Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".

1. AnzoGraph.com
Driving AI and Machine Insights with
Knowledge Graphs in a Connected
World
Thomas Cook
Sales Director, AnzoGraph DB

2. Data Continues to Grow
AI and ML Demand Increases
Complexity of Data Ecosystem Grows
• Need to Build on Existing Analytics Capabilities with:
• Automated Data Preparation & Better Understanding
• Explainable AI & ML with Provenance
• Improved Algorithms & Analytics
• Cost Efficient Operations
Context
Knowledge
Graphs
&
Graph
Analytics

3. The Data Preparation Problem
Data Access
● Manual ETL coding
● Practicalities limit the # of
sources and types of data
Data Processing
● Laborious discovery, profiling
and selection
● Use of rules and coding for
harmonization & cleansing
Feature Engineering
● Manual coding to transform
data
● Manual feature engineering
& selection
1 Cleaning Big Data, Forbes Magazine
70-80% of time spent in Data Preparation & Feature Engineering
3
Viewed as the “least enjoyable” part of work by 76% of data scientists1
Structured Data

4. Traditional approaches to connecting siloed data
Rigid data model
Relationships more difficult
Expensive
Does not adapt well to change
Concurrency & Performance
Raw operational data dumps become
unwieldly, difficult to consume and manage
Referred to as the “Data Swamp”
Data engineering efforts are costly,
complex, lack lineage, often times not
repeatable
Heavy volume Spark clusters are
difficult to manage and tune properly
Data Warehouse
Data Lake

5. “Graph analytics will grow in the next few years
due to the need to ask complex questions across
complex data, which is not always practical or
even possible at scale using SQL queries”
…Gartner – Top 10 Data and Analytics Technology Trends for 2019

6. Knowledge Graphs to Automate
Data Preparation & Improve Common Understanding

7. gg
Why Graph?
Graph’s Flexible Data
Model
Rich insights on
relationships, not just
entities
Leveraging Industry
Data Models
Process & analyze growing amounts of diverse data
Structured and
Unstructured Data
Natural Language
Sparse Data
Data you know you need to
analyze
Data you don’t know you need to
analyze
AI and ML
Traditional Analytics
Unique and Insightful
Analytics
Feature Engineering
Can evolve as data
changes
Refactoring often not
necessary as
data/needs evolve

8. What is a Knowledge Graph?
Data Architect View
One method to integrate data
from multiple data sets,
structured or unstructured, and
to leverage standard industry
ontologies to enhance analytics.
Executive View
Common understanding of all
disparate data.
Ontologist View
The best way to represent knowledge
and meaning and provide linkage and
relationship information in a data
analytics platform. Ontologies are at the
center providing a way to standardize
and enhance the conceptual model.
Inferencing provides semantic reasoning
for better understanding.

9. ©2018 Cambridge Semantics Inc. All rights reserved.
Canonical data model
provides context for
common understanding
Easy to find and access the
right data
Automate complex data
preparation tasks
Perform deep link analysis
of complex relationships
for improved insights

10. Using Knowledge Graphs with
Graph Analytics Database as
Scalable Infrastructure for ML & Data Science

11. Analytical Capabilities - AnzoGraph DB
Negation
Property Paths
BIND
Aggregates
Basic Federated Query
ORDER BY and offsets
Functions on Strings
Functions on Numerics
Functions on Dates and
Times
Hash Functions
Basic Graph Patterns
Count/Avg
Min/Max
GroupConcat
Sample
Algorithms like:
• Page Rank
• Shortest Path
• All Path
• Label Propagation
• Weakly Connected
Components
• K neighborhood
• Counting Triangles
Inferencing
(RDFS+ OWL 2 DL)
OpenCypher (preview)
Labeled Property
Graphs (RDF*)
OLAP Scalability:
• Window Aggregates
• Advanced Grouping
Sets
• Named Views
• Named Queries
• Conditional
Expressions
Database via
SPARQL
AnzoGraph® DB
Extras
Graph Database
and Inferencing
Data Science
Extensions
UDX
Distributions
● Bernoulli
● Binomial
● Chi-squared
● Exponential
● Hypergeometric
● Laplace
● Log Normal
● Logarithmic Series
● Negative Binomial
● Normal
Correlations
● Pearson
Entropy
● Cross Entropy
● Differential Entropy
Design your own

12. Over 150 Geospatial Functions
AnzoGraph.com
Define Regions
• Points, polygon,
circles
Use Common
geospatial shape data
files
• Shp, Geojson, KML,
WKT, WKB
Understand
Relationships
• Equals, Intersects,
Overlaps, Disjoint,
Within, Touches
Convert Coordinate
Systems
• Cartesian, Spherical,
Cylindrical, Elliptical

13. Virtualization

14. AnzoGraph Benchmark Results
217 X
AnzoGraph DB when compared to Neo4j
on and industry standard
TPC-H benchmark
113 X
AnzoGraph’s LUBM benchmark
performance over previous fastest result
10-300X
AnzoGraph’s performance on graph
algorithms over SPARK SQL and SPARK
with GraphFrames
Analytical Benchmarks

15. Knowledge Graphs Use Cases

16. Traditional & Graph Analytics
Schema-less Data Model
Standards
Customizable Algorithms
Open Platform
Use Cases
AnzoGraph DB
• Data Harmonization & Analytics
• Enterprise Knowledge Graphs
• Scientific Data Discovery
• Customer 360
• Supply Chain
• IoT
• Fraud Detection
• Financial Research
• Network Optimization
• Anti-Money Laundering

17. Parabole and AnzoGraph Cognitive Analytics - alphaESG
• Extract text and
relationships from massive
amounts documents and
news feed
• Use AnzoGraph DB to
• Create cognitive models
• Contextualize news,
filings & reports
• Harmonize data from
SASB and various data
sources
• Provide customized
outputs and signals
• Execute analytics

18. Download AnzoGraph DB Free Edition Today http://AnzoGraph.com

19. AnzoGraph.com
Thank you

20. AnzoGraph.com
Extra Slides

21. ©2019 Cambridge Semantics Inc. All rights reserved.
Execute Supervised & Unsupervised ML with Graph Algorithms
Graph Algorithm
• PageRank
• Shortest Path
• K-neighbors
• All Paths
• Counting Triangles
• Weakly Connected
Components
• Label Propagation
• Triangle Enumeration
• Triangle Counting
• Clustering Coefficient
and more!
Who is the most influential person in your
customer list?
What’s the most important item relating to a
search of your knowledge graph?
What is the shortest path to your destination
across a route?
What’s the optimal path for packets to travel
across your network
source: Wikipedia
Try functions via SPARQL in Zeppelin or python Jupyter Notebooks

22. ©2019 Cambridge Semantics Inc. All rights reserved.
Graph Algorithms produce additional Features to train ML Models
Graph Algorithms
source: Wikipedia
Try functions via SPARQL in Zeppelin or python Jupyter Notebooks

23. What it is:
● Fast, Scalable Graph Database
○ In-Memory Massively Parallel Processing
(MPP) ACID-Compliant Graph Database
○ Supports RDF & Labelled Property Graphs
What it does:
○ Fast Data Loading
○ Fast Query
○ Rich Analytics
■ Graph Algorithms
■ BI/DW Analytics
■ Inferencing
■ Data Science/Feature Engineering
Algorithms
■ Define-Your-Own Analytics
○ Linear Database Scaling
○ Persist data on cheap storage
Based on Open Standards
• Built on RDF & SPARQL 1.1 standards
• LPG with the RDF* /SPARQL*
• LPG with Cypher (in 2020)
Deploy on-prem or cloud
• Kubernetes/Helm on-demand cloud
deployment
• AWS, Google and Azure
AnzoGraph™ DB
Awards
Select Customers

24. ©2019 Cambridge Semantics Inc. All rights reserved.
Graph OLAP Built for Analytics at Scale and Speed
SQL OLAP vs Graph OLAP
SQL OLAP Graph OLAP
On-line Analytics at Massive Parallel
Processing (MPP) Scale with SQL Database
Example
Netezza
Amazon Redshift
Analytics
• Warehouse-Style BI Analytics
On-line Analytics at Massive Parallel Processing (MPP)
Scale with Native Graph Database
Example
AnzoGraph DB
Analytics
• Warehouse-Style BI Analytics
• Graph Algorithms
• Inferencing
• Data Science Functions

25. ©2019 Cambridge Semantics Inc. All rights reserved.
Graph OLAP Built for Analytics at Scale and Speed
Graph OLTP vs Graph OLAP
Graph OLTP Graph OLAP
Transactional databases
• Built for building transactional
applications & individual
transactions
• Scales vertically
Example
Neo4j
AWS Neptune
Analytical databases
• Built for analytics and to deal with scale &
performance
• Deep Link analysis
• Analytics on the population
• Scales horizontally
• Can complement Graph OLTP systems
Example
AnzoGraph DB

26. Page
Labelled Property Graphs facilitates Analytics
isA: <Man>
birthday: 09/17/1975
isA: <Woman>
Birthday: 4/23/1979
isA: <Place>
has: Water
has: Trees
partOf: <TheMountain>
Person
: Jill
Person
: Jack
Place:
The
Hill
friendOf
WentUp
WentUp
metAt=<TheHill>
metDate=07/04/2018
Date=07/04/2018
Date=07/04/2018
Today with RDF* and SPARQL*
• Relationships can be described as
clearly as any LPG database
RDF*/SPARQL* extensions to the
standard make W3C open standards
databases even more capable

27. Page
User-defined Extensions (UDXs):
Allows users to extend AnzoGraph DB functionality for custom usage
User-Defined
Functions
(UDF)
Create and register custom analytic functions, such as functions that
concatenate values or convert integers to alternate currencies.
User-Defined
Aggregates
(UDA)
Create and register aggregate functions, such as functions that
compute the arithmetic mean or calculate the average number from
a list of maximum and minimum values.
User-Defined
Services
(UDS)
Create and register services that create local SPARQL endpoints.
User-Defined
Tables (UDT)
Create and register a function that is repeatedly invoked within a
query to generate the rows of a table on-the-fly.
Data
Science
Functions
User-
defined
Functions
(UDX)
Functions you can build in JAVA or C++

28. ©2019 Cambridge Semantics Inc. All rights reserved.
Execute Inferencing using RDFS+ and OWL 2 RL
Person:
Jack
Person:
Jill
Is Married
Inference
Is Married
Person:
Jack
Person:
Sam
Knows
Inference
Knows
AnzoGraph allows you to insert inferred triples into
the specified target graph
If Jack is married to Jill, then you can
definitely infer that Jill is married to Jack
Jack knows Sam, but Sam may not know Jack.
Here, the inference is less clear
Both cases are supported.
Try functions via SPARQL in Zeppelin or python Jupyter Notebooks

29. ©2019 Cambridge Semantics Inc. All rights reserved.
ELT for Data Engineering
•Wrangling, Blending, Munging, Transformations, Enrichment, Views
•Use statistical functions, transformations or enrichment to get the data
into the form needed for the downstream ML pipeline
INSERT {
graph <myNewGraph> {
?s a <Person>;
<fullname> ?fullname
}
}
USING <myOldGraph>
WHERE {
?s a <Person>;
<firstname> ?fname;
<lastname> ?lname;
BIND(CONCAT(?fname, “ “, ?lname) as ?fullname)
}

30. ©2019 Cambridge Semantics Inc. All rights reserved.
ELT for Data Engineering
Materialized Views – good for heavy calculations - perform once - use many times
CREATE MATERIALIZED VIEW <ages> AS
CONSTRUCT { ?person <age> ?age . }
WHERE { GRAPH <tickit> {
{ SELECT ?person ((YEAR(?date))-(YEAR(xsd:dateTime(?birthdate))) AS ?age)
WHERE {
?person <birthday> ?birthdate .
BIND(xsd:dateTime(NOW()) AS ?date)
}
}
}
}

31. ©2019 Cambridge Semantics Inc. All rights reserved.
ELT for Data Engineering
•Enrichment
Add new features from federated call using SERVICE call to
Linked Open Data Cloud or other internal SPARQL endpoints
Example:
Look up address and geocodes for company, census population data,
crime rate, demographics, etc. All these can be new features to fed
into ML pipeline

32. ©2019 Cambridge Semantics Inc. All rights reserved.
ML Step #1: Data Prep: Data Discovery and Feature Engineering
2.1 Bernoulli Distribution Determines the probability of Success or Failure (or Yes or No).
2.2 Binomial Distribution Determines the probability of success versus failure.
2.3 Chi-squared Distribution Determines the relationship between two categorical variables.
2.4 Exponential Distribution Determines the probability of event occurrence in time interval when past event number is unknown
2.5 Hypergeometric Distribution Determines the probability of success versus failure of a specific scenario.
2.6 Laplace Distribution Determines the probability of intervals.
2.7 Log Normal Distribution To model certain instances, such as the change in price distribution of a stock or commodity positions.
2.8 Logarithmic Series Distribution Determines the probability of occurrence of events like claim frequencies in insurance companies.
2.9 Negative Binomial Distribution Determines the probability of success versus failure.
2.10 Normal Distribution Model and determines probabilities of all natural and social data.
2.11 Poisson Distribution Determines the probability that a certain number of events will occur in a specific time period.
2.12 Skellam Distribution Determines the probability of two independent variables.
2.13 Beta-binomial Distribution Model number of successes in n binomial trials when probability of success p is a Beta random variable.
2.14 Continuous Uniform Distribution Assigns equal probability to all values between its minimum and maximum.
2.15 Discrete Uniform Distribution Determines the probability of finite number of outcomes equally likely to happen.
2.16 Student’s t-Distribution Determines the probability when sample size is small.
2.17 Weibull Distribution Used to assess product reliability, analyse life data and model failure times.

33. ©2019 Cambridge Semantics Inc. All rights reserved.
ML Step #1: Data Discovery and Feature Engineering
Correlations
3.1 Pearson Correlation Coefficient Determines the positive, negative or no relationship between two variables.
3.2 Matthews Correlation Coefficient Determines the positive, negative or no relationship between two binary variables (0 & 1).
3.3 Spearman’s Rank Correlation Coefficient Measures the strength of a linear relationship between paired data.
5.1 Principal Component Analysis Reduces the dimensionality of large data sets and making predictive models.
6.1 Geometric Mean Determines the average growth rates.
6.2 Skewness Metric Calculates Pearson’s coefficient of skewness on Numeric Values.
6.3 T-Digest Metric Determines the percentile and quantile values accurately.
Feature Exploration
Profiling Metrics

34. ©2019 Cambridge Semantics Inc. All rights reserved.
Scalability
Graph OLAP – Horizontally Scalable
Have more data. Need better performance. Add more servers
Deploy on VMs or bare metal with a TAR file that
is compatible with CentOS
Automated deployment in the Cloud.
Available in the AWS Marketplace & others soon
60 Day Full Feature Free Trial. Download or Cloud Deployment. Visit booth or AnzoGraph.com

35. Automated Deployment and Operations
Storage and Compute Integration
MODEL
Graph Data Model
• Lift Data into
Data Fabric
• Design Ontologies
• Connect Data
Models
ON-BOARD
Ingest & Map
• Automated ETL
• Collaborative
Mapping
• Metadata
Capture
Enterprise
Data Sources
Machine
Learning and AI
Enterprise
Search
“Last Mile”
Analytics Tools
Metadata Catalog
Semantic-based Metadata Management, Governance and Lineage
Cloud or On-Prem Data Storage Infrastructure
Data Storage Layer
Ingest
BLEND
GraphMarts
• Combine and Align
Related Data Sets
• In-memory MPP
OLAP Query Engine
• Data Layers
ACCESS
Hi-Res Analytics
• Analyze All
Data Together
• Fast, Iterative Queries
Ad Hoc, What if
• Code Free or API
Graphical Application Interface
Anzo - The Modern Data Discovery and Integration Layer for the Enterprise Data Fabric