Discovery Informatics: Multimodal Information Interfaces

for Creating & Analyzing Large Data Sets

By Jeff Stanton

School of Information Studies

Syracuse University

Where are we going?

Ever increasing amounts of data to display/diagnose

Traditional data exploration methods

Emerging alternatives for creating/analyzing big data

Example Application

Discovery Informatics for Psychology

McKinsey: 40% growth in data per year with only 5% growth in IT spending.

WalMart: Collects 2.5 PB per hour from customer transactions.

IDC: Big data not simply a matter of size, but rather of growth rate, speed of acquisition, rate of decay, linkage complexity, and format heterogeneity.

Gartner: 1.47 million big data jobs unfilled

The Dimensions of Big Data

An organization employing 1,000 knowledge workers loses $5.7 million annually in time wasted reformatting data as it moves among applications. Search failures cost that same organization an additional $5.3m a year. (Source: IDC)

The Costs of Big Data

The (Human) Cost of “Joins”

R/R-Studio

Commercial support from R comes from Revolution Analytics;

Oracle, IBM, Mathematica, SPSS, are among the major companies offering R integrationIBM Platform HPC provides parallel computing options for R (jaql, netezza)

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Channels

(log)Kbits/Sec

(log) FrameRate, Hz

Sensing Big Data

Rough estimates based on Balasubramanian(2006), Current Biology

• Hearing is multi-directional – does not require attentional focus on a single source• Hearing is the most acute of the senses in detecting the frequency of occurrence

of events – as little as 5 ms apart• Hearing supports “multi-tasking” by allowing the brain to detect events occurring

at different frequencies and time-scales simultaneously

Pitch discrimination: >90 pitchesLoudness discrimination: >40 levelsTiming discrimination: 20 msHorizontal localization: ~8 positionsVertical localization: ~4 positionsTimbre variations: ∞

Image credit: “The Five Senses” by Fabio Pantoja

Holographic Table Display

Example Application

1. Research goal: Translate selection test items and re-check psychometric characteristics

2. Assemble baseline data from validation study(ies) in original language

3. Crowdsource item and answer translations with bilingual native speakers

4. Use natural language processing to visualize most common wording variations by regional dialect by linking to map data

5. Choose most universal item texts and answers

6. Crowdsource backtranslations with bilingual native speakers; return to step 3 as needed

7. Deploy final version of test; compare results to baseline data and return to step 3 as needed

Discovery Informatics for Psychology

Study Design Workspace

CrowdsourcedData Collection

Data Cleaning/Dim.

Reduction

Data Linking & Mapping

Visualization & Animation