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VIQING Visual Interactive QueryING Chris Olston UC Berkeley Authors Chris Olston, Michael Stonebraker, Alexander Aiken, Joseph M. Hellerstein 14th IEEE Symposium on Visual Languages Halifax, Nova Scotia, Canada September 1st - 4th, 1998 Outline • Introduction – Related Work – Background • • • • Visual query results Specifying visual queries How VIQING generalizes other work Status and future work 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 VIQING Chris Olston, UC Berkeley ? 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 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 DataSplash Database 1.5232 2.8238 3.9221 Table VIQING Chris Olston, UC Berkeley One row Canvas Example DataSplash Visualization • This visualization shows which political party each state has favored since 1952 Each state is one database row Red: Democrat Blue: Republican • A DataSplash canvas can be infinitely panned and zoomed VIQING Chris Olston, UC Berkeley Portals: Nested Visualizations • Portals are sub-windows in one canvas that show another canvas Bush ‘88 Clinton ‘92 Dukakis ‘88 Bush ‘92 A Portal This portal contains a canvas of presidential candidates ordered by year (X axis), with the winner on top (Y axis) • Portals can be independently panned and zoomed 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 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 VIQING Chris Olston, UC Berkeley ? 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 Presidential Candidates VIQING Chris Olston, UC Berkeley States Outline • Introduction – Related Work – Background • • • • Visual query results Specifying visual queries How VIQING generalizes other work Status and future work VIQING Chris Olston, UC Berkeley ? User Interface: Performing a Visual Selection • Select graphical rows by rubber-banding • The result: A portal that contains only the selected rows – The canvas inside the portal has only 6 rows – Selection portals can be used for visual joins ... VIQING Chris Olston, UC Berkeley ? Performing a Visual Join • Drag . . . . . . . . and Drop Join 1960’s presidential candidates with political parties 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 VIQING Chris Olston, UC Berkeley Parties Candidates States ? 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 VIQING Chris Olston, UC Berkeley Parties Candidates States ? Result of Visual Reordering • Now, parties join with states, which join with candidates Georgia voted with the other Democrat states in ‘60, but against them in ‘64 – We can see the voting history of each state, by traditional party VIQING Chris Olston, UC Berkeley Parties States Candidates 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) 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 VIQING Chris Olston, UC Berkeley Removing Intermediate Tables • Often, 2 tables join via an intermediate table – eg, Candidates Vote records 92-TX-R 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 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 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 VIQING Chris Olston, UC Berkeley Generalizing “Small Multiple” Graphs • VIQING can create “small multiple” graphs – Several views of a graph, indexed by a variable Z=5 Z = 10 – This is a visual join between a canvas which contains several values for the index variable and the graph canvas 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 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 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] VIQING Chris Olston, UC Berkeley