This volume explores emerging research and pedagogy in analytics, collaboration, and decision support with an emphasis on business intelligence and social media. In general, the chapters help understand where technology involvement in human decisions is headed. Reading the chapters can help understand the opportunities and threats associated with the use of information technology in decision making. Computing and information technologies are reshaping our global society, but they can potentially reshape it in negative as well as positive ways. Analytics, collaboration and computerized decision support are powerful decision aiding and decision making tools that have enormous potential to impact crisis decision making, regulation of financial systems, healthcare decision making and many more important decision domains. Many information technologies can potentially support, assist and even decide for human decision makers. Despite the potential, some researchers think that we know the answers to how these technologies will change society. The "Wisdom of Crowds" or "Big Data" become the topic of the day and are soon replaced with new marketing terms. In many ways, mobile technology is just another form factor to adapt decision support capabilities too and experiment with new capabilities. The cloud is a nebulous metaphor that adds to the mystery of information technology. Wireless technology enables the ubiquitous presence of analytics and decision support. With new networking capabilities, collaboration is possible anywhere and everywhere using voice, video and text. Documents can be widely shared and massive numbers of documents can be carried on a small tablet computer. Recent developments in technologies impact the processes organizations use to make decisions. In addition, academics are looking for ways to enhance their pedagogy to train students to be more adept in understanding how emerging technology will be used effectively for decision making in organizations. The chapters are based on papers originally reviewed at the Special Interest Group on Decision Support Systems (SIGDSS) Workshop at the 2013 International Conference on Information Systems (ICIS 2013). Ultimately this volume endeavors to find a balance between systematizing what we know, so we can teach our findings from prior research better, and stimulating excitement to move the field in new directions.

This is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. The decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). The act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. The book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. It is meant to answer several questions, such as: a) “How are evolving the business models towards the ever more collaborative schemes?”; b) “What is the role of the decision-maker in the new context?” c) “What are the basic attributes and trends in the domain of decision-supporting information systems?”; d) “Which are the basic methods to aggregate the individual preferences?” e)“What is the impact of modern information and communication technologies on the design and usage of decision support systems for groups of people?”.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation

This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.

Social media has become an integral part of society as social networking has become a main form of communication and human interaction. To stay relevant, businesses have adopted social media tactics to interact with consumers, conduct business, and remain competitive. Social technologies have reached a vital point in the business world, being essential in strategic decision-making processes, building relationships with consumers, marketing and branding efforts, and other important areas. While social media continues to gain importance in modern society, it is essential to determine how it functions in contemporary business. The Research Anthology on Strategies for Using Social Media as a Service and Tool in Business provides updated information on how businesses are strategically using social media and explores the role of social media in keeping businesses competitive in the global economy. The chapters will discuss how social tools work, what services businesses are utilizing, both the benefits and challenges to how social media is changing the modern business atmosphere, and more. This book is essential for researchers, instructors, social media managers, business managers, students, executives, practitioners, industry professionals, social media analysts, and all audiences interested in how social media is being used in modern businesses as both a service and integral tool.

The effects of recent economic and financial crises have reached an international scale. A number of different nations have experienced the fallout of these events, calling into question issues of accountability and reform in public management. The Handbook of Research on Modernization and Accountability in Public Sector Management is an essential scholarly publication that focuses on responsibility within public sector institutions and the importance of these institutions being ethical, transparent, and rigorous. Featuring coverage on a broad range of topics, such as corporate social responsibility, e-government, and financial accountability, this publication is geared toward regulatory authorities, researchers, managers, and professionals working in the public domain.

This book constitutes the refereed post-conference proceedings of the 11th IFIP TC 3 World Conference on Computers in Education, WCCE 2017, held in Dublin, Ireland, in July 2017. The 57 revised full papers and 10 short papers were carefully reviewed and selected from 116 submissions during two rounds of reviewing and improvement. The papers are organized in the following topical sections: futures of technology for learning and education; innovative practices with learning technologies; and computer science education and its future focus and development. Also included is "The Dublin Declaration" which identifies key aspects of innovation, development successes, concerns and interests in relation to ICT and education.

In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies. This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data. • Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making. • Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context. • Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions. • Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.

This two-volume set LNAI 13355 and 13356 constitutes the refereed proceedings of the 23rd International Conference on Artificial Intelligence in Education, AIED 2022, held in Durham, UK, in July 2022. The 40 full papers and 40 short papers presented together with 2 keynotes, 6 industry papers, 12 DC papers, 6 Workshop papers, 10 Practitioner papers, 97 Posters and Late-Breaking Results were carefully reviewed and selected from 243 submissions. The conference presents topics such as intelligent systems and the cognitive sciences for the improvement and advancement of education, the science and engineering of intelligent interactive learning systems. The theme for the AIED 2022 conference was „AI in Education: Bridging the gap between academia, business, and non-pro t in preparing future-proof generations towards ubiquitous AI."

Earthquake Occurrence provides the reader with a review of algorithms applicable for modeling seismicity, such as short-term earthquake clustering and pseudo-periodic long-term behavior of major earthquakes. The concept of the likelihood ratio of a set of observations under different hypotheses is applied for comparison among various models. In short-term models, known by the term ETAS, the occurrence space and time rate density of earthquakes is modeled as the sum of two terms, one representing the independent or spontaneous events, and the other representing the activity triggered by previous earthquakes. Examples of the application of such algorithms in real cases are also reported. Dealing with long-term recurrence models, renewal time-dependent models, implying a pseudo-periodicity of earthquake occurrence, are compared with the simple time-independent Poisson model, in which every event occurs regardless of what has occurred in the past. The book also introduces a number of computer codes developed by the authors over decades of seismological research.