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Aditya ParameswaranAssistant ProfessorUniversity of Illinois

(w/ Manasi Vartak, Samuel Madden @ MIT;Tarique Siddiqui, Silu Huang @ Illinois)

http://data-people.cs.illinois.eduDSIA Workshop, VIS 2015

Towards Visualization Recommendation Systems

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“Bring out your dead!” courtesy Monty Python

The Dark Ages of Visualization Recommendations

Substantial manual effort and tedious trial-and-error

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To the Age of Enlightenment: the Holy Grail

Can we build systems that automatically recommend visualizations highlighting patterns

of interest?

“The Holy Grail” courtesy Monty Python

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Why now?Reason 1: Too much data: records and attributes

Most of the dataset is unexplored!

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Why now?Reason 2: Lack of skills

Harvard Business Review

Mashable.com

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Limitations in Current Tools

• Big Picture• Analyst Preferences• Specification• Exploration

not ACID …

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Limitations in Current Tools

• 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

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Recent Attempts at Vizrec Systems

• Tableau Elastic• Voyager • Harvest • Profiler • Our systems– SeeDB [VLDB 14 x 2, VLDB 16]– zenvisage [unpublished]

This conference!

Still early days!

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SeeDB: Comparative TasksTask: Compare staplers (target, query) with other productsResults:Visualizations where staplers “differ most” from other products

Issue: Many attributes Many many visualizations!

MA CA IL NY

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10 1030

3020

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Stapler sales

Other sales

Stapler prod

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Other prod

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: Search Tasks

Very early demo! Feedback welcome.

(you saw it here first...)

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5 Recommendation Axes• Specification of Intended Task 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

data-people.cs.illinois.edu/papers/dsia.pdf

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Architectural Considerations• Pre-computation• Online computation–Sharing –Parallelism–Pruning–Approximations [VLDB’15]

data-people.cs.illinois.edu/papers/dsia.pdf

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A Clarion Call to DSIA Researchers…

Visualization Recommendation Systems:are critically importantare timelylead to interesting viz, db, ml, hci

problemsLet’s move towards the age of enlightenment!“The Holy Grail” courtesy Monty Python

data-people.cs.illinois.edu/papers/dsia.pdf

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Ongoing Projects in Interactive Analytics

Minimizing effort & maximizing efficiencyhttp://data-people.cs.illinois.edu

• Data Manipulation [VLDB’15 x 2]

• Data Visualization [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

Recent Papers, Demos

POPULACE

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Research Thrust II: CrowdsMinimizing cost and maximizing accuracy

inhuman-powered data managementData

ProcessingAlgorithms

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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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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Visualizations

Queries (100s)

Sharing

Pruning

Optimizer

DBMS

Middleware Layer

Task Specification

Manual Visualization Builder

Visualization Pane

Recommendation Bar

User StudyPart 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

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

II. Users Prefer SeeDB100% 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”

Questions on Part 2?

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Overall research agenda …

Human-in-the-loop Data Management

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