turning big data into smart data with graph technologies
DESCRIPTION
join Objectivity, Inc.’s, Nick Quinn in a discussion of the latest trends in Big Data Analytics, defining what “Big Data” is and understanding how to maximize your existing architectures by utilizing NOSQL technologies to improve functionality and provide real-time results. How Graph Database technology, like InfiniteGraph, can support complex relationship analytics problems. How to turn your Big Data into Smart Data. How to develop applications with significant time-to-market advantages and technical cost savings.TRANSCRIPT
Objectivity OverviewThe Company, Customers and Products
Nick QuinnFor Estuate Inc.
August 2013
Objectivity Inc.
• Objectivity, Inc. is headquartered in Sunnyvale, CA.
• Objectivity has over two decades of Big Data and NoSQL experience
• We develop NoSQL platforms for managing and discovering relationships and patterns in complex data:
– Objectivity/DB - an object database that manages localized, centralized or distributed databases
– InfiniteGraph - a massively scalable graph database built on Objectivity/DB that enables organizations to find, store and exploit the relationships in their data
– GraphMyLife – an App that uses InfiniteGraph in the Cloud to allow users to combine multiple social networks and search, discover and share information
Millions of deployments - Our technology is embedded in hundreds of enterprise and government systems and commercial products
Copyright © Objectivity, Inc. 2013
Not Only SQL – A group of 4 primary technologies
Simple Highly Interconnected
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Applications for Graph Analytics
LOGISTICS HEALTHCARE INFORMATICS
MARKET ANALYSIS SOCIAL NETWORK ANALYSIS
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Graph (Relationship) Analytics...A SQL ShortcomingThink about the SQL query for finding all links between the two “blue” rows... it's hard!!
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.
Copyright © Objectivity, Inc. 2013
...Graph Analytics
InfiniteGraph - The solution can be found with a few lines of code
A SQL Shortcoming
A3 G4
Table_A Table_B Table_C Table_D Table_E Table_F Table_G
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Representing the Graph...
Combatant A
Civilian Q
Situation Y
Civilian P
Bank X
Civilian S
Civilian R
Events/Places People/Orgs Facts
Situation X
The existing intelligence data might look like this:
Target T
Cafe C S Seen Near TA Banks at X
A Called P
A Seen At Y
A Seen Near X P Emailed S
P Called Q Q Seen Near T
P Called R R Seen Near T
X Paid S
A Eats At
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VERTEX EDGE2 N
...Representing the Graph..
“Nodes” “Connections”
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Representing the Graph...
Combatant A
Civilian Q
Situation Y
Civilian P
Civilian S
Civilian R
Events/Places People/Orgs Facts
Situation X
Target T
We start by identifying the nodes (Vertices) and the connections (Edges)
NODES CONNECTIONS
S Seen Near TA Banks at X
A Called P
A Seen At Y
A Seen Near X P Emailed S
P Called Q Q Seen Near T
P Called R R Seen Near T
X Paid SBank X
Cafe C
A Eats At
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...Representing the Graph..
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
Eats At
Cafe C
VERTEX EDGE“Nodes” “Connections”
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...Analyzing the Graph...
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
Eats At
Cafe C
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...Analyzing the Graph...
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
Eats At
Cafe C
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...Threat Analysis
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
SUSPECTS
NEEDS PROTECTION
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Graph Databases Can Connect The Dots
DATABASE(S)
GRAPH DATABASE
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Visual Analytics
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• A high performance distributed database engine that supports analyst-time decision
support and actionable intelligence
• Cost effective link analysis – flexible deployment on commodity resources (hardware
and OS).
• Efficient, scalable, risk averse technology – enterprise proven.
• High Speed parallel ingest to load graph data quickly.
• Parallel, distributed queries
• Flexible plugin architecture
• Complementary technology
• Fast proof of concept – easy to use Graph API.
InfiniteGraph - The Enterprise Graph Database
Copyright © Objectivity, Inc. 2013
InfiniteGraph Capabilities
Parallel Graph Traversal Inclusive or Exclusive Selection
X
X
Shortest or All Paths Between Objects
Start Start
Start Finish Start
Compute Cost To Date
Visualize
Computational & Visualization Plug-Ins
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Online Demo - Call Detail Record AnalysisUsed in law enforcement, counter-terrorism and Customer Resource Management
Thank You!
Please take a look at objectivity.comFor InfiniteGraph Online Demos, White Papers,
Free Downloads, Samples & Tutorials
and visit our booth for a demonstration