What is Visual Analytics? - ODU€¦ · Evaluating Information Visualization ! An Insight-Based Methodology for Evaluating Bioinformatics Visualizations ! An Insight-Based Longitudinal

What is Visual Analytics?

Dr. Michele C. Weigle

CS 796/896 Visual Analytics Seminar Spring 2011

http://www.cs.odu.edu/~mweigle/CS796-S11/

What is Visual Analytics?

CS 796/896 - Spring 2011 - Weigle 2

! New multidisciplinary field

! Combines various research areas !  visualization !  human-computer interaction !  data analysis !  data management !  geo-spatial and temporal data processing !  spatial decision support !  statistics

From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.

http://www.youtube.com/watch?v=K9PvskathGI

Information Overload Problem


! The danger of getting lost in data, which may be !  irrelevant to the current task at hand !  processed in an inappropriate way, or !  presented in an inappropriate way

! There is a need for methods and models to turn the data into reliable and comprehensible knowledge


Goal of Visual Analytics


! Who or what defines the "relevance of information" for a given task?

! How can inappropriate procedures in a complex decision making process be identified?

! How can the resulting information be presented in a decision-oriented or task-oriented way?


The Need for Visual Analytics


!  Essential in application areas where large information spaces have to be processed and analyzed !  astrophysics !  monitoring climate and weather !  emergency management !  network security !  terrorism informatics !  biology and medicine !  business intelligence !  web science – digital libraries (WS-DL)


Visualization in WS-DL Papers (Jan 25-Feb 1) !  Zoetrope: Interacting with the Ephemeral Web

!  "Catch Me If You Can": Visual Analysis of Coherence Defects in Web Archiving

!  VisGets: Coordinated Visualizations for Web-based Information Exploration and Discovery

!  Describing Story Evolution from Dynamic Information Streams

From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 6

Visual Analytics in Action Simulation of Climate Models


Tool: CGV [113]

Visual Analytics in Action Distributed Network Attack on SSH


Tool: NFlowVis [76]

Visual Analytics in Action Cooling Jacket Simulation


Tool: SimVis

Visual Analytics in Action Multivariate Datasets in Demography


But, It's Not Just For Massive Data

From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 11 From Chabot, "Demystifying Visual

Analytics", 2009

Multi-dimensional analysis of 738-row spreadsheet

Problem is not number of observations, but number of dimensions

Note: next few slides present viewpoint of the CEO of Tableau Software

And, It Doesn't Have To Be Complex ! Many people adopt visual analytics to help

them see and understand relatively simple problems.

! Standard visual paradigms (lines, bars, maps) are often better at representing data than fancy new visualizations.


Analytics", 2009


Analytics", 2009

What's The Goal? !  People spend lots of time with data

!  exploring it !  cleaning it !  gaining confidence in it !  summarizing it !  confirming facts !  presenting findings

!  Make this fast! !  let people apply computing operations on data by

interacting directly with visual representations


Analytics", 2009

The Visual Analytics Process ! Combines automatic and visual analysis

methods

! Tight coupling through human interaction

! Goal: gain knowledge from data


The Visual Analytics Process

CS 796/896 - Spring 2011 - Weigle 16 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.

Building Blocks of Visual Analytics Research


Visualization ! Scientific visualization !  3D data !  real-world, physical models

!  Information visualization !  abstract data !  no explicit spatial references !  data values cannot be naturally

mapped to 2D or 3D display space !  interaction is important


Data Management !  Integration of heterogeneous data

!  Data cleansing !  dealing with missing, inaccurate values, inconsistent labels !  Google Refine - http://code.google.com/p/google-refine/

!  Makes use of intelligent data analysis techniques and visualization

!  Challenges: !  streaming data sources !  automatic extraction of information from large document

collections (i.e., the web)


Data Management Papers (Feb 8-15) !  Duplicate Record Detection: A Survey

!  A Taxonomy of Clutter Reduction for Information Visualisation

!  Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering

!  Give Chance a Chance: Modeling Density to Enhance Scatter Plot Quality Through Random Data Sampling


Data Mining !  Computational methods to automatically extract

valuable information from raw data

!  Approaches: !  supervised learning – uses training samples

!  examples: decision trees, neural networks !  unsupervised learning

!  example: cluster analysis !  association rule mining !  dimensionality reduction

!  Visual data mining !  interactive visualization, to help with parameter specification


Data Mining Papers (Feb 22-Mar 1) ! From Visual Data Exploration To Visual Data

Mining: A Survey

!  Investigating and Reflecting on the Integration of Automatic Data Analysis and Visualization in Knowledge Discovery

! Extreme Visualization: Squeezing a Billion Records into a Million Pixels


Spatio-Temporal Data Analysis ! Spatio-temporal !  involves both space and time

! Complexities of scale and uncertainty !  scale maps to look for patterns and trends !  data is often incomplete, collected at different times


Spatio-Temporal Data Analysis Papers (Mar 1-22) !  Interactive Visual Clustering of Large Collections of

Trajectories

!  Data Mining on Temporal Data: A Visual Approach and its Clinical Application to Hemodialysis

!  Interactive Pattern Search in Time Series

!  TimeMatrix: Visualizing Temporal Social Networks Using Interactive Matrix-Based Visualizations


Infrastructure / Tools ! Linking together all the processes, functions,

and services required by visual analytics applications

! Require high interactivity

! Most visual analytics applications are custom-built, stand-alone


Infrastructure / Tools Papers (Mar 22-29) ! Polaris: A System for Query, Analysis, and

Visualization of Multidimensional Relational Databases

!  Jigsaw: Supporting Investigative Analysis Through Interactive Visualization

! Collaborative Brushing and Linking for Co-Located Visual Analytics of Document Collections


Perception and Cognition !  Human side of visual analytics

!  Visual perception !  means by which people interpret their surroundings (or

images on a computer screen)

!  Cognition !  ability to understand the visual information and make

inferences

!  Knowledge of how we "think visually" is important in the design of user interfaces


Perception and Cognition Papers (Apr 5-12) ! Characterizing Users' Visual Analytic Activity

for Insight Provenance

! Distributed Cognition as a Theoretical Framework for Information Visualization

! Toward a Deeper Understanding of the Role of Interaction in Information Visualization


Evaluation ! What makes a good visual analytics system? ! How can we compare systems?

! Evaluation is very difficult given !  the explorative nature of visual analytics !  the wide range of user experience !  the diversity of data sources !  the actual tasks themselves


Evaluation Papers (Apr 12-19) ! Evaluating Information Visualization

! An Insight-Based Methodology for Evaluating Bioinformatics Visualizations

! An Insight-Based Longitudinal Study of Visual Analytics


The Future

All grand challenge problems of the 21st century, such as climate change, energy, financial, health, security, require the exploration and analysis of very large and complex data sets which can neither be done by the computer nor the human alone.


CS 796/896 Going Forward !  Today

!  research methods webpage !  more visualization videos

!  Next Tuesday – Jan 18 !  Martin Klein – Intro to R Tutorial !  Demos of some tools, interesting websites !  May be meeting in 3rd floor OR Lab (sign-up for the

mailing list and watch your email for details)

!  Jan 25 – First Student Presentations


More Video ! Hans Rosling's TED 2006 talk !  http://www.ted.com/index.php/talks/

hans_rosling_shows_the_best_stats_you_ve_ever_seen.html

! David McCandless's TED 2010 talk !  http://www.ted.com/talks/

david_mccandless_the_beauty_of_data_visualization.html


Documents

What is Visual Analytics? - ODU€¦ · Evaluating Information Visualization ! An Insight-Based Methodology for Evaluating Bioinformatics Visualizations ! An Insight-Based Longitudinal