Using A Distributed Graph Database To Make Sense Of Disparate Data Stores

Copyright © Objectivity, Inc. 2013
Using A Distributed Graph Database To Make Sense Of Disparate Data
Stores
Leon Guzenda
Dataweek
San Francisco – October 2, 2013
 Current Big Data Analytics
 Graph Analytics
 InfiniteGraph
 The ETL & Discovery Process

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
 Millions of deployments - Our technology is embedded in hundreds of enterprise
and government systems and commercial products

A Typical Objectivity Deployment - Sensor Data Fusion
Network Centric Collaborative Targeting

A Typical InfiniteGraph Deployment - GraphMyLife

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 StoreHadoop
Column
Store
Data W/HRDBMS

Incremental Analytics Improvements Aren’t Enough
All current solutions use the same basic architectural model
• None of the popular solutions have 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 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.

Graph Analytics

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.

...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

Applications for Graph Analytics
LOGISTICS
HEALTHCARE INFORMATICS
MARKET ANALYSIS SOCIAL NETWORK ANALYSIS

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 COMINT and HUMINT 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

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

VERTEX EDGE
2 N
...Representing the Graph..
“Nodes” “Connections”

...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”

...Analyzing the Graph...
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

...Threat Analysis
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

Visual Analytics

Graphs Can Scale Very Quickly
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...

THE ETL & DISCOVERY PROCESS

Not Only SQL – A group of 4 primary technologies
Simple Highly
Interconnected

• 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

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

A Powerful InfiniteGraph Query
San Francisco
Palo Alto
Hillsboro
Oakland
Pacifica
Palo Alto Cupertino
San Jose
Half Moon Bay
Problem: Find the cheapest route for moving a 200 ton load from San Francisco to San Jose
// Policies: Depth_First, Exclude Railway_Edge, Exclude_Road_Edge
// Calculate: Cost_To_This_City()
// Navigate: From “San Francisco” To “San Jose”
// Visualizer: Map_Cheapest_Route
// Visualizer: List_Cost_Breakdown.
Water
Rail
Road
Problem: Find the cheapest route for moving a 200 ton load from San Francisco to San Jose
// Note: This is pseudocode, not the actual Java statements.

Recognizing Graphs In Object Models...
Tree Structures
Graph (Network) Structures
Relationship Data
Object Class A
Object Class A
1-to-Many Relationship
Data
Object Class A
Many-to-Many
Object Class A

...Recognizing Graphs In Object Models
Tree Structures
Graph (Network) Structures
Relationship Data
Object Class A
Object Class A
1-to-Many Relationship
Data
Object Class A
Many-to-Many
Object Class A
EDGE
VERTEX
GRAPH MODEL

The ETL Process
ETL Tools/Applications
Commodity Linux Platforms and/or High Performance Computing Clusters
Structured Semi-Structured
Object
DB
Graph
DB
Unstructured
Doc DB K-V StoreHadoop
Column
Store
Data W/HRDBMS
Nodes & Edges

Commonly Used Graph Algorithms...
 Connectedness
 Node degree
 Shortest Path
 Average path length
 Transitive Closure
 Graph diameter (or Span)
 Centrality (Betweeness, Degree and Closeness)
In the graph below, node D has the highest betweeness centrality

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

Online Demo - Call Detail Record Analysis
Used in law enforcement, counter-terrorism and Customer Resource Management

Thank You!
Please take a look at objectivity.com
For InfiniteGraph Online Demos, White Papers, Free
Downloads, Samples & Tutorials
and visit our booth for a demonstration

Using A Distributed Graph Database To Make Sense Of Disparate Data Stores

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Using A Distributed Graph Database To Make Sense Of Disparate Data Stores

Semelhante a Using A Distributed Graph Database To Make Sense Of Disparate Data Stores (20)

Mais de InfiniteGraph

Mais de InfiniteGraph (20)

Último

Último (20)

Using A Distributed Graph Database To Make Sense Of Disparate Data Stores