS solutions and the implications of such adoption both in the public and in the private sector.