The document discusses how Digital Reasoning uses advanced algorithms to analyze large-scale unstructured data for the defense and intelligence community. It emphasizes building dynamic social networks from noisy data, enabling insights into relationships and connections without a prior structure. The platform aims to transform chaotic information into actionable intelligence while addressing customer concerns about data privacy and ownership.

1. Generating Dynamic Social
Networks from Large Scale
Unstructured Data
Enterprise Software to Make Sense of Really Junky Data
Tim Estes - CEO, Digital Reasoning

2.  What We’ll Discuss
• What is a social network?
• The web of relationships between entities that influences actions
• Why does it matter?
• To reference Aesop: “You are known by the company you keep.”
• What’s required to build one algorithmically?
• What’s similar, what’s the same, what’s connected

3.  What’s similar?
We use patented algorithms for deducing related terms from the data…
Bush White Justin Britney
Nashville
House Timberlake Spears
president bush house tenn miley cyrus britney spears
president george w gov the predators pussycat dolls the album
administration white predators bob dylan x factor
bush administration clinton oakland nine inch nails my friends
george the administration milwaukee rock star mtv
george w president-elect st louis the timberwolves madonna
george bush barack obama carolina sean preston lady gaga
brown barack a season lanarkshire singer
american president george w baltimore ticket prices a student
clinton kentucky nme

4.  What’s the same?
Concept resolution:
Roll up similar things into groups of the same (again, algorithmically)
Example: Tony Blair

5.  What’s connected?
Link analysis:
Show who and what are connected (again, you guessed it, algorithmically)
Terrorist Leader Connections

6. Let’s Put an Idea to the Test...
 With powerful analytics can you remove some or
most of the need for a priori structure in designing
and understanding social networks or other quasi-
YES
ontological schemas? and
 Can you also do it with messy unstructured data?
YES

7. But first...
Why do we (Digital Reasoning) care?

8. Because its what we do for a living.
We make sense of the senseless.
 Our customers have critical needs
- Digital Reasoning works primarily in the Defense and Intelligence
Community making sense of noisy, unstructured data and turning it
into usable entity-centric systems supporting mission critical
intelligence.
 The data is big and bad
- Little structure in content, topics all over the place, and totally different
ontologies/schemas across the community.
 The times we live in create urgencies
- We care because the better and faster we are at making sense of this
kind of data, the safer our country is.

9. Why did we take a data-centric, deployed software model?
 Unique Environments
- Given who our customers are... we can’t host their data. No one can.
The solution had to be a pure deployed software model.
 Meaning in Hard to Reach Places
- The data is basically a bunch of pieces that don’t want to be connected.
People that don’t want to be found.
 Result?
- Imagine trying to turn that kind of data in that type of architecture from a
bunch of loose communication into a social network that has patterns of
life, weightings of influence, and projections of probable future actions...

10. Here’s what it looks like in an architecture…

11. Now let’s show what can be learned with a little application of
Entity-Oriented Analytics to a bunch of web data.

12. Test Case
 Web Blog+Wikipedia data (collected by Fetch)
- 6M Blog URLs collected over 1Yr +
- 16M unique blog messages
- no unifying these, topic or author
- tricky to get “good” big data from the open web. ended up using .5% of that
original source. 1TB became 4GB.
 No a priori structure, sparse metadata, nearly all meaning emerges
from analysis
 Let’s see what we can find out...

13. Examining connections related to “Carl Icahn”
The data shows
connections to and from
Carl Icahn by:
• people
• periodicals
On closer examination • topics
the data tells us: • companies
Carl Icahn “is backing” a
startup company that
“would build” products
related to Barack Obama

14. Let’s examine what connections we find to “Egypt”
Egypt is identified as a
location, as an organization
(country) and as an
unassigned entity with all
related connections
On closer examination we see
interesting connections in the
blogs for Egypt, Cairo, Issues
and the phrase “powder keg”.
If we drill down into the actual
blog entry we see the context of
the connections

15. How about connections to “Steve Jobs”?
One connection isconnections in
The entities and interesting: Topics
“Steve Jobs” to “Walt Mossberg”
the blog data are vast – which
to “Kindle”
is not surprising. Authors
Synthesys shows the of authors
The large amount reason for
connection as “pricing” popularity
and topics reflect the
of Steve Jobswordawe see the
Clicking on this as blog subject
context of the connection

16. Demo Platform
 Synthesys Platform Beta
 elastic
 user-driven
 entity-oriented-analytics on demand

17. Observations
 New innovations will be algorithmic and focused on turning hard-
to-use data into dynamic, evolving knowledge that can automate
machine execution
 Architectures/solutions will have to accommodate customers that
don’t want to move their data to a Public Cloud
 It is a true statement... “If you can connect the dots, you can
connect the people”

18. So why should You care?
 Because there is a lot of data that doesn‘t belong on a shared grid.
Such as Top Secret data, Sensitive Corporate Data, and Personal
Data.
 Because people may want to own (Personal Computing model)
vs. rent (Mainframe model) analytics
 Because you may not want to convert your data to fit the model of
the hosted solution or map to their ontology to get the answers
you need.

19. To learn more…
 See us at:
- Strata Science Fair (Wed evening 6:45PM)
- Digital Reasoning Booth #305
- www.digitalreasoning.com

20. Questions?
Automated Understanding, Trusted Decisions, True Intelligence