Jeff Morris, Head of Product Marketing, Neo4j

1. 5/13/2019
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Translating the Human Analog
to Digital with Graphs
Jeff Morris
Head of Product Marketing
@JeffMMorris
jeff@Neo4j.com
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I was raised on the Space Program
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I Chase the Uniqueness of Live Music
Experiences & Their Communities
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FATHER_OF DRIVES
My Graph
LISTENS_TO
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I’m still listening to a lot of graph-y books
Adjacent Possibilities Think in Maps Connecting with PeopleJPL Innovation
Uniqueness of Individuals Practice, Practice, Practice
Food
Journey
Space
Journey
Human Senses
InnovationStartups
Agenda
• My Life in the Graph
• Human Analog Activities as Graphs
• Analog to Digital Applications
• Graph Layers
• Graph Design Thinking
• Recommendations, AI, Smart Homes and Graphs
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People, Objects, Locations, Events Represented in Graphs
CC
CC
AA AAAA
UU
SS SS SSSS SS
USER_ACCESS
CONTROLLED_BY
SUBSCRIBED _BY
User
Customers
Accounts
Subscriptions
VPVP
StaffStaff StaffStaff StaffStaffStaffStaff
DirectorDirectorStaffStaffDirectorDirector
ManagerManager ManagerManager ManagerManager ManagerManager
Fiber
Link
Fiber
Link
Fiber
Link
Fiber
Link
Fiber
Link
Fiber
Link
Ocean
Cable
Ocean
Cable
SwitchSwitch SwitchSwitch
RouterRouter RouterRouter
ServiceService
Organizational
Hierarchy
Product
Subscriptions
Global
Network
Operations
& Processes
Social
Networks
Background
• Ad-Tech supplier in NYC identifies "intent signals"
• Collects device-born consumer data from mobile,
desktops & tablets
• Contains device and buyer data on more than
90% of American households
Business Problem
• Recognize buyer receptivity to offers near time
of purchase
• Device data and consumer behaviors change
frequently
• Triangulate who is holding a device, where and
when it happens, to signal active purchase
intent, and create real-time offers to assist user
Solution and Benefits
• 3 Billion nodes, 9 billion relationships
• 1 Billion daily transactions on 3 servers
• Hybrid solution with Neo4j, Hadoop, Spark,
MongoDB and Ruby
• Breakthrough results from 60%-250%
higher than industry benchmarks
Ad Technology ADVERTISING TECHNOLOGY
Social Network, IoT and Real-Time Buyer Identification8
EE Customer since 2014 Q3
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Background
• How Neo4j is used in investigations
• Non-technical reporters manually gather data
• “Low-tech” data curation
• Journalists want to model data as a story,
not as data
• Who, What, Where, Why and How
Business Problem
• Identify repeated business relationships among
individuals and their holdings and accounts
• Scan documents and identify possible entities,
then create relationships between people and
documents.
• Names and alias variances
Solution and Benefits
• Uses Neo4j in “story discovery” phase
• Uncovers shortest paths for leads for reporters
• Many investigations
underway now
Columbia University EDUCATION
Investigative Journalism / Fraud Detection9
CE Customer since 2016 Q1EE Customer since 2015 Q4
Paradise Papers Metadata Model
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Common Thread: Density Drives Value In Graphs
Metcalfe’s Law of the Network (V=n2)
5 hops = Less Value
100’s of hops
More Value
Entity Linking
Analysis of relationships
to detect organized
crime and collusion
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Endpoint-Centric
Analysis of users and
their end-points
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Navigation Centric
Analysis of navigation
behavior and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Augment Digital Methods by Examining (Analog) Data
DISCRETE ANALYSIS CONNECTED ANALYSIS
Cross Channel
Analysis of anomaly
behavior correlated
across channels
4.
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ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
PHONE
NUMBER
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
Modeling Fraud Transactions as an Organized Ring
At first glance, each
account holder
looks normal.
Each has multiple
accounts…
ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 3
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE
NUMBER
PHONE
NUMBER
SSN 2
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
ACCOUNT
HOLDER 1
Modeling Fraud Transactions as an Organized Ring
AHA!
But they share
common phone
numbers, addresses
and SSNs!
These are difficult to
detect using
traditional
methods
CREDIT
CARD
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Background
• Brazil's largest bank, #38 on Forbes G2000
• $61B annual sales 95K employees
• Most valuable brand in Brazil
• 28.9M credit card & 25.6M debit card accounts
• High integrity, customer-centric values
Business Problem
• Data silos made assessing credit worthiness hard
• High sensitivity to fraud activity
• 73% of all transactions over internet and mobile
• Needed real-time detection for 2,000 analysts
• Scale to trillions of relationships
Solution and Benefits
• Credit monitoring and fraud detection application
• 4.2M nodes & 4B relationships for 100 analysts
• Grow to 93T relationships for 2000 analysts by 2021
• Real time visibility into money flow across multiple
customers
Itau Unibanco FINANCIAL SERVICES
Fraud Detection / Credit Monitoring15
CE Customer since 2016 Q1EE Customer since Q2 2017
Common Graph Entities are Analog
People
Locations
Processes
Devices
Objects
Motives
• Who – People
• What – Activities & Events
• Where – Locations
• When – Time
• Why – Motives & Feelings
• How – Processes, Devices &
Networks
Activities
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Humans Inputs Are Analog
Sight Sound
Touch Taste
Smell Motives
Ideas Proprioception
Balance
Feelings
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Operating In an Increasingly Digital World
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CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Latitude: 37.5629900°
Longitude: -122.3255300°
Nodes
• Can have Labels to classify nodes
• Labels have native indexes
Relationships
• Relate nodes by type and direction
Properties
• Attributes of Nodes & Relationships
• Stored as Name/Value pairs
• Can have indexes and composite indexes
• Visibility security by user/role
Property Graph Captures Analog Information
MARRIED TO
LIVES WITH
PERSON PERSON
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Analog to Digital Apps
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Ten Year Head Start
Native Connectedness Differentiates Neo4j
Conceive
Code
Compute
Run
Non-Native Graph DBNative Graph DB RDBMS
Optimized for graph workloads
The Whiteboard Model Is the Physical Model
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Ideation is an analog
activity
• Easily understood
• Easily evolved
• Easy collaboration
between business
and IT
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Cypher: Powerful and Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
Data Sources
CLIENT Admin Dashboard
Session
Data
Feedback
Scored
Recommen-
dations
Graph
Algorithms
AI / ML
Click
Stream
Data
INTELLIGENT RECOMMENDATIONS FRAMEWORK
Discovery
Exclude
Boost
Diversity
User Segmentation
Item Similarity
Intelligent Recommendations Framework
Recommendation Engines
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Product Data
Real-time
(local) inventory
Promotions
Routing and
delivery
Real-time
Recommendations
Personalization
Geo-data
Payment
options
Social Data
Available Data from Online Stores
Customer Graph
Product Graph
Supply Graph
Real-time product
recommendations
Real-time supply
chain management
Real-time risk mitigation
Region
Street
Customer
Address
Phone
Customer
Email
Email
Address
Phone
Product
Product
Category
Product
Category
Store
Street
Store
The Graph Behind Online Stores
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Business Problem
• Optimize walmart.com user experience
• Connect complex buyer and product data to
gain super-fast insight into customer needs and
product trends
• RDBMS couldn’t handle complex queries
Solution and Benefits
• Replaced complex batch process real-time online
recommendations
• Built simple, real-time recommendation system
with low-latency queries
• Serve better and faster recommendations by
combining historical and session data
Background
• Founded in 1962 and based in Arkansas
• 11,000+ stores in 27 countries with walmart.com
online store
• 2M+ employees and $470 billion in annual
revenues
Walmart RETAIL
Real-Time Recommendations27
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Graph Layers
Knowledge Graphs
30 GraphConnect speakers 2015-2017
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Background
• Fortune 100 heavy equipment manufacturer
• 27 Million warranty & service documents parsed
• Foundation for AI-based supply chain management
Business Problem
• Improve maintenance predictability
• Need a knowledge base for 27 million warranty
documents and maintenance orders
• Graphs gather context for AI to identify ‘prime
examples’ of connections among parts, suppliers,
customers and their mechanics anticipate when
equipment will need servicing and by whom.
Solution and Benefits
• Text to knowledge graph
• Common ontology for complaints, symptoms & parts
• Anticipates when equipment will need servicing
• Improves customer and brand satisfaction
• Maximizes lifespan and value of equipment
Caterpillar Heavy Equipment Manufacturing
Parts Assembly & Equipment Maintenance31
Thomson Reuters Graph
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• Data Fusion for Portfolio
Managers
• Graph layers
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Graphs Get VERY Hungry for Data
Graphs’ appetite to connect more data accelerates the ability to find adjacent
innovations
Customer iteration cycles from 2 weeks to 3 months
In-Q-Tel’s Mission Economy
• Venture Capital sponsored by
National Intelligence
• Decomposes and
reassembles technology
stacks into common
“genome” vocabulary
• Matches mission problems to
technology assemblies and
vendors
• Evaluates tech across
communications, Bio tech,
robotics, software, hardware,
IoT
• Faster evaluations, better
innovations
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Graph Design Thinking
Connect the Innovators and Their Projects
Data Scientists
Real-time
Graph traversal
Applications
Developers
& Prod Mgrs
Analysts and
Business Users
Big Data IT &
Architecture
ID, Auth & Security
Network & IT Ops Metadata Management
360⁰
Marketing Customer
360
Real-time
Cybersecurity
Chief Officers of …
Compliance, Data, Digital,
Information,Innovation, Marketing,
Operations, Risk & Security…
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Design Thinking in Neo4j Innovation Lab
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Innovation Lab Illustrated
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Graph Analytics
Graph
Algorithms
Cypher for
Apache Spark™
Graph-Enhanced
AI & ML
Similarity
ML
Neo4j Graph Platform
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Development
&
Administration
Analytics
Tooling
BUSINESS USERS
DEVELOPERS
ADMINS
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & Visualization
DATA
ANALYSTS
DATA
SCIENTISTS
Drivers & APIs
APPLICATIONS
A
I
openCypherCloud
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Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay for Google Assistant ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations41
EE Customer since 2016 Q3
Recommendations, AI
Smart Homes and
Graphs
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Highly Valuable Connected Data Use Cases
Drive Enterprise Adoption
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Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Identity & Access
Management
Knowledge
Graph
Home
Security
Internet
of things
Institutional
Memory
Entertainment
Recommendations
Home
Operations
Personalization
Voice Enabled Smart Home
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Background
• Largest Cable TV & Internet Provider in US
• 3rd Largest network on the planet
• xFi is consumer experience in 3M houses
• Internet, router, devices, security, voice & telephony
• Transformational customer experience
Business Problem
• Integrate all experience in a smart home
• Create innovative ideas based on cross-platform
and household member preferences
• Add integrated value of xFinity triple play & quad-
play services (internet, VoIP, cable TV & home
security)
Solution and Benefits
• Custom content per household member
• Security reminders (kids are home, garage left open)
• Serves millions of households
• Makes content recommendations based on
occupant, time of day, permissions and preferences
• Has Siri-like voice commands
COMCAST Xfinity xFi TELECOMMUNICATIONS
Smart Home / Internet of Things46
EE Customer since 2016
Q4
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Graphs Drive Digital Innovation
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Context Paths
Auto-Graphs
Graph Layers
1st Order Graph
Cross-Connect
Cross-tech
applications
Internet of Things
operations
Transparent Neural
Networks
Blockchain-managed
systems
Adjacent graph layers
inspire new
innovations
Metadata / Risk
Management
Knowledge Graphs
AI- Powered Customer
Experiences
Connect unlike objects
such as people to
products, locations
Mobile app explosion
Recommendation
engines
Fraud detectors
Desire for more context
to follow connections
Extract properties
during traversals
Connects like objects
People, computer
networks, telco, etc.
Networks of People Activities & Processes Objects & Knowledge
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Who, What, Where, When, How and Why
Assist Human Analog with Digital Innovations
Over Networks
On-prem & cloud
computing, Cellular,
Telco & Internet, IoT,
Blockchain
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Translating the Human Analog
to Digital with Graphs
Jeff Morris
Head of Product Marketing
@JeffMMorris
jeff@Neo4j.com
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