The Briefing Room
Connecting the Dots: How a Graph Database Enables Discovery
Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
Twitter Tag: #briefr The Briefing Room
!   Reveal the essential characteristics of enterprise software,
good and bad
!   P...
Twitter Tag: #briefr The Briefing Room
JUNE: Database
July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS
Twitter Tag: #briefr The Briefing Room
Database
SQL
NoSQL
Graph
Object
Grid
NewSQL
Cloud
Document
And lots more…
Twitter Tag: #briefr The Briefing Room
Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group	
	
robin.bloor...
Twitter Tag: #briefr The Briefing Room
!   Objectivity develops NoSQL platforms
!   Its flagship product is Objectivity/DB...
Twitter Tag: #briefr The Briefing Room
Leon Guzenda
Leon Guzenda is one of the founding members of
Objectivity and one of ...
Connecting The Dots
How A Graph Database Enables The Discovery Of Extra Value In An
Existing Enterprise Or Big Data Reposi...
Objectivity Inc.
• Objectivity, Inc. is headquartered in San Jose, CA.
• Objectivity has over two decades of Big Data and ...
A Typical Deployment – HUMInt
A Typical “Big Data” Analytics Setup
Data Aggregation and Analytics Applications
Commodity Linux Platforms and/or High Per...
Not Only SQL – A group of 4 primary technologies
Simple Highly
Interconnected
Copyright © Objectivity, Inc. 2013
Graph Analytics
Incremental Analytics Improvements Aren’t Enough
All current solutions use the same basic architectural model
•  None of t...
Graph (Relationship) Analytics...
A SQL Shortcoming
Think about the SQL query for finding all links between the two “blue”...
...Graph Analytics
InfiniteGraph - The solution can be found with a few lines of code
A SQL Shortcoming
A3 G4
Table_A Tabl...
Applications for Graph Analytics
LOGISTICS HEALTHCARE INFORMATICS
MARKET ANALYSIS SOCIAL NETWORK ANALYSIS
Copyright © Obje...
Representing the Graph...
Combatant A
Civilian Q
Situation Y
Civilian P
Bank X
Civilian S
Civilian R
Events/Places People/...
Representing the Graph...
Combatant A
Civilian Q
Situation Y
Civilian P
Civilian S
Civilian R
Events/Places People/Orgs Fa...
VERTEX EDGE
2 N
...Representing the Graph..
“Nodes” “Connections”
Copyright © Objectivity, Inc. 2013
...Representing the Graph..
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Targe...
...Analyzing the Graph...
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target ...
...Analyzing the Graph...
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target ...
...Threat Analysis
Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen ...
Graph Databases Can Connect The Dots
DATABASE(S)
GRAPH DATABASE
Copyright © Objectivity, Inc. 2013
Visual Analytics
Copyright © Objectivity, Inc. 2013
Graphs Can Scale Very Quickly
Copyright © Objectivity, Inc. 2013
We often hear about the “trillion row” database. Amazon S...
THE BIG DATA CONNECTION PLATFORM
•  A high performance distributed database engine that supports analyst-time decision
support and actionable intelligence
...
InfiniteGraph Capabilities
Parallel Graph Traversal Inclusive or Exclusive Selection
X
X
Shortest or All Paths Between Obj...
Commonly Used Graph Algorithms...
l  Connectedness
l  Node degree
l  Shortest Path
l  Average path length
l  Transiti...
Data Visualization
& Analytics
Big Data
Connection
Platform
*Now	
  	
  HP	
   *Now	
  	
  IBM	
  
Conventional & Relation...
Online Demo - Call Detail Record Analysis
Used in Law Enforcement, Counter-Terrorism and Customer Resource Management
GraphMyLife™ Demo/Overview
Thank You!
InfiniteGraph – For highly interconnected
data that has data in the connections
Please take a look at objectivi...
Twitter Tag: #briefr The Briefing Room
Perceptions & Questions
Analyst:
Robin Bloor
The Bloor Group
The Bloor Group
NoSQL Confusion
As the graph
indicates, NoSQL is a
very confusing
descriptor.
WHAT CAN A GIVEN
DATABASE AC...
The Bloor Group
Database Types
1
2
3
4
Big Table: No JOIN DBMS
Relational Database: Optimized for data stored in
sets
Docu...
The Bloor Group
Workload Types
1
2
3
4
Big Table: Select, Project
Relational Database: Select, Project, Join
Document Data...
The Bloor Group
A Database For All Seasons?
It is feasible to build an engine that handles all
these workloads, but not fe...
The Bloor Group
And In The Future?
We are beginning to
see the emergence of
the triple store
Graph databases are
suited to...
The Bloor Group
Questions
!   We tend not to think much of graph database
applications, partly because we have not had an
...
The Bloor Group
Questions
!   Data analytics is currently the motivation for a
good deal of database purchases. What kind ...
Twitter Tag: #briefr The Briefing Room
Twitter Tag: #briefr The Briefing Room
July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS
Upcoming Topics...
Twitter Tag: #briefr The Briefing Room
Thank You
for Your
Attention
Upcoming SlideShare
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Connecting the Dots—How a Graph Database Enables Discovery

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The Briefing Room with Robin Bloor and Objectivity

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Connecting the Dots—How a Graph Database Enables Discovery

  1. 1. The Briefing Room Connecting the Dots: How a Graph Database Enables Discovery
  2. 2. Twitter Tag: #briefr The Briefing Room Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.com
  3. 3. Twitter Tag: #briefr The Briefing Room !   Reveal the essential characteristics of enterprise software, good and bad !   Provide a forum for detailed analysis of today s innovative technologies !   Give vendors a chance to explain their product to savvy analysts !   Allow audience members to pose serious questions... and get answers! Mission
  4. 4. Twitter Tag: #briefr The Briefing Room JUNE: Database July: CLOUD August: HIGH PERFORMANCE ANALYTICS September: ANALYTICS
  5. 5. Twitter Tag: #briefr The Briefing Room Database SQL NoSQL Graph Object Grid NewSQL Cloud Document And lots more…
  6. 6. Twitter Tag: #briefr The Briefing Room Analyst: Robin Bloor Robin Bloor is Chief Analyst at The Bloor Group robin.bloor@bloorgroup.com
  7. 7. Twitter Tag: #briefr The Briefing Room !   Objectivity develops NoSQL platforms !   Its flagship product is Objectivity/DB, a distributed database management system !   Objectivity also offers InfiniteGraph, a graph database built on top of Objectivity/DB, and GraphMyLife, a mobile application that provides search capabilities across social networks Objectivity
  8. 8. Twitter Tag: #briefr The Briefing Room Leon Guzenda Leon Guzenda is one of the founding members of Objectivity and one of the original architects of Objectivity/DB. He currently works with Objectivity’s major customers to help them develop and deploy complex applications and systems that use Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom. He was also design and development manager for ICL’s 2900 IDMS product. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
  9. 9. Connecting The Dots How A Graph Database Enables The Discovery Of Extra Value In An Existing Enterprise Or Big Data Repository Leon Guzenda Bloor Briefing Room June 25, 2013 Ø  Current Big Data Analytics Ø  Graph Analytics Ø  InfiniteGraph Ø  The Big Data Connection Platform
  10. 10. Objectivity Inc. • Objectivity, Inc. is headquartered in San Jose, 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 – a mobile App that allows users to combine multiple social networks to search, discover and share information l  Millions of deployments - Our technology is embedded in hundreds of enterprise and government systems and commercial products Copyright © Objectivity, Inc. 2013
  11. 11. A Typical Deployment – HUMInt
  12. 12. A Typical “Big Data” Analytics Setup Data Aggregation and Analytics Applications Commodity Linux Platforms and/or High Performance Computing Clusters Structured Semi-Structured Unstructured Graph DB Object DB Doc DB K-V Store Hadoop Column Store Data W/ H RDBMS Copyright © Objectivity, Inc. 2012
  13. 13. Not Only SQL – A group of 4 primary technologies Simple Highly Interconnected Copyright © Objectivity, Inc. 2013
  14. 14. Graph Analytics
  15. 15. Incremental Analytics Improvements Aren’t Enough All current solutions use the same basic architectural model •  None of the current solutions have a 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 the data in those connections. •  Why? Because traditional and earlier NoSQL solutions are bad at handling relationships. •  Graph databases can efficiently store, manage and query the many kinds of relationships hidden in the data. Copyright © Objectivity, Inc. 2013
  16. 16. Graph (Relationship) Analytics... A SQL Shortcoming Think 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
  17. 17. ...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 Copyright © Objectivity, Inc. 2013
  18. 18. Applications for Graph Analytics LOGISTICS HEALTHCARE INFORMATICS MARKET ANALYSIS SOCIAL NETWORK ANALYSIS Copyright © Objectivity, Inc. 2013
  19. 19. 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 Copyright © Objectivity, Inc. 2013
  20. 20. 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 Copyright © Objectivity, Inc. 2013
  21. 21. VERTEX EDGE 2 N ...Representing the Graph.. “Nodes” “Connections” Copyright © Objectivity, Inc. 2013
  22. 22. ...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” Copyright © Objectivity, Inc. 2013
  23. 23. ...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 Copyright © Objectivity, Inc. 2013
  24. 24. ...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 Copyright © Objectivity, Inc. 2013
  25. 25. ...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 Copyright © Objectivity, Inc. 2013
  26. 26. Graph Databases Can Connect The Dots DATABASE(S) GRAPH DATABASE Copyright © Objectivity, Inc. 2013
  27. 27. Visual Analytics Copyright © Objectivity, Inc. 2013
  28. 28. Graphs Can Scale Very Quickly Copyright © Objectivity, Inc. 2013 We often hear about the “trillion row” database. Amazon S3 has reached 2 trillion, but one Objectivity site: • Processes 10s of trillions of objects per day • Supports over 1000 analysts around the clock. Consider a graph where each node has 10 connections: • At 6 degrees of freedom, finding a path between two nodes may require traversing a million links. • 9 degrees of freedom requires a billion traversals • 12 degrees of freedom requires a trillion traversals • 15 degrees of freedom requires a quadrillion traversals...
  29. 29. THE BIG DATA CONNECTION PLATFORM
  30. 30. •  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
  31. 31. 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 Copyright © Objectivity, Inc. 2013
  32. 32. Commonly Used Graph Algorithms... l  Connectedness l  Node degree l  Shortest Path l  Average path length l  Transitive Closure l  Graph diameter (or Span) l  Centrality (Betweeness, Degree and Closeness) l  In the graph below, node D has the highest betweeness centrality
  33. 33. Data Visualization & Analytics Big Data Connection Platform *Now    HP   *Now    IBM   Conventional & Relationship Analytics ORACLE Big Data Solutions + A Typical Deployment Supplements Traditional or Big Data Systems With Graph Analytics Copyright © Objectivity, Inc. 2013
  34. 34. Online Demo - Call Detail Record Analysis Used in Law Enforcement, Counter-Terrorism and Customer Resource Management
  35. 35. GraphMyLife™ Demo/Overview
  36. 36. Thank You! InfiniteGraph – For highly interconnected data that has data in the connections Please take a look at objectivity.com For InfiniteGraph Online Demos, White Papers, Free Downloads, Samples, Tutorials and the GraphMyLife App
  37. 37. Twitter Tag: #briefr The Briefing Room Perceptions & Questions Analyst: Robin Bloor
  38. 38. The Bloor Group
  39. 39. The Bloor Group NoSQL Confusion As the graph indicates, NoSQL is a very confusing descriptor. WHAT CAN A GIVEN DATABASE ACTUALLY DO? The important question is
  40. 40. The Bloor Group Database Types 1 2 3 4 Big Table: No JOIN DBMS Relational Database: Optimized for data stored in sets Document Database: Optimized for data stored in hierarchical structures Graph Database: Optimized for data stored in graphs or directed graphs (networks) There are 4 types of database (engine):
  41. 41. The Bloor Group Workload Types 1 2 3 4 Big Table: Select, Project Relational Database: Select, Project, Join Document Database: Search and Join Graph Database: Graph walking There are 4 types of associated workload:
  42. 42. The Bloor Group A Database For All Seasons? It is feasible to build an engine that handles all these workloads, but not feasible to have one that handles them all well This is because performance is highly dependent on how you physically store the data Different workloads mean different physical data storage Currently, the unexploited engines are the graph database and the document database
  43. 43. The Bloor Group And In The Future? We are beginning to see the emergence of the triple store Graph databases are suited to being triple stores We do not yet know if a new database type will emerge
  44. 44. The Bloor Group Questions !   We tend not to think much of graph database applications, partly because we have not had an easy way to search graphs of data. !   In my view we have reached a situation where there will always be multiple “data engines.” Is that Objectivity’s view? !   While we have focused on Objectivity as a Graph Database, what other workloads can it be applied to? !   Which sectors/businesses are currently in Objectivity’s “sweet spot”?
  45. 45. The Bloor Group Questions !   Data analytics is currently the motivation for a good deal of database purchases. What kind of data analytics does one carry out on a graph database? !   Have you encountered significant interest in triple stores and semantic searches? !   Which companies/products do you regard as competitors/partners?
  46. 46. Twitter Tag: #briefr The Briefing Room
  47. 47. Twitter Tag: #briefr The Briefing Room July: CLOUD August: HIGH PERFORMANCE ANALYTICS September: ANALYTICS Upcoming Topics www.insideanalysis.com
  48. 48. Twitter Tag: #briefr The Briefing Room Thank You for Your Attention
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