i247: Information Visualization and Presentation Marti Hearst

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i247 Information Visualization and
PresentationMarti Hearst
Multidimensional Graphing    
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Today

• Discuss found visualizations
• Discuss Polaris paper
• Introducing the EDA assignment
• In-class practice with EDA

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The Polaris Framework

• Goal support interactive exploration of
  multi-dimensional relational databases
• Nice overview of how to combine different
  standard visualizations into interactive systems.
• Data types
• Only ordinal and quantitative!
• Treats intervals as quantitative
• Assigns an order to all nominal fields
  (alphabetical)
• Ordinal dimensions independent variable
• Quantitative measures dependent variables
• Supports design principles
• Small simultaneous multiples for comparison
• Data-dense display
• Allows proper use of retinal properties
  (Bertin)
• Clevelands idea regarding mapping independent
  and dependent variables

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Polaris Paper

• Two nice examples of exploratory data analysis
• Analysts form hypotheses
• Create views to confirm or refute
• If refuted, follow leads to find new hypotheses
• Look for different things
• Trends
• Anomalies

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Specifying Table Configurations

• Operands are the database fields
• each operand interpreted as a set
• quantitative and ordinal fields interpreted
  differently
• Three operators
• concatenation (), cross product (X), nest (/)

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Table Algebra Operands

• Ordinal fields interpret domain as a set that
  partitions table into rows and columns
• Quarter (Qtr1),(Qtr2),(Qtr3),(Qtr4) ?

• Quantitative fields treat domain as single
  element set and encode spatially as axes
• Profit (Profit-410,650) ?

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Concatenation () operator

• Ordered union of set interpretations

Profit Sales (Profit-310,620),(Sales0,1000
)
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Cross (x) operator
Cross-product of set interpretations
Quarter x ProductType
(Qtr1,Coffee), (Qtr1, Tea), (Qtr2, Coffee),
(Qtr2, Tea), (Qtr3, Coffee), (Qtr3, Tea), (Qtr4,
Coffee), (Qtr4,Tea)
ProductType x Profit
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Nest (/) operator

• Quarter x Month
• would create entry twelve entries for each
  quarter. i.e., (Qtr1, December)
• Quarter / Month
• would only create three entries per quarter
• based on tuples in database not semantics
• can be expensive to compute

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Combining the Data Types

• Ordinal - Ordinal

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Combining the Data Types

• Quant - Quant

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Combining the Data Types

• Ordinal - Quantitative

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Data Transformations

• Deriving Additional Fields
• Aggregation
• Sums
• Averages / Variances / Std. Deviations
• Min/Max
• LOTS of other functions (arctan )
• Counting of Ordinal Dimensions
• CNT(field)
• Discrete Partitioning
• Binning (fixed-sized groups, for creating
  histograms)
• Partitioning (ad hoc sizes, good for encoding
  data)
• Ad hoc Grouping
• The ordinal version of partitioning
• Choose meaningful groups

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Data Transformations (cont)

• Sorting and Filtering
• Filtering allows for choosing which values to
  focus on
• Sorting helps find trends and anomolies
• Brushing and Tooltips
• Brushing allows for selecting and highlighting
  interesting points can then create a new dataset
  with them.
• Tableau/Polaris is missing linking, which usually
  goes with brushing (its high on the to-do list).
• Linking allows you to see which items that are
  brushed in one view are highlighted in another
• Undo and Redo
• A key interface capability which is
  well-supported here.

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Querying the Database
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Assignment

• Exploratory Data Analysis
• Choose a dataset
• Formulate hypotheses
• Test these hypotheses and also explore the
  dataset using visualization tool(s)
• Tableau and optionally others of your choosing
• Well supply some datasets or you can use your
  own
• You can work in pairs but not in larger groups
• Due Monday February 25 (2.5 weeks)

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EDA Practice

• Data from UCB Career Center
• What jobs do graduates get, grouped by major
  area?