vi q ing v isual i nteractive q uerying chris olston uc berkeley 14th ieee symposium on visual...
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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
AuthorsChris Olston, Michael Stonebraker, Alexander Aiken, Joseph M. Hellerstein
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VIQING Chris Olston, UC Berkeley
Outline
• Introduction– Related Work– Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work
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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
• 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
?
Related Work
?
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VIQING Chris Olston, UC Berkeley
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
1.52322.82383.9221
Database
One rowTable Canvas
DataSplash
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VIQING Chris Olston, UC Berkeley
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: DemocratBlue: Republican
Each state is one database row
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VIQING Chris Olston, UC Berkeley
Portals: Nested Visualizations
• Portals are sub-windows in one canvas that show another canvas
• Portals can be independently panned and zoomed
Bush ‘88 Clinton ‘92
Bush ‘92Dukakis ‘88A Portal
This portal contains a canvas of presidential candidates ordered by year (X axis), with the winner on top (Y axis)
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VIQING Chris Olston, UC Berkeley
Outline
• Introduction– Related Work– Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work
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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
Visual Join
• A visual join ( ) combines information from two or more database tables via portals
Presidential Candidates States
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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VIQING Chris Olston, UC Berkeley
Outline
• Introduction– Related Work– Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work
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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
Performing a Visual Join?• Drag . . . . . . . . and Drop
VIQING Chris Olston, UC Berkeley
Join 1960’s presidential candidates with political parties
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VIQING Chris Olston, UC Berkeley
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
?
Parties Candidates States
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VIQING Chris Olston, UC Berkeley
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
?
Parties Candidates States
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VIQING Chris Olston, UC Berkeley
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
?
Parties States Candidates
Georgia voted with the other Democrat states in ‘60, but against them in ‘64
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VIQING Chris Olston, UC Berkeley
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
– Don’t need to know SQL syntax -- just drag and drop (direct-manipulation)
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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
Removing Intermediate Tables
• Often, 2 tables join via an intermediate table– eg, Candidates Vote records States
• However, we don’t want to see the intermediate table– we want Candidates States
• To do this, visually remove intermediate– Drag intermediate portal away from the canvas
92-TX-R
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VIQING Chris Olston, UC Berkeley
Outline
• Introduction– Related Work– Background
• Visual query results
• Specifying visual queries
• How VIQING generalizes other work
• Status and future work
![Page 21: VI Q ING V isual I nteractive Q ueryING Chris Olston UC Berkeley 14th IEEE Symposium on Visual Languages Halifax, Nova Scotia, Canada September 1st - 4th,](https://reader036.vdocuments.net/reader036/viewer/2022062714/56649d535503460f94a2fa2f/html5/thumbnails/21.jpg)
VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
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
Z = 5 Z = 10
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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
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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VIQING Chris Olston, UC Berkeley
For more info...
• Paper in Proc. Visual Languages 1998– Or my web page:
http://datasplash.cs.berkeley.edu/cao
• Email me: [email protected]