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

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### PolarisQuery, 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,…
- Scientific projects:
- Human Genome Project
- Sloan Digital Sky Survey
- Need tools to extract meaning from these databases

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

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

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

Principle of Importance Ordering: Encode the most important

information in the most effective way [Cleveland & McGill]

The Pivot Table Interface

- Common interface to statistical packages/Excel
- Cross-tabulations
- Simple interface based on drag-and-drop

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

- 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

- 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

- 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

- Notes
- Aggregation operators applied after sort
- Only one layer is shown; additional z-sort

Sort

Relational Table

Tuples in Panes

Marks in Panes

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

- Each dimension in the cube is structured as a tree
- Each level in tree corresponds to level of detail

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

- 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)

Generalization: Techniques

- Selection
- Simplification
- Exaggeration
- Regularization
- Displacement
- Aggregation

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

- 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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