The document discusses the development of visualization recommendation systems aimed at automating the process of suggesting visualizations based on patterns in large datasets. It highlights the challenges faced in current tools, such as poor context comprehension and limited user preference understanding, while also referencing ongoing projects and research efforts in the field. The emphasis is on moving towards effective systems that enhance data analysis and user experience in interactive analytics.

1. Aditya Parameswaran
Assistant Professor
University of Illinois
(w/ ManasiVartak, Samuel Madden @ MIT;
Tarique Siddiqui, Silu Huang @ Illinois)
http://data-people.cs.illinois.edu
DSIAWorkshop,VIS 2015
TowardsVisualization
Recommendation Systems
1

2. “Bring out your dead!” courtesy Monty Python
The Dark Ages ofVisualization
Recommendations
Substantial manual effort and tedious trial-and-error
2

3. To the Age of Enlightenment:
the Holy Grail
Can we build systems that automatically recommend
visualizations highlighting patterns of interest?
3
“The Holy Grail” courtesy Monty Python

4. Why now?
Reason 1: Too much data: records and attributes
Most of the dataset is unexplored!
4

5. Why now?
Reason 2: Lack of skills
Harvard Business Review Mashable.com
5

6. Limitations in CurrentTools
• Big Picture
• Analyst Preferences
• Specification
• Exploration
not ACID …
6

7. Limitations in CurrentTools
• Big Picture
– Poor comprehension of context
• Analyst Preferences
– Limited understanding of user interests
• Specification
– Insufficient means to specify trends of interest
• Exploration
– Inadequate navigation to unexplored areas
7

8. RecentAttempts atVizrec Systems
• Tableau Elastic
• Voyager
• Harvest
• Profiler
• Our systems
– SeeDB [VLDB 14 x 2,VLDB 16]
– zenvisage [unpublished]
This conference!
8
Still early days!

9. SeeDB: ComparativeTasks
Task:
Compare staplers (target, query)
with other products
Results:
Visualizations where staplers
“differ most” from other products
Issue: Many attributes  Many many visualizations!9
50
10 10
30
MA CA IL NY
30
20
10
40
Stapler sales
Other sales
Stapler prod
9
Other prod

10. : SearchTasks
Very early demo! Feedback welcome.
(you saw it here first...)
10

11. 5 RecommendationAxes
• Specification of IntendedTask or Insight
– e.g., comparative (X vs.Y), search (find X with a
desired criteria), outliers (find unusual X)
• Data Characteristics
– e.g., typical correlations, patterns, trends across
attributes, across rows
• Semantics or Domain Knowledge
• Visual Ease of Understanding
• Analyst Preferences
11data-people.cs.illinois.edu/papers/dsia.pdf

12. Architectural Considerations
• Pre-computation
• Online computation
–Sharing
–Parallelism
–Pruning
–Approximations [VLDB’15]
12data-people.cs.illinois.edu/papers/dsia.pdf

13. A Clarion Call to DSIA Researchers…
Visualization Recommendation Systems:
are critically important
are timely
lead to interesting viz, db, ml, hci problems
Let’s move towards the age of enlightenment!
“The Holy Grail” courtesy Monty Python
13
data-people.cs.illinois.edu/papers/dsia.pdf

14. Ongoing Projects in Interactive Analytics
Minimizing effort & maximizing efficiency
http://data-people.cs.illinois.edu
• Data Manipulation [VLDB’15 x 2]
• DataVisualization [VLDB’14 x 2,VLDB ’15,VLDB ‘16]
• Data Collaboration [VLDB ’15 x 2, CIDR ’15,TAPP ’15]
• Data Processing with [VLDB ’15, HCOMP ’15, KDD ‘15]
datahub
14
Recent Papers, Demos
POPULACE

15. 15

16. ResearchThrust II: Crowds
Minimizing cost and maximizing accuracy in
human-powered data management
Data Processing
Algorithms
Auxiliary Plugins:
Quality, Pricing
Data Processing
Systems
Filter [SIGMOD12,VLDB14] Max [SIGMOD12]
Clean [KDD12,TKDD13] Categorize [VLDB11]
Search [ICDE14] Debug [NIPS12] Count [HCOMP15]
Deco [CIKM12, VLDB12, TR12, SIGMOD Record 12]
DataSift [HCOMP13, SIGMOD14] HQuery [CIDR11]
Conf [KDD13, ICDE15] Evict [TR12] Debias [KDD15]
Pricing[VLDB15] Quality [HCOMP14]
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17. Human-in-the-loop
Data Management
Dual personalities
• Analysts supervising the analysis
– How do we help them get the insights they want?
• Crowds helping the analysis
– How do we best make use of them to process data?
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18. Visualizations
Queries (100s)
Sharing
Pruning
Optimizer
DBMS
Middleware
Layer
18

19. Task Specification
ManualVisualization Builder
Visualization Pane
Recommendation Bar

20. User Study
Part I :Validate utility metric vs. other metrics
– See paper!
Part II : Study impact of recommendations
– H1: SeeDB finds interesting visualizations faster
– H2: Users prefer tool w/recommendations

21. I. SeeDB enables faster analysis
• Users view more visualizations with SeeDB
• Users bookmark more visualizations with SeeDB
• Bookmark rate 3X higher with SeeDB
# charts # bookmarks bookmark rate
Manual 6.3 +/- 3.8 1.1 +/- 1.45 0.14 +/- 0.16
SeeDB 10.8 +/- 4.41 3.4 +/- 1.35 0.43 +/- 0.23

22. II. Users Prefer SeeDB
100% users prefer SeeDB over Manual
“. . . quickly deciding what correlations are relevant” and
“[analyze] . . . a new dataset quickly”
“. . . great tool for proposing a set of initial queries for a
dataset”
“. . . potential downside may be that it made me lazy so I
didn’t bother thinking as much about what I really could study
or be interested in”

23. Questions on Part 2?

24. Overall research agenda …
Human-in-the-loop
Data Management
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