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, 1998 Authors Chris Olston, Michael Stonebraker, Alexander Aiken, Joseph M. Hellerstein

VI Q ING Chris Olston, UC Berkeley Outline Introduction –Related Work –Background Visual query results Specifying visual queries How VIQING generalizes other work Status and future work

VI Q ING 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 ?

VI Q ING 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 ?

VI Q ING 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 Database One row Table Canvas DataSplash

VI Q ING 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: Democrat Blue: Republican Each state is one database row

VI Q ING 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 ‘88Clinton ‘92 Bush ‘92Dukakis ‘88 A Portal This portal contains a canvas of presidential candidates ordered by year (X axis), with the winner on top (Y axis)

VI Q ING Chris Olston, UC Berkeley Outline Introduction –Related Work –Background Visual query results Specifying visual queries How VIQING generalizes other work Status and future work

VI Q ING 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 ?

VI Q ING 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 ?

VI Q ING Chris Olston, UC Berkeley Outline Introduction –Related Work –Background Visual query results Specifying visual queries How VIQING generalizes other work Status and future work

VI Q ING 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

VI Q ING Chris Olston, UC Berkeley Performing a Visual Join ? Drag and Drop VI Q ING Chris Olston, UC Berkeley Join 1960’s presidential candidates with political parties

VI Q ING 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

VI Q ING 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

VI Q ING 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

VI Q ING 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)

VI Q ING 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

VI Q ING 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

VI Q ING Chris Olston, UC Berkeley Outline Introduction –Related Work –Background Visual query results Specifying visual queries How VIQING generalizes other work Status and future work

VI Q ING 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

VI Q ING 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 = 5Z = 10

VI Q ING 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

VI Q ING 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 ?

VI Q ING Chris Olston, UC Berkeley For more info... Paper in Proc. Visual Languages 1998 –Or my web page: me: