This book provides students with an in-depth understanding of the concepts, frameworks and processes used to analyze and present visual data for better decision-making. Expert contributors provide guidance in translating complex concepts from large data sets and how this translation drives management practice. The book’s first part provides a descriptive consideration of state-of-the-art science in visual design. The second part complements the first with a rich set of cases and visual examples, illustrating development and best practice to provide students with real-world context. Through their presentation of modern scientific principles, the editors inspire structured discussions of audience and design, recognizing differences in need, bias and effective processes across contexts and stakeholders. This cutting-edge resource will be of value to students in business analytics, business communication and management science classes, who will learn to be capable managers through the effective and direct visual communication of data. Researchers and practitioners will also find this an engaging and informative book.

Visual analytics has come a long way since its inception in 2005. The amount of data in the world today has increased significantly and experts in many domains are struggling to make sense of their data. Visual analytics is helping them conduct their analyses. While software developers have worked for many years to develop software that helps users do their tasks, this task is becoming more and more onerous, as understanding the needs and data used by expert users requires more than some simple usability testing during the development process. The need for a user-centered evaluation process was envisioned in Illuminating the Path, the seminal work on visual analytics by James Thomas and Kristin Cook in 2005. We have learned over the intervening years that not only will user-centered evaluation help software developers to turn out products that have more utility, the evaluation efforts can also help point out the direction for future research efforts. This book describes the efforts that go into analysis, including critical thinking, sensemaking, and various analytics techniques learned from the intelligence community. Support for these components is needed in order to provide the most utility for the expert users. There are a good number of techniques for evaluating software that hasbeen developed within the human-computer interaction (HCI) community. While some of these techniques can be used as is, others require modifications. These too are described in the book. An essential point to stress is that the users of the domains for which visual analytics tools are being designed need to be involved in the process. The work they do and the obstacles in their current processes need to be understood in order to determine both the types of evaluations needed and the metrics to use in these evaluations. At this point in time, very few published efforts describe more than informal evaluations. The purpose of this book is to help readers understand the need for more user-centered evaluations to drive both better-designed products and to define areas for future research. Hopefully readers will view this work as an exciting and creative effort and will join the community involved in these efforts.

This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

With the growth in our reliance on information systems and computer science information modeling and knowledge bases have become a focus for academic attention and research. The amount and complexity of information, the number of levels of abstraction and the size of databases and knowledge bases all continue to increase, and new challenges and problems arise every day.This book is part of the series Information Modelling and Knowledge Bases, which concentrates on a variety of themes such as the design and specification of information systems, software engineering and knowledge and process management.

This is an open access book. Time is an exceptional dimension with high relevance in medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data via visual representations enables us to learn from the past in order to predict, plan, and build the future. This second edition builds upon the great success of the first edition. It maintains a brief introduction to visualization and a review of historical time-oriented visual representations. At its core, the book develops a systematic view of the visualization of time-oriented data. Separate chapters discuss interaction techniques and computational methods for supporting the visual data analysis. Many examples and figures illustrate the introduced concepts and techniques. So, what is new for the second edition? First of all, the second edition is now published as an open-access book so that anyone interested in the visualization of time and time-oriented data can read it. Second, the entire content has been revised and expanded to represent state-of-the-art knowledge. The chapter on interaction support now includes advanced methods for interacting with visual representations of time-oriented data. The second edition also covers the topics of data quality as well as segmentation and labeling. The comprehensive survey of classic and contemporary visualization techniques now provides more than 150 self-contained descriptions accompanied by illustrations and corresponding references. A completely new chapter describes how the structured survey can be used for the guided selection of suitable visualization techniques. For the second edition, our TimeViz Browser, the digital pendant to the survey of visualization techniques, received a major upgrade. It includes the same set of techniques as the book, but comes with additional filter and search facilities allowing scientists and practitioners to find exactly the solutions they are interested in.

This book constitutes the revised selected papers of the 11th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2023, held in La Plata, Argentina*, in June 2023. The 14 full papers were carefully reviewed and selected from a total of 38 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing, Big Data, Machine and Deep Learning, Smart Cities and E-Government, Visualization.

The development of connected, communicating objects is showing no signs of slowing down. With an increasing number of objects available on the market, the evolution of the Internet of Things is leading to more and more fields being explored via information and communication sciences. This book analyzes the ecosystem of the Internet of Things by retracing the historical and technological context of the Internet's evolution from traditional to dynamic, social and semantic, and then towards this ecosystem of connected objects. The evolution of concepts surrounding the Internet of Things is explored via real-life examples of connected objects; both those used for specific functions and for more general everyday objects. Numerous issues associated with these new technological and digital transformations in a "hyperconnected" world, as well as the impact of the massive influx of connected objects, are discussed. The crucial questions of potential intrusion into the private lives of users as well that of security are then studied.

This book approaches macroprudential oversight from the viewpoint of three tasks. The focus concerns a tight integration of means for risk communication into analytical tools for risk identification and risk assessment. Generally, this book explores approaches for representing complex data concerning financial entities on low-dimensional displays. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. Accordingly, this book creates a Self-Organizing Financial Stability Map (SOFSM), and lays out a general framework for mapping the state of financial stability. Beyond external risk communication, the aim of the visual means is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence.

As the analysis of big datasets in sports performance becomes a more entrenched part of the sporting landscape, so the value of sport scientists and analysts with formal training in data analytics grows. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance provides the most authoritative and comprehensive guide to the use of analytics in sport and its application in sports performance, coaching, talent identification and sports medicine available. Employing an approach-based structure and integrating problem-based learning throughout the text, the book clearly defines the difference between analytics and analysis and goes on to explain and illustrate methods including: Interactive visualisation Simulation and modelling Geospatial data analysis Spatiotemporal analysis Machine learning Genomic data analysis Social network analysis Offering a mixed-methods case study chapter, no other book offers the same level of scientific grounding or practical application in sports data analytics. Sports Analytics is essential reading for all students of sports analytics, and useful supplementary reading for students and professionals in talent identification and development, sports performance analysis, sports medicine and applied computer science.