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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
CS 796/896 - Spring 2011 - Weigle 3
! 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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
Goal of Visual Analytics
CS 796/896 - Spring 2011 - Weigle 4
! 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?
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
The Need for Visual Analytics
CS 796/896 - Spring 2011 - Weigle 5
! 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)
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 7
Tool: CGV [113]
Visual Analytics in Action Distributed Network Attack on SSH
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 8
Tool: NFlowVis [76]
Visual Analytics in Action Cooling Jacket Simulation
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 9
Tool: SimVis
Visual Analytics in Action Multivariate Datasets in Demography
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 10
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.
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 12 From Chabot, "Demystifying Visual
Analytics", 2009
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 13 From Chabot, "Demystifying Visual
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 14 From Chabot, "Demystifying Visual
Analytics", 2009
The Visual Analytics Process ! Combines automatic and visual analysis
methods
! Tight coupling through human interaction
! Goal: gain knowledge from data
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 15
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
CS 796/896 - Spring 2011 - Weigle 17 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 18
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)
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 19
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 20
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
CS 796/896 - Spring 2011 - Weigle 21 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 22
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
CS 796/896 - Spring 2011 - Weigle 23 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 24
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
CS 796/896 - Spring 2011 - Weigle 25 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 26
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
CS 796/896 - Spring 2011 - Weigle 27 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 28
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
CS 796/896 - Spring 2011 - Weigle 29 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
Evaluation Papers (Apr 12-19) ! Evaluating Information Visualization
! An Insight-Based Methodology for Evaluating Bioinformatics Visualizations
! An Insight-Based Longitudinal Study of Visual Analytics
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 30
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 - Spring 2011 - Weigle 31 From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al.
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 32
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
From Mastering the Information Age: Solving Problems with Visual Analytics, Keim et al. CS 796/896 - Spring 2011 - Weigle 33