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Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces. [1]
Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." [2] It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. [3] Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. [4] As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.
Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst’s task of applying human judgments to reach conclusions from a combination of evidence and assumptions. [2]
Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows:
Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways: [2] [5]
These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.
Visual analytics is a multidisciplinary field that includes the following focus areas: [2]
Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must enable diverse analytical tasks such as: [2]
These tasks will be conducted through a combination of individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable hypothesis-based and scenario-based analytical techniques, providing support for the analyst to reason based on the available evidence. [2]
Data representations are structured forms suitable for computer-based transformations. These structures must exist in the original data or be derivable from the data themselves. They must retain the information and knowledge content and the related context within the original data to the greatest degree possible. The structures of underlying data representations are generally neither accessible nor intuitive to the user of the visual analytics tool. They are frequently more complex in nature than the original data and are not necessarily smaller in size than the original data. The structures of the data representations may contain hundreds or thousands of dimensions and be unintelligible to a person, but they must be transformable into lower-dimensional representations for visualization and analysis. [2]
Theories of visualization include: [3]
Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual reasoning through visual representations permits the analytical reasoning process to become faster and more focused. [2]
The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets S = S1, ..., Sm are chosen, whereas each Si , i ∈ (1, ..., m) consists of attributes Ai1, ..., Aik. The goal or output of the process is insight I. Insight is either directly obtained from the set of created visualizations V or through confirmation of hypotheses H as the results of automated analysis methods. This formalization of the visual analytics process is illustrated in the following figure. Arrows represent the transitions from one set to another one.
More formally the visual analytics process is a transformation F: S → I, whereas F is a concatenation of functions f ∈ {DW, VX, HY, UZ} defined as follows:
DW describes the basic data pre-processing functionality with DW : S → S and W ∈ {T, C, SL, I} including data transformation functions DT, data cleaning functions DC, data selection functions DSL and data integration functions DI that are needed to make analysis functions applicable to the data set.
VW, W ∈ {S, H} symbolizes the visualization functions, which are either functions visualizing data VS : S → V or functions visualizing hypotheses VH : H → V.
HY, Y ∈ {S, V} represents the hypotheses generation process. We distinguish between functions that generate hypotheses from data HS : S → H and functions that generate hypotheses from visualizations HV : V → H.
Moreover, user interactions UZ, Z ∈ {V, H, CV, CH} are an integral part of the visual analytics process. User interactions can either effect only visualizations UV : V → V (i.e., selecting or zooming), or can effect only hypotheses UH : H → H by generating a new hypotheses from given ones. Furthermore, insight can be concluded from visualizations UCV : V → I or from hypotheses UCH : H → I.
The typical data pre-processing applying data cleaning, data integration and data transformation functions is defined as DP = DT(DI(DC(S1, ..., Sn))). After the pre-processing step either automated analysis methods HS = {fs1, ..., fsq} (i.e., statistics, data mining, etc.) or visualization methods VS : S → V, VS = {fv1, ..., fvs} are applied to the data, in order to reveal patterns as shown in the figure above. [7]
In general the following paradigm is used to process the data:
Analyse First – Show the Important – Zoom, Filter and Analyse Further – Details on Demand [8]
Cognitive science is the interdisciplinary, scientific study of the mind and its processes with input from linguistics, psychology, neuroscience, philosophy, computer science/artificial intelligence, and anthropology. It examines the nature, the tasks, and the functions of cognition. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."
Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.
Scientific visualization is an interdisciplinary branch of science concerned with the visualization of scientific phenomena. It is also considered a subset of computer graphics, a branch of computer science. The purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data. Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.
Visualization or visualisation is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering and scientific purposes.
The following outline is provided as an overview of and topical guide to human–computer interaction:
Distributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s.
Geovisualization or geovisualisation, also known as cartographic visualization, refers to a set of tools and techniques supporting the analysis of geospatial data through the use of interactive visualization.
Data and information visualization is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data. When intended for the general public to convey a concise version of known, specific information in a clear and engaging manner, it is typically called information graphics.
The following outline is provided as an overview of and topical guide to thought (thinking):
Jock D. Mackinlay is an American information visualization expert and Vice President of Research and Design at Tableau Software. With Stuart Card, George G. Robertson and others he invented a number of information visualization techniques.
Lawrence Jay Rosenblum is an American mathematician, and Program Director for Graphics and Visualization at the National Science Foundation.
An interaction technique, user interface technique or input technique is a combination of hardware and software elements that provides a way for computer users to accomplish a single task. For example, one can go back to the previously visited page on a Web browser by either clicking a button, pressing a key, performing a mouse gesture or uttering a speech command. It is a widely used term in human-computer interaction. In particular, the term "new interaction technique" is frequently used to introduce a novel user interface design idea.
Cultural analytics refers to the use of computational, visualization, and big data methods for the exploration of contemporary and historical cultures. While digital humanities research has focused on text data, cultural analytics has a particular focus on massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media. Taking on the challenge of how to best explore large collections of rich cultural content, cultural analytics researchers developed new methods and intuitive visual techniques that rely on high-resolution visualization and digital image processing. These methods are used to address both the existing research questions in humanities, to explore new questions, and to develop new theoretical concepts that fit the mega-scale of digital culture in the early 21st century.
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.
The Troland Research Awards are an annual prize given by the United States National Academy of Sciences to two researchers in recognition of psychological research on the relationship between consciousness and the physical world. The areas where these award funds are to be spent include but are not limited to areas of experimental psychology, the topics of sensation, perception, motivation, emotion, learning, memory, cognition, language, and action. The award preference is given to experimental work with a quantitative approach or experimental research seeking physiological explanations.
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Interactive Visual Analysis (IVA) is a set of techniques for combining the computational power of computers with the perceptive and cognitive capabilities of humans, in order to extract knowledge from large and complex datasets. The techniques rely heavily on user interaction and the human visual system, and exist in the intersection between visual analytics and big data. It is a branch of data visualization. IVA is a suitable technique for analyzing high-dimensional data that has a large number of data points, where simple graphing and non-interactive techniques give an insufficient understanding of the information.
Visual computing is a generic term for all computer science disciplines dealing with images and 3D models, such as computer graphics, image processing, visualization, computer vision, virtual and augmented reality and video processing. Visual computing also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. The core challenges are the acquisition, processing, analysis and rendering of visual information. Application areas include industrial quality control, medical image processing and visualization, surveying, robotics, multimedia systems, virtual heritage, special effects in movies and television, and computer games.
The Visualization Handbook is a textbook by Charles D. Hansen and Christopher R. Johnson that serves as a survey of the field of scientific visualization by presenting the basic concepts and algorithms in addition to a current review of visualization research topics and tools. It is commonly used as a textbook for scientific visualization graduate courses. It is also commonly cited as a reference for scientific visualization and computer graphics in published papers, with almost 500 citations documented on Google Scholar.
Guided analytics is a sub-field at the interface of visual analytics and predictive analytics focused on the development of interactive visual interfaces for business intelligence applications. Such interactive applications serve the analyst to take important decisions by easily extracting information from the data.
Media related to Visual analytics at Wikimedia Commons