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Mia M

DOE Visualization and Analytics Workshop June 7-8 2007 - 1 views

www.sci.utah.edu/vaw2007

visualdata hz10

shared by Mia M on 24 Mar 10 - Cached
  • Mia M on 24 Mar 10
     
    The final report concerning visualization and analytics after the DOE/ASCR Workshop Salt Lake City, Utah June 7-8, 2007
Vicki Davis

Horizon Report 2010 Resources about Visual Data Analysis - 0 views

delicious.com/hz10+analytics

education learning hz10 analytics

shared by Vicki Davis on 08 Mar 10 - Cached
  • Vicki Davis on 08 Mar 10
     
    This listing of resources covers Visual Analysis. Visual analysis "blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in visuals."
Sydnee S

Challenges in Visual Data Analysis - 0 views

portal.acm.org/citation.cfm

shared by Sydnee S on 30 Mar 10 - Cached
  • data is produced at unprecedented rates.
    • Sydnee S
      Sydnee S on 30 Mar 10
       
      Data is constantly produced around the worlrd.
  • ability to analyze these data volumes increases at much lower pace.
    • Sydnee S
      Sydnee S on 30 Mar 10
       
      Since there is so much data to analyze, it is beginning to take much longer to analyze things.
  • leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information "concealed" in the data.
  • ...2 more annotations...
  • field of visual analytics focuses on handling massive, heterogenous, and dynamic volumes of information through integration of human judgement by means of visual representations and interaction techniques
  • combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research.
    • Sydnee S
      Sydnee S on 30 Mar 10
       
      The more this visual data analysis is worked on, the faster it will get.
Vicki Davis

How Privacy Vanishes Online, a Bit at a Time - NYTimes.com - 0 views

www.nytimes.com/...17privacy.html

privacy analytics education

shared by Vicki Davis on 17 Mar 10 - Cached
  • Vicki Davis on 17 Mar 10
     
    Data that is innocuous or is it? Excellent article from the New York Times on Privacy. "In a class project at the Massachusetts Institute of Technology that received some attention last year, Carter Jernigan and Behram Mistree analyzed more than 4,000 Facebook profiles of students, including links to friends who said they were gay. The pair was able to predict, with 78 percent accuracy, whether a profile belonged to a gay male. So far, this type of powerful data mining, which relies on sophisticated statistical correlations, is mostly in the realm of university researchers, not identity thieves and marketers. But the F.T.C. is worried that rules to protect privacy have not kept up with technology. The agency is convening on Wednesday the third of three workshops on the issue. "
  • Vicki Davis on 17 Mar 10
     
    Privacy concerns are tremendous with any kind of data and as you predict what is happening with the future, you should look at this.
Tanner B

Visual Data Analysis, Scientific Computing - 0 views

www.scientificcomputing.com/visual-data-analysis.aspx

visual data analysis

shared by Tanner B on 30 Mar 10 - No Cached
  • Visual Data Analysis (VDA) tool packages are powerful software tools that allow the development of complex solutions to visualize and analyze data.
  • The imaging features of a VDA package are often the most tangible aspect of the tool, but the overall effectiveness of a package is really based on its data manipulation and analysis capabilities.
  • A robust and flexible VDA tool should contain a broad range of mathematical, statistical and data manipulation capabilities, which are seamlessly tied to the visualization components through a high-level array-based scripting language.
  • ...5 more annotations...
  • Support for popular formats (XML, CDF, HDF, binary, ASCII, TIF) should be included in the base product, and services for customized data handlers should be available through the provider.
  • There are several factors that determine or impact performance, including use of pre-built data manipulation functions, limitations on data set size, parallelization, supported data types, and performance of the package's algorithms.
  • In general, the best performers will be the packages that support a broad range of data types, thus minimizing impact on memory requirements; support parallelization technology such as OpenMP; and have kernels optimized for visual data analysis.
  • Various techniques, solutions, and products are available on the market to visually analyze data, but few offer a full spectrum of visualization capabilities coupled with sufficient complementary analytical functionality.
  • In conclusion, a careful evaluation of key VDA tool characteristics, with the end solution in mind, should factor into the selection process to include the combined functionality, reliability, scalability, performance and portability of a VDA tool.
Tanner B

2010 Horizon Report » Four to Five Years: Visual Data Analysis - 0 views

wp.nmc.org/...visual-data-analysis

visual data analysis

shared by Tanner B on 30 Mar 10 - Cached
  • Visual data analysis blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in even the most complex visual presentations.
  • Data collection and compilation is no longer the tedious, manual process it once was, and tools to analyze, interpret, and display data are increasingly sophisticated, and their use routine in many disciplines.
  • In advanced research settings, scientists and others studying massively complex systems generate mountains of data, and have developed a wide variety of new tools and techniques to allow those data to be interpreted holistically, and to expose meaningful patterns and structure, trends and exceptions, and more.
  • ...8 more annotations...
  • Researchers that work with data sets from experiments or simulations, such as computational fluid dynamics, astrophysics, climate study, or medicine draw on techniques from the study of visualization, data mining, and statistics to create useful ways to investigate and understand what they have found.
  • The blending of these disciplines has given rise to the new field of visual data analysis, which is not only characterized by its focus on making use of the pattern matching skills that seem to be hard-wired into the human brain, but also in the way in which it facilitates the work of teams working in concert to tease out meaning from complex sets of information.
  • it possible for almost anyone with an analytical bent to easily interpret all sorts of data
  • Many are free or very inexpensive, bringing the ability to engage in rich visual interpretation to virtually anyone.
  • Online services such as Many Eyes, Wordle, Flowing Data, and Gapminder accept uploaded data and allow the user to configure the output to varying degrees.
  • By manipulating variables, or simply seeing them change over time (as Gapminder has done so famously) if patterns exist (or if they don’t), that fact is easily discoverable.
  • The promise for teaching and learning is further afield, but because of the intuitive ways in which it can expose complex relationships to even the uninitiated, there is tremendous opportunity to integrate visual data analysis into undergraduate research, even in survey courses.
  • Visual data analysis may help expand our understanding of learning itself. Learning is one of the most complex of social processes, with a myriad of variables interacting in highly complex ways, making it an ideal focus for the search for patterns.
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