Transcript Polaris: A System for Query, Analysis and Visualization of Multi

Polaris: A System for Query,
Analysis and Visualization of
Multi-dimensional Relational
Databases
Presented by Darren Gates
for ICS 280
Introduction
• Polaris is a system for exploring large
multi-dimensional databases, using the
Pivot Table interface, but extending this
idea to graphical displays and allowing the
construction of complex queries.
• Polaris uses tables to organize multiple
graphs on a display, with each table
consisting of layers and panes.
Pivot Tables
• Multi-dimensional databases are often
treated as n-dimensional cubes.
• Pivot Tables allow rotation of multidimensional datasets, allowing different
dimensions to assume the rows and columns
of the table, with the remaining dimensions
being aggregated within the table.
Example: Baseball data
• By dragging and dropping the dimensions
to and from the left-hand column, top row,
upper-left corner, and central data area
(where the remaining dimensions are
aggregated), one can change the Pivot Table
view. Any of these views can be
subsequently graphed.
Polaris Design Concepts 1
• An analysis tool for a large, multidimensional database must:
– allow data-dense displays for a large number of
records and dimensions
– allow multiple display types
– have an exploratory interface; should be able to
rapidly change how data is viewed
Polaris Design Concepts 2
• Characteristics of tables that make them
effective to display multi-dimensional data:
– multivariate: multiple dimensions can be
encoded in the structure of the table
– comparative: tables generate “small-multiple”
displays of information
– familiar: users are accustomed to tabular
displays
Polaris Display 1
• Drag and drop fields from database scheme
onto shelves
• May combine multiple data sources, each
data source mapping to a separate layer
• Multiple fields may be dragged onto each
shelf
• Data may be grouped or sorted, and
aggregations may be computed
Polaris Display 2
• Selecting a single mark in a graphic
displays the values for the mark
• Can lasso a set of marks to brush records
• Marks in the graphics use retinal properties
(see subsequent slide)
Table Algebra
• A formal mechanism to specify table
configurations
• Operators:
– concatenation +
– cross x
– nest /
Graphics
• Ordinal-Ordinal: e.g. the table
– the axis variables are typically independent of
each other
• Ordinal-Quantitative: e.g. bar chart
– the quantitative variable is often dependent on
the ordinal variable
• Quantitative-Quantitative: e.g. maps
– view distribution of data as a function of one or
both variables; discover causal relationships
Retinal Properties
• Ordinal/nominal mapping vs. quantitative
mapping
• Properties: Shape, size, orientation, and
color.
• When encoding a quantitative variables,
should only vary one aspect at a time
Querying
• Three steps:
– Select the records
– Partition the records into panes
– Transform the records within the panes
• To create database queries, it is necessary to
generate an SQL query per table pane (i.e.
must iterate over entire table, executing
SQL for each pane).
Discussion
• Allows overlap between the relations that
are divided into each pane of the Polaris
display, unlike the basic Pivot Table model.
• Allows more versatile computation of
aggregates (e.g., medians and averages, in
addition to sums).
• Intuitive drag-and-drop interface, like that
seen in Pivot Tables
Possible Improvements
• Generate database tables from a selected set
of marks
• Integrate a table lens, instead of having to
click a mark to view its details