Polaris query analysis and visualization of large hierarchical relational databases
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Polaris Query, Analysis, and Visualization of Large Hierarchical Relational Databases. Pat Hanrahan With Chris Stolte and Diane Tang Computer Science Department Stanford University. Motivation. Large databases have become very common Corporate data warehouses Amazon, Walmart,…

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Polaris Query, Analysis, and Visualization of Large Hierarchical Relational Databases

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Polaris query analysis and visualization of large hierarchical relational databases

PolarisQuery, Analysis, and Visualization of Large Hierarchical Relational Databases

Pat Hanrahan

With Chris Stolte and Diane Tang

Computer Science Department

Stanford University


Motivation

Motivation

  • Large databases have become very common

    • Corporate data warehouses

      • Amazon, Walmart,…

    • Scientific projects:

      • Human Genome Project

      • Sloan Digital Sky Survey

  • Need tools to extract meaning from these databases


Related work

Related Work

  • Formalisms for graphics

    • Bertin’s “Semiology of Graphics”

    • Mackinlay’s APT

    • Roth et al.’s Sage and SageBrush

    • Wilkinson’s “Grammar of Graphics”

  • Visual exploration of databases

    • DeVise

    • DataSplash/Tioga-2

  • Visualization and data mining

    • SGI’s MineSet

    • IBM’s Diamond


Formalism

Formalism


Polaris formalism

Polaris Formalism

  • UI interpreted as visual specification that defines:

    • Table configuration

    • Type of graphic in each pane

    • Encoding of data as visual properties of marks

    • Data transformations and queries


Schema

Schema

Market

State

Year

Quarter

Month

Product Type

Product

Profit

Sales

Payroll

Marketing

Inventory

Margin

COGS

...

Ordinal fields

(categorical)

Coffee chain data[Visual Insights]

Quantitative fields

(measures)


Polaris visual encodings

Polaris Visual Encodings

Principle of Importance Ordering: Encode the most important

information in the most effective way [Cleveland & McGill]


The pivot table interface

The Pivot Table Interface

  • Common interface to statistical packages/Excel

    • Cross-tabulations

  • Simple interface based on drag-and-drop


Data cubes

Data Cubes

  • Structure relation as n-dimensional cube

Each cell aggregatesall measures for those dimensions

Each cube axis

corresponds to a dimension

in the relation


Table algebra operands

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)} 


Concatenation operator

Concatenation (+) Operator

  • Ordered union of two sets

  • Quarter + ProductType

  • = {(Qtr1),(Qtr2),(Qtr3),(Qtr4)}+{(Coffee),(Espresso)}

  • = {(Qtr1),(Qtr2),(Qtr3),(Qtr4),(Coffee),(Espresso)}

  • Profit + Sales

  • = {(Profit),(Sales)}


Cross operator

Cross () Operator

  • Direct-product of two sets

  • Quarter  ProductType =

  • {(Qtr1,Coffee), (Qtr1, Tea), (Qtr2, Coffee), (Qtr2, Tea),

  • (Qtr3, Coffee), (Qtr3, Tea), (Qtr4, Coffee), (Qtr4,Tea)}

  • ProductType  Profit =


Sql dataflow

SQL Dataflow

  • Notes

    • Aggregation operators applied after sort

    • Only one layer is shown; additional z-sort

Sort

Relational Table

Tuples in Panes

Marks in Panes


Multiscale visualization

Multiscale Visualization


Hierarchical structure

Hierarchical Structure

  • Challenge: these databases are very large

    • Queries/Vis should not require all the records

  • Augment database with hierarchical structure

    • Provide meaningful levels of abstraction

    • Derived from domain or clustering

    • Provides metadata (missing data for context)


Hierarchies and data cubes

Hierarchies and Data Cubes

  • Each dimension in the cube is structured as a tree

  • Each level in tree corresponds to level of detail


Schema star schema

Schema: Star Schema

Existence Table

Fact table

Location

Market

State

State

Month

Product

Profit

Sales

Payroll

Marketing

Inventory

Margin

...

Time

Year

Quarter

Month

Products

Product Type

Product Name

Measures

  • Generalizations

  • Snowflake schemas

  • Lattices (DAGs)


Categorical hierarchies

Categorical Hierarchies

  • Quarter  Month

    • Direct product of two sets

    • Would create twelve entries for each quarter, i.e. (Qtr1, December)

  • Quarter / Month

    • Based on tuples in database not semantics

    • Would only create three entries per quarter

    • Can be expensive to compute

  • Quarter . Month

    • Based on tuples in existence tables (not db)


Cartographic generalization

Cartographic Generalization

Canterbury and East Kent

1:50,000

1:625,000


Generalization techniques

Generalization: Techniques

  • Selection

  • Simplification

  • Exaggeration

  • Regularization

  • Displacement

  • Aggregation


Summary

Summary

  • Polaris

    • Spreadsheet or table-based displays

    • Simple drag-and-drop interface

    • Built on a formalism that allows algebraic manipulation of visual mapping of tuples to marks

    • Multiscale visualizations using data and visual abstraction

    • Connects to SQL/MDX servers

  • Seehttp://www.graphics.stanford.edu/projects/polaris


Future work

Future Work

  • Articulate full-set of multiscale design patterns

  • Transition between levels of detail

  • Develop system infrastructure for browsing VLDB

  • Support layers/lenses/linking with tuple flow

  • Device independence through graphical encodings

  • Extend formalism to 3D

  • Couple scientific and information visualization


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