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VIQING Visual Interactive QueryING

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This visualization shows which political party each state has favored since 1952 ... Now, parties join with states, which join with candidates ... – PowerPoint PPT presentation

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Title: VIQING Visual Interactive QueryING


1
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
2
Outline
  • Introduction
  • Related Work
  • Background
  • Visual query results
  • Specifying visual queries
  • How VIQING generalizes other work
  • Status and future work

3
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

4
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

5
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
6
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
7
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
8
Outline
  • Introduction
  • Related Work
  • Background
  • Visual query results
  • Specifying visual queries
  • How VIQING generalizes other work
  • Status and future work

9
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
10
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
11
Outline
  • Introduction
  • Related Work
  • Background
  • Visual query results
  • Specifying visual queries
  • How VIQING generalizes other work
  • Status and future work

12
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
13
Performing a Visual Join
  • Drag . . . . . . . . and Drop

Join 1960s presidential candidates with
political parties
VIQING
Chris Olston, UC Berkeley
14
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

15
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

16
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
17
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)

18
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

19
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

20
Outline
  • Introduction
  • Related Work
  • Background
  • Visual query results
  • Specifying visual queries
  • How VIQING generalizes other work
  • Status and future work

21
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

22
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

23
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

24
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

25
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
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