In this webinar, data analytics gurus Sathish Thyagarajan and Steve Sarsfield introduce AnzoGraph™, our graph OLAP database, demonstrate the different types of analyses you can perform with it and how it complements Neo4j, AWS Neptune and other OLTP systems. Finally, they’ll show how you can get it up and running on your laptop in about 5 minutes.

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3. Growing
amounts of
data requiring
the need for
better insights
fast- need for
linked data
insights
Graph databases
are well-suited to
deal with
hierarchy in data
and unstructured
data
Linked data and
graph
databases
provide better
foundation for
AI, ML and
Deep Learning
Graph Databases and specifically Graph OLAP Databases
can help tackle these challenges.

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8. select b.name , to_date(time,'yyyy-mm-dd') as day, sum(a.qty) as Total_purchased
from Sales a, Customer b, Inventory C where a.Customer = b.Custid and a.Item =
b.SKU and b.Name = 'Jack' and upper(C.description)='PEPSI' and
to_date(a.time,'yyyy-mm-dd')>='2018-01-01' group by b.name,
to_date(time,'yyyy-mm-dd') order by b.name, to_date(time,'yyyy-mm-dd');

11. Guggenheim
Museum
Smithsonian
Louvre
Met
New York
Paris
Washington

12. 235.10.1.1
10.53.27.233
10.53.27.231
19.15.9.9
10.53.27.232
10.53.27.234
19.15.9.8

13. Atlanta
Charlotte
London
Boston
New York
Paris
Bank A
Bank B
Bank C

14. Informed
Predictions
Person
Thing
Place
Machine
learning
algorithms
Natural
language
processing

15. Linear
comparison of
gene against
reference gene
to find variants
New graph
techniques to
look across
multiple genes
and map known
variants

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Key factor:
● Data warehouse-style queries.
● Traverse large portions of the DB.

20. Query 16: The Parts/Supplier Relationship Query counts the number of suppliers who
can supply parts that satisfy a particular customer's requirements. The customer is
interested in parts of eight different sizes as long as they are not of a given type, not of a
given brand, and not from a supplier who has had complaints registered at the Better
Business Bureau. Results must be presented in descending count and ascending brand,
type, and size.

21. Query
Scalability of
short, fast
queries
Real-time
transactions
Batch & Near
Real-time
Fast Data
Loading
Real-time Deep
Queries &
Analytics on
Data Volumes
Advanced
Analytics
Focus on single transactions &
relationships
[Servers added for more ingestion]
Focus on interactive analytics
on entire corpus of data
[Servers added for processing speeds
and data volumes]

22. Query
Scalability of
short, fast
queries
Real-time
transactions
Batch & Near
Real-time Fast
Data Loading
Real-time Deep
Queries &
Analytics on
Data Volumes
Advanced
Analytics
Defining
“normal” based
on long term
patterns
Check if things
are “normal” with
short running
queries
OLTP AND OLAP are often
used together
Example: IT Security Analytics
Security
Forensics
Current Threat
and Alerts

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Date: 11/4/2018
Quan: 4

26. SPARQL 1.1 Standards AnzoGraph Extensions
Graph Patterns
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
Graph Algorithms
Page Rank
Shortest Path
All Path
Label Propagation
Weakly Connected Components
K neighborhood
Counting Triangles
Inferences (RDFS+)
Window Aggregates
Advanced Grouping Sets
Named Views
Named Queries
Labeled Property Graphs (RDF*)
Conditional Expressions
User-Defined Extensions
<more>

27. source: Wikipedia

29. 60 Day Free Trials
Available on AnzoGraph.com

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32. Also, contact:
steve.sarsfield@cambridgesemantics.com
sathish.thyagarajan@cambridgesemantics.com
Let us know how it goes. Happy to discuss your
use case.
AnzoGraph.com