Uncertainty-Aware Uncertainty-Aware Data Transformations for Data Transformations for Collaborative ReasoningCollaborative Reasoning

Kwan-Liu MaQuickTime™ and a

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Research GoalResearch Goal

Develop mathematical foundations for uncertainty-aware data transformations to facilitate trustworthy collaborative reasoning using visual means

Proposed TasksProposed Tasks

1. Developing data transformation methods, coupled with uncertainty measures, for the extraction of relational and semantic structures of data

2. Modeling of uncertainty extraction, propagation and aggregation from transformations to reasoning

3. Studying applications for supporting Uncertainty-aware collaborative reasoning

Mathematical modeling of uncertainty-aware visual analytics process

Evaluating Visual Analytics Process Evaluating Visual Analytics Process using Uncertainty Propagationusing Uncertainty Propagation

• Formalize the representation of uncertainty and basic operations

• Quantify, propagate, aggregate, and convey uncertainty through a series of data transformations

• Enhance and evaluate visual reasoning using uncertainty

An Uncertainty-Aware An Uncertainty-Aware Evaluation FrameworkEvaluation Framework

Sensitivity Modeling

DataSources

Derived Data/Abstractions

Visual Elements

InsightDATA/VISUAL TRANSFORMATIONS

VISUAL MAPPING

VIEW

SensitivityCoefficients

Uncertainty Propagation Uncertainty on

Derived DataSource Uncertainty

Uncertainty Visual Mapping

Uncertainty Views

Sensitivity Analysis

Comparing differenttransformation methods

Case 1: Geo-temporal DataCase 1: Geo-temporal Data

• Records on migration using boats over a period of 3 years• Analysis to study landing patterns and estimate landing success

rate • Hypothesis: distance and time are correlated• How much confidence can we place on our findings?

Go-fast

Rustic

Raft

Data and Transformation UncertaintyData and Transformation Uncertainty

• Uncertainty: incomplete data, accuracy of distance computation

• Data Completion– Pair-wise deletion– Cluster based

• Distance estimation– Model dependent– Evaluation uses

sensitivity analysis

Sensitivity AnalysisSensitivity Analysis

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Case 2: Social Network AnalyticsCase 2: Social Network Analytics

• Cell phone records for 400 people• The goal is to characterize the sub-networks of

interest linked to a particular cell phone user.

Structural AbstractionStructural Abstraction

Transformation using Transformation using Relative ImportanceRelative Importance

Uncertainty Analysis ofUncertainty Analysis ofthe Importance Transformthe Importance Transform

• Node size: importance• Halos and error bars: uncertainty• Pie chart: sensitivity parameters wrt connected nodes

• Weighted based on number of calls• 200 mostly depends on 5, 137 and 2

Impact of the Proposed WorkImpact of the Proposed Work

• First work addressing uncertainty throughout the whole visual analytics process

• Providing a more trustworthy view of the data• A framework for cross comparison of different

data transformation methods • Providing a mechanism for assessing “what

if” scenarios

Plans Plans for Developingfor Developing FODAVA FODAVA

• Research collaborations with FODAVA lead institute and RVAC centers

• Demonstration and dissemination of research results through a variety of mechanisms

• Other outreach and education activities– Industry– VisWeek/VAST– SIGGRAPH, KDD, ICDM, …

Kwan-Liu MaKwan-Liu Mama@cs.ucdavis.eduhttp://www.cs.ucdavis.edu/~ma