VIQING Visual Interactive QueryING

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VIQING Visual Interactive QueryING

• Chris Olston
• UC Berkeley

14th IEEE Symposium on Visual Languages
Halifax, Nova Scotia, Canada September 1st -
4th, 1998
Authors Chris Olston, Michael Stonebraker,
Alexander Aiken, Joseph M. Hellerstein
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Outline

• Introduction
• Related Work
• Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work

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Introduction

• Databases are hard to use
• Difficult to understand data in textual form
• SQL query language hard to learn
• Visual Programming Can Help!
• Database visualization systems (like DataSplash)
  display data in graphical form
• VIQING provides a simple interface for expressing
  queries over visualizations

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Related Work

• Other interfaces offer visual programming
• Visualization
• QBE, Cupid, Tioga-1, AVS, Khoros, MS-Access,
  DEVise
• Querying
• 4GLs, Tioga-1, AVS, Khoros, Access, DEVise, Magic
  Lenses
• But only VIQING/DataSplash offers a unified
  visual programming model for visualization and
  visual querying

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Background

• DataSplash is a data visualization tool that
  displays database data in graphical form
• Each row in a database table gets translated into
  one graphical object on a canvas

DataSplash
Database
1.5232 2.8238 3.9221
Table
Canvas
One row
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Example DataSplash Visualization

• This visualization shows which political party
  each state has favored since 1952
• A DataSplash canvas can be infinitely panned and
  zoomed

Red Democrat Blue Republican
Each state is one database row
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Portals Nested Visualizations

• Portals are sub-windows in one canvas that show
  another canvas
• Portals can be independently panned and zoomed

This portal contains a canvas of presidential
candidates ordered by year (X axis), with the
winner on top (Y axis)
A Portal
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Outline

• Introduction
• Related Work
• Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work

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Visual Selection

• A visual selection displays only rows that pass a
  selection filter
• Which states voted Democratic in 1992?

Note that all red (traditionally Democratic)
states voted Democratic in 1992
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Visual Join

• A visual join ( ) combines information from two
  or more database tables via portals

Each presidential candidate has a portal
containing the states that voted for him
One join portal for every row in the candidates
table
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Outline

• Introduction
• Related Work
• Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work

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User Interface Performing a Visual Selection

• Select graphical rows by rubber-banding
• The result
• The canvas inside the portal has only 6 rows
• Selection portals can be used for visual joins
  ...

A portal that contains only the selected rows
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Performing a Visual Join

• Drag . . . . . . . . and Drop

Join 1960s presidential candidates with
political parties
VIQING
Chris Olston, UC Berkeley
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The Result A Three-Level Visual Join

• Now candidates are joined with political parties
• We know which candidates belong to which parties
• Can see trends for each party over time

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Visual Reordering

• Visual queries have an ordering
• Visual reordering can be performed after the join
• To reorder drop a portal onto a row of its child
  canvas

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Result of Visual Reordering

• Now, parties join with states, which join with
  candidates
• We can see the voting history of each state, by
  traditional party

Georgia voted with the other Democrat states in
60, but against them in 64
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Benefits of VIQING Queries

• Easier to use than SQL
• Can incrementally build and refine queries
• Query manipulations on custom graphical
  representation of data, which is easier to
  understand than text
• Dont need to know SQL syntax -- just drag and
  drop (direct-manipulation)

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Join Predicates

• We have not discussed how VIQING knows what join
  predicates to use
• In most cases, join predicates are equality
• eg, candidate.party_name party.party_name
• These can be inferred from foreign key
  relationships defined at schema creation time
• Alternatively, could specify more general join
  predicates with a tool like MS Access

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Removing Intermediate Tables

• Often, 2 tables join via an intermediate
  table
• eg, Candidates Vote records States
• However, we dont want to see the
  intermediate table
• we want Candidates States
• To do this, visually remove intermediate
• Drag intermediate portal away from the canvas

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Outline

• Introduction
• Related Work
• Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work

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How VIQING Generalizes Other Work

• VIQING generalizes nested report writers
• Each level of nesting is a set of join portals
• Drill-down performed by entering a join portal
• VIQING generalizes master/detail forms
• Master-detail relationship is a join
• Data entry support could be added to DataSplash

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Generalizing Small Multiple Graphs

• VIQING can create small multiple graphs
• Several views of a graph, indexed by a variable
• This is a visual join between a canvas which
  contains several values for the index variable
  and the graph canvas

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Status and Future Work

• Implemented as an extension to DataSplash
• Future work
• Support for more SQL query expressibility
• aggregates, subqueries, etc.
• An automatic way to expose meta-data
• Which portals correspond to which tables?
• Improved support for large data sets
• This is a DataSplash issue, orthogonal to VIQING

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Summary

• VIQING combines querying with visualization by
  using portals
• Construct basic SQL queries by direct
  manipulation of pictorial data
• Visual select, join, reorder, remove intermediate
• Create nested reports, master/detail forms
• Generate small multiple graphs

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For more info...

• Paper in Proc. Visual Languages 1998
• Or my web page http//datasplash.cs.berkeley.edu
  /cao
• Email me cao_at_cs.berkeley.edu