1. Architectural Support for Database Visualization
Dennis Groth
Indiana University Computer Science
May 1, 2002

2. Talk Structure Problem Motivation Overview of Visualization Process
Data Preparation
Definable Maps
Visualizing Databases
Summary and Future Research
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Dennis Groth

3. Motivation What is visualization?
“the act or process of interpreting in visual terms or of putting into visible form.”
[Webster’s]
“Transforming the symbolic into the geometric.”
[McCormick et al, 1987]
“The binding (or mapping) of data to representations that can be perceived.”
[Foley, 1994]
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Dennis Groth

4. The Goal of Visualization
Gain insight into data
Understand the “whole”
Different than presentation graphics:
Used to communicate information to others
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5. Classic Presentation Display of 6 variables. [Minard, 1861] 9/19/2018
Dennis Groth

6. Scientific Visualization
Visual representation of scientific data.
Rainfall in Peru over 3 day period
[Goldberg et al, 1987]
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7. Information Visualization
Visual representation of abstract data.
[Shneiderman, et al]
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8. What’s the Difference? Scientific data: Abstract data:
Often, already numeric
Natural mapping to coordinates
Abstract data:
May not be numeric (No order, No scale)
Mappings must be defined or constructed
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Dennis Groth

9. Visualization Process (KDD)
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10. Interpret Patterns
Discriminating clusters
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11. Research Mission
To leverage database techniques and technologies in order to enhance visualization activities, contributing to measurable improvements in efficiency, effectiveness and satisfaction of users.
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Dennis Groth

12. Research Contributions
Architecture
Mapping and Map Specification
Measuring Usability
System Implementation
Application
Visualizing relationships
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Dennis Groth

13. Architecture User Front End DB Query Specification Map Specification
Filtered
Image
Query
Specification
Map
Specification
Filter
Image
Display
Query
Map
Scaled
Data
Rendering
Data
Domain
Filtered
Data
Data
Extraction
Data
Preparation
Visual Query
Plot
Raw
Data
Pre-
Image
Unscaled
Data DB
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Dennis Groth

14. Architecture User Front End DB Query Specification Map Specification
Filtered
Image
Query
Specification
Map
Specification
Filter
Image
Display
Query
Map
Scaled
Data
Rendering
Data
Domain
Filtered
Data
Data
Extraction
Data
Preparation
Visual Query
Plot
Raw
Data
Pre-
Image
Unscaled
Data DB
9/19/2018
Dennis Groth

15. Simple Visualization Output Input Data t1 t2 t3 . tn 9/19/2018
Dennis Groth

16. Database Activities Salary Age Sex Salary Age … M 640001 57
Select Salary,Age,Count(*)
From Employee
Group By Salary,Age
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Dennis Groth

19. Architecture User Front End DB Query Specification Map Specification
Filtered
Image
Query
Specification
Map
Specification
Filter
Image
Display
Query
Map
Scaled
Data
Rendering
Data
Domain
Filtered
Data
Data
Extraction
Data
Preparation
Visual Query
Plot
Raw
Data
Pre-
Image
Unscaled
Data DB
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Dennis Groth

20. Data Preparation Filtered Map Data Map Join Aggregation Pre-Image
Raw Data
Canned Algorithms
Future Extensions
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21. Map Join Output Input Data Map t1 m1 t2 m2 t3 m3 . . tn mn 9/19/2018
Dennis Groth

22. Database Activities SalaryRank Age Sex Salary Age … M 640001 57 Salary
3318 …
Age
Select SalaryRank,Age, Count(*)
From Employee, SalaryMap
Where Employee.Salary = SalaryMap.Salary
Group By SalaryRank,Age
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Dennis Groth

24. Architecture User Front End DB Query Specification Map Specification
Filtered
Image
Query
Specification
Map
Specification
Filter
Image
Display
Query
Map
Scaled
Data
Rendering
Data
Domain
Filtered
Data
Data
Extraction
Data
Preparation
Visual Query
Plot
Raw
Data
Pre-
Image
Unscaled
Data DB
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Dennis Groth

25. Visual Query Defines the linkages between the data and the display
Pre-defined schema for each visualization
Histogram – {X, Height}, {X, Y, Height}
Scatterplot – {X, Y}, {X, Y, Aggregation}, …
Line, Surface, …
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Dennis Groth

26. Architecture User Front End DB Query Specification Map Specification
Filtered
Image
Query
Specification
Map
Specification
Filter
Image
Display
Query
Map
Scaled
Data
Rendering
Data
Domain
Filtered
Data
Data
Extraction
Data
Preparation
Visual Query
Plot
Raw
Data
Pre-
Image
Unscaled
Data DB
9/19/2018
Dennis Groth

27. Map Maps are relations, so: Applied with relational operators
Modifications to maps do not affect base data
Multiple maps can be applied to one dataset
One map can be applied to multiple datasets
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28. Constructing Maps Standard database operations: Insert statements
Constant values: Insert into MonthMap values (1, “January”)
Calculated values: Insert into MonthMap Select floor(SalesAmount / 1000) as MapValue, * From MonthlySales
Algorithm driven (cluster, binning, etc.)
Mapping Language
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29. Mapping Language Map program – P = < p1, p2, …, pk >
Based on Datalog
No recursion, no negation
Each rule is written as:   
 is a boolean expression
 is an expression that evaluates to a numeric value
Functions are allowed
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30. Map Program Rules
Rules are defined over sets of attributes, not specific tables
(t) is the value of the head of the rule when substituting attribute values from tuple t
(t) is the value of the body of the rule when substituting attribute values from tuple t
Two special types of rules
Except   (Excludes tuples)
  Else (If all preceding rules fail)
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Dennis Groth

31. Map Program Interpretations
Given P = < p1, p2, …, pk > and input instance s
Relational Interpretation:
IR(P,s) = {< t , i(t) > : t  s  1  i  k  i(t)}
Functional Interpretation:
IF(P,s) = {< t , i(t) > : t  s  1  i  k  i(t) 
(j)[1  j < i  j(t)]}
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Dennis Groth

32. Properties of the Language
Monotonic - Equivalent to subset of RA
Safe - Finite output
Closed – Takes as input a relation and returns a relation
Allows composition - IR(P, IF(Q, IF(R,s) ) )
One implementation supports both interpretations
Complexity - O(|input|)
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Dennis Groth

33. SQL Approach Each rule has an equivalent SQL query
Select  From InputRelation Where 
Each program has an equivalent union query
Select 1 From InputRelation Where 1 UNION …
… UNION Select k From InputRelation Where k
Complexity - O(|input| k)
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34. Example Map Programs 1 Month = ‘January’ 2 Month = ‘February’ . . .
Age  Sex=‘F’
Age  Else
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36. Map Language Usability
Prior studies
Early work (Reissner, Welty)
SQL vs QBE (Yen and Scamell)
SQL vs Visual Language (Catarci, et al)
Goal:
Quantify usability of the mapping language
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37. Experiment Design Subjects
27 undergraduate students (Low skill level)
28 graduate students (Medium skill level)
10 professionals (High skill level)
All 3 groups attempted the same tasks and were given the same training materials
Half of each group used SQL, the other half used the mapping language
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38. Results Accuracy (Within Group) Satisfaction (Pre to Post Comparison)
Undergraduates perform better with rules (p < .10)
Graduates perform better with rules (p < .10)
Note: Excludes 3 subjects that did not answer any problems
No difference for professionals
Satisfaction (Pre to Post Comparison)
Undergrad/Professional satisfied with rules
Everyone not satisfied with SQL (p < .10)
Preference
Professionals preferred rule language (p < .001)
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Dennis Groth

39. Results Interesting trend for satisfaction (5=best) Rule Satisfaction
Pre-Test
Post-Test
Undergrad
2.9
3.0
Grad
3.2
Professional
SQL Satisfaction
Pre-Test
Post-Test
Undergrad
3.2
2.6
Grad
3.1
2.5
Professional
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Dennis Groth

40. System Implementation
Queries
Database
Client
Server
Map Requests
Map Construction
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Dennis Groth

42. User Interaction Rotation, Translation, Scaling Drill-down queries
Select data-points for use in other contexts
Like brushing
Combining plots
Scaled independently or dependently
Overlay, Offset, Tile
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Dennis Groth

43. Visualization of Databases
Proof of concept application
Mapping based on entropy
Allows insight into structure of a relation
Functional Dependencies (Exact)
Approximate Dependencies (Almost an FD)
Information dependency measures (Dalkilic and Robertson, 2000)
Visualizations show every relationship in one plot
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Dennis Groth

44. Identifying Relationships
Function-Like
Functional Dependency
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45. Visualization of Databases
Entropy calculation :: H(A) = -  pi log(1/pi) where each pi is the probability of ai in the active domain of attribute A
Gives the average number of bits needed to transmit an A value
0  H(A)  log(|Adom(A)|)
H(AB) = H(AB) – H(A)
If H(AB) = 0, the FD AB holds
Provides a measure of approximateness
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Dennis Groth

47. Comparing Datasets
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48. Summary Architecture supporting visualization Mapping as a key element
Mapping language
Usability evaluation
System Implementation
Mission: To leverage database techniques and technologies in order to enhance visualization activities, contributing to measurable improvements in efficiency, effectiveness and satisfaction of users.
9/19/2018
Dennis Groth

49. Future Research Visualization Data Mining Human Computer Interaction
Extensions driven by applications
Data Mining
Rule management (information overload)
Human Computer Interaction
Empirical testing of application manager user interface
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Dennis Groth

50. Questions
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