https://infovis-wiki.net/w/api.php?action=feedcontributions&user=86.96.226.20&feedformat=atomInfoVis:Wiki - User contributions [en]2024-03-28T23:14:15ZUser contributionsMediaWiki 1.40.1https://infovis-wiki.net/w/index.php?title=Visual_Analytics&diff=147796Visual Analytics2012-11-23T07:43:42Z<p>86.96.226.20: /* Papers */</p>
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<div>{{Definition|'''Visual analytics''' is the science of analytical reasoning facilitated by interactive visual interfaces. [Thomas and Cook, 2005]}}<br />
<br />
{{Quotation|People use visual analytics tools and techniques to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessment effectively for action.<br />
<br>Visual analytics is a multidisciplinary field that includes the following focus areas:<br><br />
*analytical reasoning techniques that let users obtain deep insights that directly support assessment, planning, and decision making;<br />
*visual representations and interaction techniques that exploit the human eye’s broad bandwidth pathway into the mind to let users see, explore, and understand large amounts of information simultaneously;<br />
*data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis; and<br />
*techniques to support production, presentation, and dissemination of analytical results to communicate information in the appropriate context to a variety of audiences.<br />
|[Thomas and Cook, 2005, 2006]}}<br />
<br />
[[Image:Keim06visual-analytics-disciplines.png|right|thumb|300px|Visual analytics as a highly interdisciplinary field of research.<br>[Keim et al., 2006]]]<br />
{{Quotation|'''Visual analytics''' is more than just visualization and can rather be seen as an integrated approach combining [[visualization]], [[human factors]] and [[data analysis]]. ... With respect to the field of [[visualization]], visual analytics integrates methodology from information analytics, geospatial analytics, and scientific analytics. Especially human factors (e.g., interaction, cognition, perception, collaboration, presentation, and dissemination) play a key role in the communication between human and computer, as well as in the decisionmaking process.|[Keim et al., 2006]}}<br />
<br />
{{Quotation|Visual analytics is the formation of abstract visual metaphors in combination with a human information discourse (usually some form of interaction) that enables detection of the expected and discovery of the unexpected within massive, dynamically changing information spaces. It is an outgrowth of the fields of scientific and information visualization but includes technologies from many other fields, including knowledge management, statistical analysis, cognitive science, decision science, and others.<br><br>This marriage of computation, visual representation, and interactive thinking supports intensive analysis. The goal is not only to permit users to detect expected events, such as might be predicted by models, but also to help users discover the unexpected—the surprising anomalies, changes, patterns, and relationships that are then examined and assessed to develop new insight.|[Cook et al., 2007]}}<br />
<br />
== Related Links ==<br />
*[http://vadl.cc.gatech.edu/ Visual Analytics Digital Library]<br />
*[http://www.cs.umd.edu/hcil/semvast/ SEMVAST] Scientific Evaluation Methods for Visual Analytics Science and Technology - Resource on Visual Analytics Evaluation<br />
<br />
== Visual Analytics @ YouTube ==<br />
*[http://www.youtube.com/watch?v=5i3xbitEVfs March 15, 2010 — VisMaster - Mastering the Information Age] What is Visual Analytics and why is it important for Europe to fund Research in this new domain?<br />
*[http://www.youtube.com/watch?v=2CMw4i9DiaM December 16, 2008 — Jigsaw] Using visualization and visual analytics to help investigative analysis and sensemaking. <br />
*[http://www.youtube.com/watch?v=cVL2Y6nfGiM November 03, 2008 — Mikael Jern on Visual Analytics] Visual Analytics Center, OECD Explorer<br />
*[http://www.youtube.com/watch?v=5uGRGqCFryg July 10, 2007 — Why Visual Analytics? - A conversation] A series of interviews to some of most known people in Visual Analytics, the science of reasoning through visualization<br />
*[http://www.youtube.com/watch?v=m_TQZwYL7MY December 12, 2006 — Visual Analytics Show segment from "BusinessNOW" on Maryland Public Television]<br />
<br />
== Basic Literature ==<br />
=== Books ===<br />
* J.J. Thomas and K.A. Cook (Eds.), [http://nvac.pnl.gov/agenda.stm Illuminating the Path: The Research and Development Agenda for Visual Analytics]. IEEE Press, 2005.<br />
* Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis and Florian Mansmann (Eds.), [http://www.vismaster.eu/book/ Mastering the Information Age – Solving Problems with Visual Analytics], Eurographics Association, 2010.<br />
You saved me a lot of hsasle just now.</div>86.96.226.20https://infovis-wiki.net/w/index.php?title=Visual_Analytics&diff=147795Visual Analytics2012-11-23T07:18:40Z<p>86.96.226.20: /* References */</p>
<hr />
<div>{{Definition|'''Visual analytics''' is the science of analytical reasoning facilitated by interactive visual interfaces. [Thomas and Cook, 2005]}}<br />
<br />
{{Quotation|People use visual analytics tools and techniques to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessment effectively for action.<br />
<br>Visual analytics is a multidisciplinary field that includes the following focus areas:<br><br />
*analytical reasoning techniques that let users obtain deep insights that directly support assessment, planning, and decision making;<br />
*visual representations and interaction techniques that exploit the human eye’s broad bandwidth pathway into the mind to let users see, explore, and understand large amounts of information simultaneously;<br />
*data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis; and<br />
*techniques to support production, presentation, and dissemination of analytical results to communicate information in the appropriate context to a variety of audiences.<br />
|[Thomas and Cook, 2005, 2006]}}<br />
<br />
[[Image:Keim06visual-analytics-disciplines.png|right|thumb|300px|Visual analytics as a highly interdisciplinary field of research.<br>[Keim et al., 2006]]]<br />
{{Quotation|'''Visual analytics''' is more than just visualization and can rather be seen as an integrated approach combining [[visualization]], [[human factors]] and [[data analysis]]. ... With respect to the field of [[visualization]], visual analytics integrates methodology from information analytics, geospatial analytics, and scientific analytics. Especially human factors (e.g., interaction, cognition, perception, collaboration, presentation, and dissemination) play a key role in the communication between human and computer, as well as in the decisionmaking process.|[Keim et al., 2006]}}<br />
<br />
{{Quotation|Visual analytics is the formation of abstract visual metaphors in combination with a human information discourse (usually some form of interaction) that enables detection of the expected and discovery of the unexpected within massive, dynamically changing information spaces. It is an outgrowth of the fields of scientific and information visualization but includes technologies from many other fields, including knowledge management, statistical analysis, cognitive science, decision science, and others.<br><br>This marriage of computation, visual representation, and interactive thinking supports intensive analysis. The goal is not only to permit users to detect expected events, such as might be predicted by models, but also to help users discover the unexpected—the surprising anomalies, changes, patterns, and relationships that are then examined and assessed to develop new insight.|[Cook et al., 2007]}}<br />
<br />
== Related Links ==<br />
*[http://vadl.cc.gatech.edu/ Visual Analytics Digital Library]<br />
*[http://www.cs.umd.edu/hcil/semvast/ SEMVAST] Scientific Evaluation Methods for Visual Analytics Science and Technology - Resource on Visual Analytics Evaluation<br />
<br />
== Visual Analytics @ YouTube ==<br />
*[http://www.youtube.com/watch?v=5i3xbitEVfs March 15, 2010 — VisMaster - Mastering the Information Age] What is Visual Analytics and why is it important for Europe to fund Research in this new domain?<br />
*[http://www.youtube.com/watch?v=2CMw4i9DiaM December 16, 2008 — Jigsaw] Using visualization and visual analytics to help investigative analysis and sensemaking. <br />
*[http://www.youtube.com/watch?v=cVL2Y6nfGiM November 03, 2008 — Mikael Jern on Visual Analytics] Visual Analytics Center, OECD Explorer<br />
*[http://www.youtube.com/watch?v=5uGRGqCFryg July 10, 2007 — Why Visual Analytics? - A conversation] A series of interviews to some of most known people in Visual Analytics, the science of reasoning through visualization<br />
*[http://www.youtube.com/watch?v=m_TQZwYL7MY December 12, 2006 — Visual Analytics Show segment from "BusinessNOW" on Maryland Public Television]<br />
<br />
== Basic Literature ==<br />
=== Books ===<br />
* J.J. Thomas and K.A. Cook (Eds.), [http://nvac.pnl.gov/agenda.stm Illuminating the Path: The Research and Development Agenda for Visual Analytics]. IEEE Press, 2005.<br />
* Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis and Florian Mansmann (Eds.), [http://www.vismaster.eu/book/ Mastering the Information Age – Solving Problems with Visual Analytics], Eurographics Association, 2010.<br />
=== Papers ===<br />
* J.J. Thomas and K.A. Cook, [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1573625 "A Visual Analytics Agenda,"] ''IEEE Computer Graphics & Applications'', vol. 26, pp. 10-13, 2006.<br />
* Pak Chung Wong and J. Thomas, [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1333623 "Visual Analytics,"] ''IEEE Computer Graphics & Applications'', vol. 24, pp. 20-21, 2004.<br />
* Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melancon. 2008. [http://infovis.uni-konstanz.de/papers/2007/Dagstuhl2007VisualAnalyticsfinal.pdf "Visual Analytics: Definition, Process, and Challenges."] In Information Visualization, Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, and Chris North (Eds.). Lecture Notes In Computer Science, Vol. 4950. Springer-Verlag, Berlin, Heidelberg 154-175<br />
* J. Thomas, [http://portal.acm.org/citation.cfm?id=1292429 "Visual analytics: a grand challenge in science: turning information overload into the opportunity of the decade,"] ''Journal of Computing Sciences in Colleges'', vol. 23, pp. 5-6, 2007.<br />
<br />
Hi BobI don't think anybody (who knew of them in the first place) will have foetgtorn standards bodies like W3C or OASIS. Indeed for those of who work with XML, the W3C is of course the central source of most of the key specifications.Surely though quality is not an automatic facet of any particular body's work, but varies according to many factors: the people, the time, the politics, etc. So while W3C has given us some great technologies (XML 1.0, XSLT, MathML, and SVG to name but four) it has also given us some stinkers (e.g. XML Schema, the whole WS-* stack, and XML 1.1).I think it's interesting that often the blame for stinkiness can be traced squarely back to vendor influence. To take one tiny example: why did the W3C decide to count the (previously forbidden) NEL character as a line feed in XML 1.1, other than for reasons of compatibility with legacy IBM systems which (practically alone of their competitors) made use of this character? This was one of the disastrous moves that made such XML 1.1 instances incompatible with the entire installed base of XML 1.0 processors.XML 1.0 (which I think of as a clean, well-written spec) has attracted over 200 errata in its lifetime. At around 40 pages that's 5 errors per page. Do you think certain recent high-profile ISO/IEC standards are significantly more faulty than that?When you mention procurement, I take it you mean the procurement by nations. The major factor here is surely that nations lean towards international standards because they are international, not necessarily because of perceptions of superior quality. Being international means that they (the nations) ultimately can control the standardisation process. Vendor-driven consortiums perform a different function and are valued at a lesser worth accordingly: it's not technical, it's political.And if laws are to be re-visited and standards bodies judged, who is going to be doing the re-visiting and the judging? Ultimately it is a precept of international standardisation that the sovereign nations order their own affairs and yes sometimes this means vendors get upset. Ultimately we (the users) need the nations as they are the only entities powerful enough to bring today's huge corporations to heel. - Alex.</div>86.96.226.20https://infovis-wiki.net/w/index.php?title=Patterns:Smooth_Transitions&diff=147794Patterns:Smooth Transitions2012-11-23T07:04:08Z<p>86.96.226.20: /* References */</p>
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<div>{| align="right"<br />
| __TOC__<br />
|}<br />
== Summary ==<br />
<br />
<br />
== Category ==<br />
Interaction<br />
<br />
== Context ==<br />
The data is represented in such a way that the entire dataset cannot be<br />
viewed at one time. However, the user has a mechanism for spatial<br />
movement from one part of the dataset to another. Alternatively a large<br />
number of data items captured frequently over a period of time are being<br />
viewed using animation.<br />
<br />
== Problem ==<br />
How to maintain the users context when travelling through the data<br />
space.<br />
<br />
== Forces ==<br />
* Need to maintain context in data space.<br />
* Moving from one part of the dataset to another.<br />
* Looking for changes in data over time.<br />
<br />
== Solution ==<br />
'''Use smooth animation and motion. This is especially true for temporal data.'''<br />
<br />
Animation or motion is an effective means of representing data that has<br />
some temporal aspect. The most important consideration when using this<br />
technique is to make the transition from one state to another as smooth<br />
as possible. Animation or motion that produces a large change of state<br />
distracts the user from the information and may cause important features<br />
to be missed. Large jumps from one location in a 3D environment to<br />
another may cause the user to become disoriented a smooth transition<br />
helps them to maintain their context in the environment. If possible the<br />
user should be able to control the rate of change and direction of the<br />
animation, this may allow them to rapidly and accurately explore the<br />
data.<br />
<br />
There are arguments for not using smooth animation or motion. If the<br />
task is to monitor data that may change only gradually over long periods<br />
of time, then using large discrete time steps may help the user to identify<br />
significant changes in the data. This implies that the application of this<br />
pattern may be task dependent.<br />
<br />
== Examples ==<br />
* Brath <ref>Brath, R. (1999) Effective Information Visualization : Guidelines and Metrics for 3D<br />
Interactive Representations of Business Data. Masters of Computer Science Thesis, Graduate<br />
Department of Computer Science, University of Toronto, Canada.</ref><br />
Note it is difficult to find examples since researchers tend not to describe<br />
the rate at which transitions occur.<br />
<br />
== Related Patterns ==<br />
<br />
<br />
Oh My goodness!!! I was lonkiog for some simple patterns for me and my older daughter at our local fabric store and they had these, I should have known they were by Mrs. Liesl! Thank you, Mrs. Liesl, these patterns are so great and very clean and classic. Thank you, Thank you, Thank you!!! Luv you and your talent! To know that I just discovered you for my baby girl was great enough but this is a cherry on top ( jumping joyfully) wohoooo!!!!</div>86.96.226.20https://infovis-wiki.net/w/index.php?title=Ockham%27s_Razor_/_Occam%27s_Razor_/_Principle_of_Simplicity&diff=147792Ockham's Razor / Occam's Razor / Principle of Simplicity2012-11-23T05:46:28Z<p>86.96.226.20: SRhSbBlLxsCXdliiTA</p>
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