Introduces general graph database application, and the architecture of a scalable realtime graph database built on top of HBase.

1. GRAPH DATABASE AT A
GLANCE
Graph Intelligence

2. Agenda
• Background of graph database
• Areas of application
• Graph Intelligence’s graph database system

3. The Background
• A hyper-connected world generating huge amount of
complex, inter-connected data demands a solution to
• Store and manage complex inter-connected data
• Make sense of that data
• Evolve with the data
• “Graph analysis is possibly the single most effective
competitive differentiator for organizations pursuing data-
driven operations and decisions after the design of data
capture.” - Gartner 2014

4. What is Graph Database
• Graph Database is a software system used to persist and
process graphs, i.e. data in terms of entities and the
relationships between entities.
• Nodes: represent entities such as people, businesses, accounts,
events, policies, etc.
• Edges: the (un)directed lines connecting nodes, represent the
relationship in between.
• Properties: pertinent information that relates to nodes and edges.

5. Why Graph DB?
• Good for problem domains that have:
• Innate network structures with need for pattern matching and recursive
graph search (e.g. 1000s of objects with many-many relationships, Complex
sequences and workflows)
• (source: Neo4j)
• Changes on structure of data on a regular basis
• Native to problem domain, and hence less modeling and programming
efforts with higher productivity

6. Fraud Detection
• Source: Neo4j graph gist

7. Predictive Analysis
0 Fraudster
Prediction
0 Churn
Prediction

8. Network Analysis
• Social Network Analysis
• How viral are my marketing content?
• Who are the most influential customers?
• How does my company relate to other industry players?
• Product/Customer Community Detection
• Uncover different categorization of products
• Uncover hidden patterns of association within and cross products
groups.

9. Recommendation
• Cross-channel real-time targeted recommendations
based on each user’s latest activities (context)
• More likely to influence user behavior
• Reduced computational resources for batch processing

10. Information in Context
• Real-time Access to Cross-domain Relationships
• Augment traditional product information with dynamic feedback
from web and social media
• 360-degree view of customer across product lines, locations,
organizations and interaction channels (sales, billing, support,
mobile, web, social media, etc.)
• Intelligence, Crime
• Master data management

11. Identity and Access Management
• Leveraging real world relationships between people,
assets, roles, organizations and security policies.
• Determine authorization by tracing from individuals, through
groups, roles and products without the mismatch of traditional
hierarchical DBMS.
• Easily manage dynamic group membership and inter-relationships
• Easy to check consistency in policy updates and avoid conflict
• Real-time queries that are multi-dimensional and across-
hierarchies.
• Graph models make it easy to evolve your identity and access
management models.

12. Some Other Areas
• Bioinformatics:
• Biological DB with 111 ontologies and 50,0000+ classes/Types:
how to store and manage relationship of all these different classes/
types in RMDB?
• Workflow optimization
• Supply chain management: optimizing workflows
• Geo-Routing: Given the polluted segment S1, find all the upstream
segments within 50 miles of City1200.

13. Graph Intelligence
• Scalable graph database for real-time analytics!
• Highly scalable >>Neo4j
• Strong transactional support >OrientDB & Titan
• Optimized for real time dynamic graphs with snapshot
isolation: the only graph database that natively tracks evolution
of graph
• Schema-free modeling: SQL-compliant
• Fast traversals with native graph structure
• The only graph database innate to Hadoop ecosystem

14. System Overview

15. Thank you!
• Chen Zhang, CEO and founder of Graph Intelligence
• czhang@graphintelligence.com
• Questions?