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Realize The Value In Your Big Data
              With Graph Technology
www.Objectivity.com




                              Leon Guzenda - Objectivity, Inc.
                                 DBTA Webinar – January 17, 2013
Overview


•   Who We Are

•   Current Big Data Analytics

•   Relationship Analytics

•   Graph Technologies

•   The Big Data Connection Platform
About Objectivity Inc.
     • Objectivity, Inc. is headquartered in Sunnyvale, California.
     • Established in 1988 to tackle database problems that network/hierarchical/relational
      and file-based technologies struggle with.
     • Objectivity has over two decades of Big Data and NoSQL experience

     • Develops 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



     Embedded in hundreds of enterprises, government organizations and products -
     millions of deployments.
4
9

2
     Human Intelligence (HUMINT) Analysis
8

1
1




    9/28/11                   4
Big Data Technologies Are Still Evolving
We All Know The Problem - Information Overload!


   Volume, Velocity, Variety, Veracity, Value...

   Making sense of it all takes time and $$$



               Current “Big Data” Analytics
A Typical “Big Data” Analytics Setup

                       Data Aggregation and Analytics Applications


          Commodity Linux Platforms and/or High Performance Computing Clusters




           Column     Data          Graph      Object                                   K-V
 RDBMS                                                         Hadoop      Doc DB
            Store     W/H            DB         DB                                     Store


         Structured                 Semi-Structured                     Unstructured
Incremental 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.
Not Only SQL – a group of 4 primary technologies

• Key-Value Stores

• “Big Table” Clones

• Document Databases

• Object and Graph databases




                                       Graph Database



                                       Graph Processing
Not Only SQL – A group of 4 primary technologies




                                           Highly
    Simple                                 Interconnected
Graph Theory 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

    WEIGHT: A quantity associated with a particular Edge

    GRAPH: A collection of linked Vertex and Edge objects



         Vertex 1                      Edge 1                     Vertex 2

      City: San Francisco        Road: I-101                 City: San Jose
      Pop: 812,826               Miles: 47.8                 Pop: 967,487
...Graph Theory Terminology...

   SIMPLE/UNDIRECTED GRAPH: A Graph where each VERTEX may be linked to
    one or more Vertex objects via Edge objects and each Edge object is connected to
    exactly two Vertex objects. Furthermore, neither Vertex connected to an Edge is more
    significant than the other.


   DIRECTED GRAPH: A Simple/Undirected Graph where one Vertex in a
    Vertex + Edge + Vertex group (an “Arc” or “Path”) can be considered the “Head” of the
    Path and the other can be considered the “Tail”.


   MIXED GRAPH: A Graph in which some paths are Undirected and others are
    Directed.
...Graph Theory Terminology
   LOOP: An Edge that is doubly-linked to the same Vertex

   MULTIGRAPH: A Graph that allows multiple Edges and Loops

   QUIVER: A Graph where Vertices are allowed to be connected by multiple Arcs.
    A Quiver may include Loops.

   WEIGHTED GRAPH: A Graph where a quantity is assigned to an Edge, e.g.
    a Length assigned to an Edge representing a road between two Vertices representing
    cities.

   HALF EDGE: An Edge that is only connected to a single Vertex

   LOOSE EDGE: An Edge that isn't connected to any Vertices.

   CONNECTIVITY: Two Vertices are Connected if it is possible to find a path between
    them.
Relationship Analytics
Example 1 – Social Network Analysis


 Sources may be covert or open

  Telecom Call Detail Records
  Banking transactions

  Flight and hotel reservations

  MASINT



  Twitter
  Facebook

  Google+

  LinkedIn

  Plaxo

  Flickr

  Youtube
Example 2 – Finding Patterns In Open Source Data...

The Challenges
   Data Volumes

   Fast-Changing Data

   Sensitivity of Data

   Significance of Data
...Example 2 – Finding Patterns In Open Source Data
Example 3 – Logistics
Example 4 - Cyber Security...
… Example 4 - Cyber Security
Link Hunter - POC For A Federal Police Force




    Run the live demo at objectivity.com [Resources, Live Demos]
MAKING GRAPH ANALYTICS WORK EFFICIENTLY
Relationship (Connection) 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.
Relationship (Connection) 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                                                                             G4
Representing the Graph...
The existing data might look like this:


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                           R Seen Near T
                                          P Called R

   Cafe C           Civilian Q            A Banks at X    S Seen Near T



                    Civilian R                            A Seen At Y


                                                            A Eats At
                    Civilian S
Representing the Graph...
We start by identifying the nodes (Vertices) and the connections (Edges)

          NODES                                     CONNECTIONS
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                         R Seen Near T
                                     P Called R

   Cafe C          Civilian Q        A Banks at X      S Seen Near T



                   Civilian R                          A Seen At Y


                                                         A Eats At
                   Civilian S
...Representing the Graph..
                          2   N
       “Nodes”   VERTEX           EDGE   “Connections”
...Representing the Graph..
         “Nodes”      VERTEX                 EDGE        “Connections”


Situation X     Seen Near      Combatant A     Seen At          Situation Y


                   Eats At       Called        Banks At


    Cafe C                     Civilian P                    Bank X


                     Called      Called       Emailed            Paid

   Civilian Q                  Civilian R                  Civilian S


                   Seen Near   Seen Near      Seen Near

                                Target T
...Analyzing the Graph...


Situation X     Seen Near   Combatant A    Seen At         Situation Y


                              Called       Banks At
                 Eats At

    Cafe C                  Civilian P                  Bank X


                   Called     Called      Emailed           Paid

   Civilian Q               Civilian R                Civilian S


                Seen Near   Seen Near     Seen Near

                             Target T
...Threat Analysis


Situation X     Seen Near   Combatant A    Seen At         Situation Y


                              Called       Banks At
SUSPECTS

                            Civilian P                  Bank X


                   Called     Called      Emailed           Paid

   Civilian Q               Civilian R                Civilian S


                Seen Near   Seen Near     Seen Near

                             Target T          NEEDS PROTECTION
Visual Analytics
Recognizing Graphs In Object Models...
  Tree Structures

                                       1-to-Many                     Relationship
                                                                        Data



                                     Object Class A               Object Class A



   Graph (Network) Structures

                         Many-to-Many                 Relationship Data




                        Object Class A                Object Class A



Copyright © Objectivity, Inc. 2012
Graph Processing Technologies and APIs


• Distributed Graph Processing

     •      Angrapa, Apache Hama, Faunus, Giraph, GoldenOrb, HipG, InfiniteGraph,
            Jpregel, KDT, OpenLink Virtuoso, Phoebus, Pregel, Sedge, Scala
            Signel/Collect, Trinity, Parallel Boost Graph Library (PGBL)...

   APIs and Graph Programming/Query Languages

     •      Blueprints, Bulbflow, Cypher, Gremlin, Pacer, Pipes, PYBlueprints, Pygr,
            Rexster, SPARQL, SPASQL, Styx...

   Graph Data Interchange Formats

     •      DGML, Dot Language, GraphML, GML, GXL, XGMML, Trivial Graph
            Format...
Graph Database Technologies


• In Memory, e.g. YarcData, Apache Hama...

• RDF stores – Allegrograph, BigData, OpenLink Virtuoso, R2DF...

• Document relationships – ArangoDB, OrientDB...

• Single server or embedded graph DBMSs – DEX, Filament, Graphbase,
  HypergraphDB, Neo4J, VertexDB...

• Layers over existing DBMSs – Horton, Infogrid, OQGraph...

• Distributed Graph DBMSs – InfiniteGraph, Titan...
Graph Databases Post-2003




                       X
Graph Databases Compared [UNSW]

        SUPPORT FOR ESSENTIAL GRAPH QUERIES
THE BIG DATA CONNECTION PLATFORM
Conventional & Graph Analytics



Data Visualization
   & Analytics
                     *Now HP   *Now IBM




     Big Data             ORACLE or
    Connection
     Platform             Other Big Data
                          Solutions        +
InfiniteGraph - The Enterprise Graph Database

• 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.
Basic Capabilities Of Most Graph Databases

           Rapid Graph Traversal                            Inclusive or Exclusive
                                                            Selection


                                                                               X
Start                                               Start




                                                                 X

                             Find the Shortest or All Paths Between Objects




                     Start                                            Finish
InfiniteGraph 3.0

          PARALLEL LOAD & SEARCH




Start




                                       Computational & Visualization Plugins

                                                                 Total Path Latency



                               Start




                                                                         Display Fastest Path
Summary - Graph Analytics

• Can Be Used For:
   – Social Network Analysis
   – Pattern finding in open source data
   – Logistics
   – Campaign planning
   – Energy usage, planning and protection
• The technology works best if the graph is extracted from existing
 sources and stored in a Graph Database.
Thank You!

  Please take a look at objectivity.com
For InfiniteGraph Online Demos, White Papers, Free
           Downloads, Samples & Tutorials



              info@objectivity.com

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Dbta Webinar Realize Value of Big Data with graph 011713

  • 1. Realize The Value In Your Big Data With Graph Technology www.Objectivity.com Leon Guzenda - Objectivity, Inc. DBTA Webinar – January 17, 2013
  • 2. Overview • Who We Are • Current Big Data Analytics • Relationship Analytics • Graph Technologies • The Big Data Connection Platform
  • 3. About Objectivity Inc. • Objectivity, Inc. is headquartered in Sunnyvale, California. • Established in 1988 to tackle database problems that network/hierarchical/relational and file-based technologies struggle with. • Objectivity has over two decades of Big Data and NoSQL experience • Develops 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 Embedded in hundreds of enterprises, government organizations and products - millions of deployments.
  • 4. 4 9 2 Human Intelligence (HUMINT) Analysis 8 1 1 9/28/11 4
  • 5. Big Data Technologies Are Still Evolving
  • 6. We All Know The Problem - Information Overload!  Volume, Velocity, Variety, Veracity, Value...  Making sense of it all takes time and $$$ Current “Big Data” Analytics
  • 7. A Typical “Big Data” Analytics Setup Data Aggregation and Analytics Applications Commodity Linux Platforms and/or High Performance Computing Clusters Column Data Graph Object K-V RDBMS Hadoop Doc DB Store W/H DB DB Store Structured Semi-Structured Unstructured
  • 8. Incremental 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.
  • 9. Not Only SQL – a group of 4 primary technologies • Key-Value Stores • “Big Table” Clones • Document Databases • Object and Graph databases Graph Database Graph Processing
  • 10. Not Only SQL – A group of 4 primary technologies Highly Simple Interconnected
  • 11. Graph Theory 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  WEIGHT: A quantity associated with a particular Edge  GRAPH: A collection of linked Vertex and Edge objects Vertex 1 Edge 1 Vertex 2 City: San Francisco Road: I-101 City: San Jose Pop: 812,826 Miles: 47.8 Pop: 967,487
  • 12. ...Graph Theory Terminology...  SIMPLE/UNDIRECTED GRAPH: A Graph where each VERTEX may be linked to one or more Vertex objects via Edge objects and each Edge object is connected to exactly two Vertex objects. Furthermore, neither Vertex connected to an Edge is more significant than the other.  DIRECTED GRAPH: A Simple/Undirected Graph where one Vertex in a Vertex + Edge + Vertex group (an “Arc” or “Path”) can be considered the “Head” of the Path and the other can be considered the “Tail”.  MIXED GRAPH: A Graph in which some paths are Undirected and others are Directed.
  • 13. ...Graph Theory Terminology  LOOP: An Edge that is doubly-linked to the same Vertex  MULTIGRAPH: A Graph that allows multiple Edges and Loops  QUIVER: A Graph where Vertices are allowed to be connected by multiple Arcs. A Quiver may include Loops.  WEIGHTED GRAPH: A Graph where a quantity is assigned to an Edge, e.g. a Length assigned to an Edge representing a road between two Vertices representing cities.  HALF EDGE: An Edge that is only connected to a single Vertex  LOOSE EDGE: An Edge that isn't connected to any Vertices.  CONNECTIVITY: Two Vertices are Connected if it is possible to find a path between them.
  • 15. Example 1 – Social Network Analysis Sources may be covert or open  Telecom Call Detail Records  Banking transactions  Flight and hotel reservations  MASINT  Twitter  Facebook  Google+  LinkedIn  Plaxo  Flickr  Youtube
  • 16. Example 2 – Finding Patterns In Open Source Data... The Challenges  Data Volumes  Fast-Changing Data  Sensitivity of Data  Significance of Data
  • 17. ...Example 2 – Finding Patterns In Open Source Data
  • 18. Example 3 – Logistics
  • 19. Example 4 - Cyber Security...
  • 20. … Example 4 - Cyber Security
  • 21. Link Hunter - POC For A Federal Police Force Run the live demo at objectivity.com [Resources, Live Demos]
  • 22. MAKING GRAPH ANALYTICS WORK EFFICIENTLY
  • 23. Relationship (Connection) 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.
  • 24. Relationship (Connection) 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 G4
  • 25. Representing the Graph... The existing data might look like this: 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 R Seen Near T P Called R Cafe C Civilian Q A Banks at X S Seen Near T Civilian R A Seen At Y A Eats At Civilian S
  • 26. Representing the Graph... We start by identifying the nodes (Vertices) and the connections (Edges) NODES CONNECTIONS 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 R Seen Near T P Called R Cafe C Civilian Q A Banks at X S Seen Near T Civilian R A Seen At Y A Eats At Civilian S
  • 27. ...Representing the Graph.. 2 N “Nodes” VERTEX EDGE “Connections”
  • 28. ...Representing the Graph.. “Nodes” VERTEX EDGE “Connections” Situation X Seen Near Combatant A Seen At Situation Y Eats At Called Banks At Cafe C Civilian P Bank X Called Called Emailed Paid Civilian Q Civilian R Civilian S Seen Near Seen Near Seen Near Target T
  • 29. ...Analyzing the Graph... Situation X Seen Near Combatant A Seen At Situation Y Called Banks At Eats At Cafe C Civilian P Bank X Called Called Emailed Paid Civilian Q Civilian R Civilian S Seen Near Seen Near Seen Near Target T
  • 30. ...Threat Analysis Situation X Seen Near Combatant A Seen At Situation Y Called Banks At SUSPECTS Civilian P Bank X Called Called Emailed Paid Civilian Q Civilian R Civilian S Seen Near Seen Near Seen Near Target T NEEDS PROTECTION
  • 32. Recognizing Graphs In Object Models... Tree Structures 1-to-Many Relationship Data Object Class A Object Class A Graph (Network) Structures Many-to-Many Relationship Data Object Class A Object Class A Copyright © Objectivity, Inc. 2012
  • 33. Graph Processing Technologies and APIs • Distributed Graph Processing • Angrapa, Apache Hama, Faunus, Giraph, GoldenOrb, HipG, InfiniteGraph, Jpregel, KDT, OpenLink Virtuoso, Phoebus, Pregel, Sedge, Scala Signel/Collect, Trinity, Parallel Boost Graph Library (PGBL)...  APIs and Graph Programming/Query Languages • Blueprints, Bulbflow, Cypher, Gremlin, Pacer, Pipes, PYBlueprints, Pygr, Rexster, SPARQL, SPASQL, Styx...  Graph Data Interchange Formats • DGML, Dot Language, GraphML, GML, GXL, XGMML, Trivial Graph Format...
  • 34. Graph Database Technologies • In Memory, e.g. YarcData, Apache Hama... • RDF stores – Allegrograph, BigData, OpenLink Virtuoso, R2DF... • Document relationships – ArangoDB, OrientDB... • Single server or embedded graph DBMSs – DEX, Filament, Graphbase, HypergraphDB, Neo4J, VertexDB... • Layers over existing DBMSs – Horton, Infogrid, OQGraph... • Distributed Graph DBMSs – InfiniteGraph, Titan...
  • 36. Graph Databases Compared [UNSW] SUPPORT FOR ESSENTIAL GRAPH QUERIES
  • 37. THE BIG DATA CONNECTION PLATFORM
  • 38. Conventional & Graph Analytics Data Visualization & Analytics *Now HP *Now IBM Big Data ORACLE or Connection Platform Other Big Data Solutions +
  • 39. InfiniteGraph - The Enterprise Graph Database • 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.
  • 40. Basic Capabilities Of Most Graph Databases Rapid Graph Traversal Inclusive or Exclusive Selection X Start Start X Find the Shortest or All Paths Between Objects Start Finish
  • 41. InfiniteGraph 3.0 PARALLEL LOAD & SEARCH Start Computational & Visualization Plugins Total Path Latency Start Display Fastest Path
  • 42. Summary - Graph Analytics • Can Be Used For: – Social Network Analysis – Pattern finding in open source data – Logistics – Campaign planning – Energy usage, planning and protection • The technology works best if the graph is extracted from existing sources and stored in a Graph Database.
  • 43. Thank You! Please take a look at objectivity.com For InfiniteGraph Online Demos, White Papers, Free Downloads, Samples & Tutorials info@objectivity.com