In this security solution demo, we have integrated Oracle NoSQL DB with InfiniteGraph to demonstrate the power of using the right tools for the solution. By integrating the key value technology of Oracle with the InfiniteGraph distributed graph database, we are able to create new views of existing Call Detail Record (CDR) details to enable discovery of connections, paths and behaviors that may otherwise be missed. Discover how to add value to your existing Big Data to increase revenues and performance!

1. The Database
The Power of Relationships in
Big Data
Leon Guzenda - Objectivity, Inc.
Silicon Valley NoSQL Meetup - 1/23/14
© Objectivity, Inc. 2014

2. Overview
• The Problem
• Current Big Data Analytics
• Relationship Analytics
• Leveraging NoSQL
• Big Data Connection Platform
• Solution Use Case Demo
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3. Objectivity, Inc.
• Headquartered in San Jose, CA
• Over two decades of NoSQL and Big Data experience
• Enables complex data virtualization and Big Data
solutions for the enterprise
• Software products:
• Objectivity/DB
• InfiniteGraph
• InfiniteGraph Social App
• Embedded in hundreds of enterprises, government
organizations and products, with millions of
deployments.
© Objectivity, Inc. 2014
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4. A Typical Deployment
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5. Current Big Data Analytics
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6. The Problem
Information Overload!
• Making sense of it all takes time and $$$
• Which lead to a rush to Big Data Analytics
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7. Current Big Data Analytics
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8. Leveraging NoSQL
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9. Not Only SQL - Four Main Technologies
Simple
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Highly
Interconnected
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10. Hadoop?
Hadoop:
Objectivity/DB & InfiniteGraph
• Parallel processing using a
divide and conquer or split and
merge paradigm
• Distributed processing with
multithreading client processes
and simple servers*
• Sharded, distributed file system
• Distributed, segmented
Federated Database with a
Single Logical View down to
fine grain objects
• Tuned for sequential scans and
simple queries
• Tuned for random access and
powerful parallel queries
• Not suitable for highly
interconnected data sets
(graphs)
• Excel at handling very large
graph structures with built-in
relationship analytics
* Process workflow could be driven using MapReduce
© Objectivity, Inc. 2014
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11. Incremental Analytic Improvements Aren’t Enough
• All current solutions use the same basic architectural
model.
• None of the current solutions has an efficient way to
store connections between entities in different silos.
• Most analytic technology focuses on the content of the
data nodes, rather than the many kinds of connections
between the nodes and in those connections.
• Why? Because most DBMSs are bad at handling
relationships.
• Object and Graph Databases can efficiently store,
manage and query the many kinds of relationships
hidden in the data.
© Objectivity, Inc. 2014
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12. Relationship Analytics…
A SQL Shortcoming
Table_A
Table_B
Table_C
Table_D
Table_E
Table_F
Table_G
There are some kinds of complex relationship handling problems that SQL
wasn't designed for.
© Objectivity, Inc. 2014
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13. …Relationship Analytics
A SQL Shortcoming
Table_A
Table_B
Table_C
Table_D
Table_E
Table_F
Table_G
InfiniteGraph - The solution can be found with a few lines of code
A3
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G4
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14. Graph Terminology
●
VERTEX: A single node in a graph data structure
●
EDGE: A connection between a pair of VERTICES
●
PROPERTIES: Data items that belong to a particular Vertex or Edge
●
WEIGHT: A quantity associated with a particular Edge
●
GRAPH: A network of linked Vertex and Edge objects
Vertex 1
City: San Francisco
Pop: 812,826
© Objectivity, Inc. 2014
Edge 1
Road: I-101
Miles: 47.8
Vertex 2
City: San Jose
Pop: 967,487
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15. Example 1 - Relationship Analytics
MARKET ANALYSIS
SOCIAL NETWORK ANALYSIS
LOGISTICS
HEALTHCARE INFORMATICS
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16. Finding The Links…
Events/Places
People/Orgs
Situation X
Combatant A
A Called P
A Seen Near X
P Emailed S
Situation Y
Bank X
P Called Q
Q Seen Near T
X Paid S
Target T
Civilian P
P Called R
R Seen Near T
Cafe C
Civilian Q
A Banks at X
S Seen Near T
Facts
Civilian R
Civilian S
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A Seen At Y
A Eats At
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17. …Finding The Links…
EDGES
VERTICES
Events/Places
People/Orgs
Facts
Situation X
Combatant A
A Called P
A Seen Near X
P Emailed S
Situation Y
Bank X
P Called Q
Q Seen Near T
X Paid S
Target T
Civilian P
P Called R
R Seen Near T
Cafe C
Civilian Q
A Banks at X
S Seen Near T
Civilian R
Civilian S
© Objectivity, Inc. 2014
A Seen At Y
A Eats At
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18. …Finding The Links…
Situation X
Seen Near
Eats At
Cafe C
Combatant A
Seen At
Called
Banks At
Bank X
Civilian P
Called
Civilian Q
Called
Emailed
Civilian R
Seen Near
Seen Near
Situation Y
Paid
Civilian S
Seen Near
Target T
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19. …Finding The Links…
Situation X
Seen Near
Combatant A
Seen At
Called
Situation Y
Banks At
SUSPECTS
Bank X
Civilian P
Called
Civilian Q
Called
Civilian R
Seen Near
Seen Near
Target T
© Objectivity, Inc. 2014
Emailed
Paid
Civilian S
Seen Near
NEEDS PROTECTION
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20. …Finding The Links
OTHER
DATABASE(S)
GRAPH DATABASE
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21. Example 2 - Finding Patterns in Open Source Data
The Challenges
●
Data Volumes
●
Fast-Changing Data
●
Sensitivity of Data
●
Significance of Data
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22. Example 3 - Cybersecurity
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23. Big Data Connection Platform
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24. Objectivity’s Disruptive Big Data Architecture
Uses Data Virtualization to hide the nodes and focus on the connections
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25. InfiniteGraph
Distributed Parallel Load and Queries
Powerful Graph Queries
X
Start
Start
X
Computational and Visualization Plugins
Distributed Parallel Link Finding
Latency Exceeded
Start
Start
© Objectivity, Inc. 2014
Finish
Custom Visualizer
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26. Solution Use Case Demo…
Let’s see InfiniteGraph coupled with Oracle’s
NoSQL Solution…
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