Jeff Stanton discusses discovery informatics for analyzing large datasets. He notes that the amount of data is growing rapidly but IT spending is not keeping pace. Traditional data exploration methods are insufficient for "big data." Emerging alternatives are needed for creating and analyzing large datasets. Stanton provides examples of big data in retail and jobs in the field. He also discusses costs of data reformatting and search failures. The document explores using crowdsourcing, natural language processing, and visualization tools to translate and validate psychological assessment items across languages and cultures.

1. Discovery Informatics:
Multimodal Information Interfaces
for Creating & Analyzing Large
Data Sets
By Jeff Stanton
School of Information Studies
Syracuse University

2. 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

3.  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

4. 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

5. The (Human) Cost of “Joins”

6. R/R-Studio
Commercial support
from R comes from
Revolution Analytics;
Oracle, IBM,
Mathematica, SPSS,
are among the major
companies offering R
integration
IBM Platform HPC
provides parallel
computing options
for R (jaql, netezza)

9. 0
1
2
3
4
5
Channels
(log)
Kbits/Sec
(log) Frame
Rate, 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 pitches
Loudness discrimination: >40 levels
Timing discrimination: 20 ms
Horizontal localization: ~8 positions
Vertical localization: ~4 positions
Timbre variations: ∞
Image credit: “The Five
Senses” by Fabio Pantoja

11. Holographic Table Display

14. 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

15. Discovery Informatics for Psychology
Study Design
Workspace
Crowdsourced
Data Collection
Data
Cleaning/Dim.
Reduction
Data Linking &
Mapping
Visualization &
Animation