Presentation from FlockData at DC Tech Startup Pitch Night in July 2015. Learn how FlockData combines multiple open-source data tools, including graph data, search data and timeline, to help customers solve problems.

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Open-source information management and data integration
COLLECT | CONNECT | COMPARE
TURNING DATA INTO INFORMATION

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Companies
resistant to data
do not thrive.
Even if you have the
desire…
you can’t get the
data…
It exists in 27
different computer
systems with
different structures.

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What do the world’s largest & most
complex data stores have in common?
Sources: https://gigaom.com/2013/06/06/heres-how-the-nsa-analyzes-all-that-call-data/
https://gigaom.com/2013/06/07/under-the-covers-of-the-nsas-big-data-effort/
http://neo4j.com/blog/why-the-most-important-part-of-facebook-graph-search-is-graph/
•multiple instances each storing tens of petabytes
•backend of the agency’s most widely used analytical
capabilities
•Accumulo is especially adept at analyzing trillions of data
points in order to build massive graphs
•Technology giants such as Facebook, Google, and Twitter
have all built graph technologies from the ground up to
differentiate and grow their business. Building and
maintaining one’s own database management system
however is not a practical solution if you’re not Facebook.
•PageRank changed the fundamentals of web search - taking
into account how the pages are connected
•Facebook = Social graph
•Google = Knowledge graph
•Twitter = Interest graph

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Multi-model is the key to
modern and future data

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Connections Analytics Timeline

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Graph Search Document

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Graph Search Document
Any data source(s)
HTTP RESTful API integration

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Graph Search Document
Any data source(s)
HTTP RESTful API integration

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Graph Search Document
Reports Apps Structure
Any data source(s)
HTTP RESTful API integration

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FlockData provides a single,
unified multi-model access
point for both data storage and
information retrieval

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Connect Meta Data
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Meta-data captured
and stored
Connections are made for
analysis
See hidden relationships
fast!

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Dashboards

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Versions of Data

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Industries:
- Online media
- Telecommunications
- Financial Services
- Healthcare
- Logistics
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Solution Categories:
- Recommendation Engines
- Network mapping & analysis
- Cross-source analytics
- Data-driven apps
- Universal search & audit
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Recommendation Engine
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What is a recommendation engine?
Incorporates any number of factors about users
Notably including products or services consumed
Leverages multiple related factors (similar products,
similar users, etc)
Traverses these factors as connections
Returns the most connected nodes as
recommended products or services
An algorithm that:

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Why build on graph data?
Only need to specify which
type of relationships to use
As little as 2-line queries
Performance:
1M rows —> ~20ms
But very little scale effect
Fast-enough for real-time performance
Efficient and flexible for expanded use
Lookalikes

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Why build a recommendation engine?
Original search
Bought together = up-sell
Also bought = up-sell
Targeted ads = cross-sell
Also viewed = conversion

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Overlay social graph of users
Insert taxonomy here
Add as many factors as you have
Each factor improves quality

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Merged graph

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Graph as the basis for recommendations

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Case Study: Macro view of ebola
Obtain a sample data set of 48K Twitter posts.
Send tweets through NLP engine for tag capture, entity &
concept extraction, sentiment analysis
FlockData JSON transformation and import definition in
under 1 day
Leverage our automatic analysis tools (word cloud, graph,
visualizations) to find connections
Use dashboards to get overview of breakdown
Use cluster analysis to find “hot spots” in the data

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Quick findings: From concept to insights in under 2 days
Sentiment, tags and concepts
are sortable, reportable, and
can be integrated with real-
time data feeds
Geo-location of user gives
automatic mapping

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Quick findings: Locate hot spots
Data categories sorted by
co-occurrence - shows
organizations where to focus
for maximum impact
FlockData data profiling
during data load is used to
drive reporting