This document discusses examples of persisting streaming text into a database for visualization. It describes extracting news from online sources, using natural language processing to convert the text to a visual diagram mapped into a graph database. The goal is to link concepts and events via natural language processing to visualize the story and build a mashup of linked data from other online sources for analysis and recommendations.

1. Text to Graph in real time
Examples of persisting streaming
text into DB for visualization

2. 1) News extracted from a social network( or
any other online medium)

4. 2) First pass: Automated data extraction:
• Companies:Watson Pharmaceuticals,Sanofi
Aventis,Anda Inc
• Product: Avapro,Irbesartan
• Terms:Generic drug, Treatmant of hypertension,
• Event: Company Affiliation, FDAPhase ,
CompanyProduct

6. Second Pass: Convert to visual diagram by
applying NLP
and mapping into Graph Database

7. Why Do it ?
Link concepts and events via NLP to visualize the
story:

8. Build a mash up from other
online sources – “Linked Data”

9. Concept Mapping Features :
Increasing Text Parsing Efficiency

15. • Why Do it ?

16. 5) Analyze links and “derived data” to build
matching profile/recommendation
Query derived links from news story to augment facts and aggregate
more data, which in turn can be applied towards custom
recommendations.
The software will be able to identify automatically without human
intervention relations between companies and people and query in
depth consequences of event ( Affiliates – “Renewable Ventures -
investment -Xcel Energy-bid Univ of Colorado-bid solar installation-
AMEX plc –implementation-key people-competitors-technologies involved
–OPPORTUNITY :match/recommendation against existing facts in
the database!
Single use case examined here – there are hundreds like these parsed
each day.
Potential for reference data and opportunity indexing.

17. Innovative Financial Data
Visuals Software

18. Stock Touch : Stock “Heat
Maps”

20. Centrifuge : Visual Network
Analytics of Fraud