Visual analytics
Encyclopedia
Visual analytics is an outgrowth of the fields information visualization
and scientific visualization
, that focuses on analytical reasoning facilitated by interactive visual interface
s.
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.
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:
These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.
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.
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 formal 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 hyphotheses 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.
In general the following paradigm is used to process the data:
Analyse First – Show the Important – Zoom, Filter and Analyse Further – Details on Demand
Related scientists
Information visualization
Information visualization is the interdisciplinary study of "the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems, library and bibliographic databases, networks of relations on the internet, and so forth".- Overview...
and scientific visualization
Scientific visualization
Scientific visualization is an interdisciplinary branch of science according to Friendly "primarily concerned with the visualization of three-dimensional phenomena , where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps...
, that focuses on analytical reasoning facilitated by interactive visual interface
User interface
The user interface, in the industrial design field of human–machine interaction, is the space where interaction between humans and machines occurs. The goal of interaction between a human and a machine at the user interface is effective operation and control of the machine, and feedback from the...
s.
Overview
Visual analytics is "the science of analytical reasoning facilitated by visual interactive interfaces." It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. 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
Cognitive science
Cognitive science is the interdisciplinary scientific study of mind and its processes. It examines what cognition is, what it does and how it works. It includes research on how information is processed , represented, and transformed in behaviour, nervous system or machine...
, design
Design
Design as a noun informally refers to a plan or convention for the construction of an object or a system while “to design” refers to making this plan...
, and perceptual
Visual perception
Visual perception is the ability to interpret information and surroundings from the effects of visible light reaching the eye. The resulting perception is also known as eyesight, sight, or vision...
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.
Visual analytics has some overlapping goals and techniques with information visualization
Information visualization
Information visualization is the interdisciplinary study of "the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems, library and bibliographic databases, networks of relations on the internet, and so forth".- Overview...
and scientific visualization
Scientific visualization
Scientific visualization is an interdisciplinary branch of science according to Friendly "primarily concerned with the visualization of three-dimensional phenomena , where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps...
. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows:
- Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows).
- Information visualization handles abstract data structures such as trees or graphs.
- Visual analytics is especially concerned with sensemaking and reasoning.
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:
- by increasing cognitive resources, such as by using a visual resource to expand human working memory,
- by reducing search, such as by representing a large amount of data in a small space,
- by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
- by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce,
- by perceptual monitoring of a large number of potential events, and
- by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.
These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.
Scope
Visual analytics is a multidisciplinary field that includes the following focus areas:- Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making
- Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis
- Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences.
- Visual representations and interaction techniques that take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once
Analytical reasoning techniques
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:- Understanding past and present situations quickly, as well as the trends and events that have produced current conditions
- Identifying possible alternative futures and their warning signs
- Monitoring current events for emergence of warning signs as well as unexpected events
- Determining indicators of the intent of an action or an individual
- Supporting the decision maker in times of crisis.
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.
Data representations
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.Theories of visualization
Theories of visualization are:- "Semiology of Graphics" in 1967 written by Jacques BertinJacques BertinJacques Bertin was a French cartographer and theorist, known from his book Semiologie Graphique , edited in 1967...
e - "Languages of Art" from 1977 by Nelson GoodmanNelson GoodmanHenry Nelson Goodman was an American philosopher, known for his work on counterfactuals, mereology, the problem of induction, irrealism and aesthetics.-Career:...
- Jock D. MackinlayJock D. MackinlayJock D. Mackinlay is an American information visualization expert and Director of Visual Analysis at Tableau Software. With Stuart K. Card, George G...
's "Automated design of optimal visualization" (APT) from 1986, and - Leland WilkinsonLeland WilkinsonLeland Wilkinson is a statistician and computer scientist at SYSTAT Software Inc. Dr. Wilkinson developed SYSTAT in the early 1980s, sold it to SPSS in 1995, and now serves as Executive VP of SYSTAT Software Inc. in Chicago. His research focuses on scientific visualization and statistical...
's "Grammar of Graphics" from 1998,
Visual representations
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.Process
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 attributesAttribute (computing)
In computing, an attribute is a specification that defines a property of an object, element, or file. It may also refer to or set the specific value for a given instance of such....
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 formal the visual analytics process is a transformation
Data transformation
In metadata and data warehouse, a data transformation converts data from a source data format into destination data.Data transformation can be divided into two steps:...
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 hyphotheses 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.
In general the following paradigm is used to process the data:
Analyse First – Show the Important – Zoom, Filter and Analyse Further – Details on Demand
Flow As First Class Citizens in Computing
It is only in recent times that flows have been represented as first class data items to build the web technology e.g JSF flows or Spring Web Flows. Also the sense of flow and sense of focus has been proposed as two different senses in our brain, This is an example of how the science of visual analytics can bring sense and richness in our understanding and control of complex process in our computation and the processes in our brain.See also
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Related scientists
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- Robert E. HornRobert E. HornRobert E. Horn is an American political scientist, who taught at Harvard, Columbia, and Sheffield universities. Currently he is a visiting scholar at Stanford University's Center for the Study of Language and Information...
- Daniel A. KeimDaniel A. KeimDaniel A. Keim is a German computer scientist and full professor in the Computer Science department at the University of Konstanz, Germany. He received his Ph.D. in Computer Science from the University of Munich in 1994...
- Theresa-Marie RhyneTheresa-Marie RhyneTheresa-Marie Rhyne is a recognized expert in the field of computer-generated visualization and a consultant who specializes in applying artistic color theories to visualization and digital media...
- Lawrence J. RosenblumLawrence J. RosenblumLawrence Jay Rosenblum is an American mathematician, and Program Director for Graphics and Visualization at the National Science Foundation.- Work :...
- John StaskoJohn StaskoJohn Thomas Stasko III is a Professor in and the Associate Chair of the School of Interactive Computing in the College of Computing at the Georgia Tech, where he joined the faculty in 1989. He also is one of the founding members of the Graphics, Visualization, and Usability Center there...
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- William Ribarsky
Further reading
- Boris Kovalerchuk and James Schwing (2004). Visual and Spatial Analysis: Advances in Data Mining, Reasoning, and Problem Soving
- Guoping Qiu (2007). Advances in Visual Information Systems: 9th International Conference (VISUAL).
- IEEE, Inc. Staff (2007). Visual Analytics Science and Technology (VAST), A Symposium of the IEEE 2007.
- May Yuan, Kathleen and Stewart Hornsby (2007). Computation and Visualization for Understanding Dynamics in Geographic Domains.
External links
- VisMaster Visual Analytics – Mastering the Information Age
- SPP - Scalable Visual Analytics
- Visual Analytics a course by Robert Kosara, 2007.
- IEEE Visual Analytics Science and Technology (VAST) Symposium
- National Visualization and Analytics Center (NVAC)
- Visual Analytics Digital Library (VADL)
- GeoAnalytics.net - GeoSpatial Visual Analytics, ICA Commission on GeoVizualisation
- International Cartographic Association (ICA), the world body for mapping and GIScience professionals
- flowingdata.com, visualization and statistics blog
- visual-analytics.org, visual analytics blog
- Middlesex University Interaction Design Centre weblog, visual analytics blog and resources
- Visual Analytics :Focusing on web pages design and content as a visual analytics of semantics and human understanding, one of the pioneer users of the phrase Visual Analytics
- Understanding Link Analysis, link and visual analysis resource