This book focuses on one of the major challenges of the newly created scientific domain known as data science: turning data into actionable knowledge in order to exploit increasing data volumes and deal with their inherent complexity. Actionable knowledge has been qualitatively and intensively studied in management, business, and the social sciences but in computer science and engineering, its connection has only recently been established to data mining and its evolution, ‘Knowledge Discovery and Data Mining’ (KDD). Data mining seeks to extract interesting patterns from data, but, until now, the patterns discovered from data have not always been ‘actionable’ for decision-makers in Socio-Technical Organizations (STO). With the evolution of the Internet and connectivity, STOs have evolved into Cyber-Physical and Social Systems (CPSS) that are known to describe our world today. In such complex and dynamic environments, the conventional KDD process is insufficient, and additional processes are required to transform complex data into actionable knowledge. Readers are presented with advanced knowledge concepts and the analytics and information fusion (AIF) processes aimed at delivering actionable knowledge. The authors provide an understanding of the concept of ‘relation’ and its exploitation, relational calculus, as well as the formalization of specific dimensions of knowledge that achieve a semantic growth along the AIF processes. This book serves as an important technical presentation of relational calculus and its application to processing chains in order to generate actionable knowledge. It is ideal for graduate students, researchers, or industry professionals interested in decision science and knowledge engineering.

Today, wellbeing is high on the personal and societal agenda, but thinking about wellbeing certainly is not a new phenomenon. The Greek philosopher Aristotle, for example, came up with the concept of Eudaimonia – the contented state of feeling healthy, happy, and prosperous – and this concept has been influential up until today. Starting from Augustine's thoughts on the topic of wellbeing, which had a great influence on theologians and others in the Early Modern Era, the contributions in this book reflect on a variety of topics ranging from wellbeing for the soul and the body to broader related concepts and theories approaching the theme from such disciplines as music, literature, history and theology.

This is a clarification of and development upon my previous work. It includes a rework of "Concerning the weakest coherent formalization of methodological skepticism as a Bayesian updater" and "On the finitist Wolfram physics model", then there is an outline of finite content theory and mathematical notes in various areas. Digital phenomenology itself is the study of a finitist (and therefore discrete) phenomenalism. It also includes my work on predictive liquid democracy, where liquid democracy is combined with prediction markets. The system allows for local satisfaction of Condorcet's jury theorem extended to multiple alternatives. See the part about predictive liquid democracy.

This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018. The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer.

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to: • understand what big data is and the factors that are involved • understand the inner workings of MapReduce, which is essential for certification exams • learn the features and weaknesses of MapReduce • set up Hadoop clusters with 100s of physical/virtual machines • create a virtual machine in AWS • write MapReduce with Eclipse in a simple way • understand other big data processing tools and their applications

Democracy is grounded in the open exchange of knowledge and ideas, but its social and political institutions are being systematically destroyed by a flood of monopoly-driven information marketing. Douglas LeMaster explains why the stakes are so high in this detailed, academic work. Democracy, he says, is the only form of social organization that is not exploitive by design. To prove his point, he examines how human history has been dominated by monopolies, such as the priestly monopolies of the great religions, the land monopolies of kings and aristocracies, the marketplace monopolies of capitalists, and most recently-a reinvented and secular information monopoly. While this latest monopoly is still in its formative stage, it's founded on corporate control of expressive rights, copyright, and patent-and it should concern all of us. Join LeMaster as he takes a close look at the latest form of an insidious disease and reveals how these latest

This practical book explores collaborative inquiry as an approach to research and change in organizations where internal members and external researchers work together as partners to address organizational issues and create knowledge about changing organizations.

The ability to uncover, share, and utilize knowledge is one of the most vital components to the success of any organization. While new technologies and techniques of knowledge dissemination are promising, there is still a struggle to derive and circulate meaningful information from large data sets. Strategic Data-Based Wisdom in the Big Data Era combines the latest empirical research findings, best practices, and applicable theoretical frameworks surrounding data analytics and knowledge acquisition. Providing a multi-disciplinary perspective of the subject area, this book is an essential reference source for professionals and researchers working in the field of knowledge management who would like to improve their understanding of the strategic role of data-based wisdom in different types of work communities and environments.

This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.