Intro to Graphs for Fedict

May 19, 20150 likes718 views

This document provides an introduction and overview of graph databases. It begins with an introduction to graphs and their history, then discusses what graph databases are and how they complement relational databases. It introduces Neo4j as an example graph database and describes its key aspects like the labeled property graph data model and Cypher query language. The document then discusses when graph databases are applicable and provides examples. It demonstrates graph querying and concludes with case studies and next steps.

Intro
to
Graph
Databases

in
a
NOSQL
world

19th
of
May
2015

Agenda

•  About
Graphs

•  About
Graph
Databases

–  About
Neo4j

•  Graph
Querying

–  Short
demonstra:on

•  Case
Studies

•  Q&A

Meet

Leonhard
Euler

(again?)

•  Swiss
mathema:cian

•  Inventor
of
Graph

Theory
(1736)

Complemen.ng

Relational Databases
VOLUME
COMPLEXITY

RDBMS

Living
in
a
NOSQL
World

Complexity

Column

Family

Size

Key-‐Value

Store

Document

Databases

Graph

Databases

90%
of
Use

cases

Rela:onal

Databases

Naviga:onal

Databases

So
what
is
a
graph
database?

•  OLTP
database

•  “end-‐user”
transac:ons

•  Model,
store,
manage
data
as
a
graph

Contrast
with
Rela.onal

Graphs
are
o]en
referred
to
as
“Whiteboard
Friendly”.
The
data
model
reﬂects
the
way
a

domain
expert
would
naturally
draw
their
data
on
a
whiteboard

“The
schema
is
the
data”.
Schema
ﬂexibility
allows
the
system
to
change
in
response
to
a

changing
environment

Neo4j
is
a
Graph
Database

•  JVM
based

•  ACID
transac:ons

•  Rich
Java
APIs

•  Query
language

•  Using
the
Labeled

Property
Graph

model

Cypher:
THE
graph
query
language

•  Learning
from
RDBMS’
evolu:on

•  Introduc:on
of
SQL!

•  Key
characteris:cs

•  Declara:ve:
tell
it
what
you
want,
not
how
to
get
it

•  Expressive:
Op:mize
for
reading

•  Pagern
matching:
easy
on
your
brain!

•  Idempotent:
state
change
expressed
idempotently

Labeled
Property
Graph
Model

Author
Book
Reader
Reader
Author
Book
Author

Labeled
Property
Graph
Summary

•  Nodes

•  Containers
for
proper:es

•  Grouped
together
in
subgraphs
by
“Labels”

•  Proper:es

•  Key-‐value
pairs

•  Primi:ve
and
array
values

•  Rela:onships

•  Name

•  Direc:on

•  May
also
contain
proper:es

•  Rela:onships
(ctd.)

•  Must
have
a
start
node
and
an
end
node

(no
dangling
rela:onships)

•  Start
node
and
end
node
can
be
the
same

(e.g.
‘self’
rela:onships)

•  Nodes
can
be
connected
by
more
than
one

rela:onship

Data
Complexity

complexity = f(size, semi-structure, connectedness)

complexity = f(size, semi-structure, connectedness)
The
Real
Complexity

Semi-‐Structure

Email:
rik@neotechnology.com

Email:
rik@vanbruggen.be

Twiger:
@rvanbruggen

Skype:
rvanbruggen

USER

CONTACT

CONTACT_TYPE

FIRST_NAME
LAST_NAME
USER_ID
EMAIL_1
EMAIL_2
TWITTER
FACEBOOK
SKYPE

Rik
Van
Bruggen
315
rik@neotechnology.com
rik@vanbruggen.be
@rvanbruggen
NULL
rvanbruggen

When
Should
I
Use
Graph
Databases??

•  Densely-‐connected,
semi-‐structured
domains

•  Lots
of
join
tables?
Connectedness

•  Lots
of
sparse
tables?
Semi-‐structure

•  Data
Model
Vola:lity

•  Easy
to
evolve

•  “Graphy”
Query
pagerns

•  Deeps
Join
Complexity
and
Performance

•  Pathﬁnding
opera:ons

•  Millions
of
‘joins’
per
second

•  Consistent
query
:mes
as
dataset
grows

Querying
a
Graph

•  “Graph
local”
vs
“Graph
global”

•  Contextualized
“ego-‐centric”
queries

•  “Parachute”
into
graph

•  Start
node(s)

•  Found
through
Index
lookups

•  Crawl
the
surrounding
graph

•  2
million+
joins
per
second

•  No
more
Index
lookups:

Index-‐free
adjacency

www.neo4j.com

www.meetup.com/graphdb-‐belgium

rik@neotechnology.com
or
+32
478
686800

Q&A,
Conclusion,
Next
Steps

People like graphs. In nowadays they use facebook social graph search to find ex-girlfriend/boyfriends of their sweet hearts, or to search for a new love. Moreover - companies use graphs to evaluate the internal communication effectiveness or to design the enterprise network scheme. In all those tasks the simple questions arise - what type of data storage should be used to solve the problem in the most effective and easy? Graph databases!

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This document summarizes a method for constructing protein networks from public proteomics data. It involves pairing proteins that co-occur in experiments and mapping these pairs to existing knowledge bases to identify biologically related pairs. Over 2300 protein pairs were found with a Jaccard similarity score of at least 0.4, and 71% of these were known associations according to literature. The associated protein pairs are stored in an online database called Tabloid Proteome that allows visualization of the network and detection of indirect protein relations through graph algorithms.

4 tom michiels - graph platform enablerRik Van Bruggen

The document discusses the rise of platforms and artificial intelligence. It describes how platforms allow users to create and consume value, and how developers can extend platform functionality through APIs. The rise of platforms also fueled advances in artificial intelligence, as platforms accumulated large amounts of user data that could be used to train machine learning models. Specific examples of platforms discussed include Facebook, Amazon, and Google.

Reinventing Identity and Access Management with Graph DatabasesRik Van Bruggen

This document discusses reinventing identity and access management (IAM) with graph databases. It notes that traditional IAM systems have static views of identity that cannot handle today's complex, dynamic identities for users, things, and services. Graph databases allow for modeling the flexible, multidimensional relationships between entities that are needed for modern IAM. Neo4j is highlighted as a graph database that can represent complex identity relationships and hierarchies and enable real-time traversal of these relationships for advanced access management and compliance checks. Case studies demonstrate how Neo4j has been used successfully for flexible, high performance IAM applications.

Cevora ICT Symposium - Graph DatabasesRik Van Bruggen

20150624 Belgian GraphDB meetup at OrdinaRik Van Bruggen

This document provides an overview of how to embed graph visualization in an application using Neo4j and D3.js. It discusses: - Accessing Neo4j via REST API or embedded mode - Using D3.js for graph visualization by converting Neo4j data to nodes and links format - Typical application architectures with Neo4j server and client-side components - An example workflow of querying Neo4j, converting the response for D3.js force layout, and building the force layout - Requirements including D3.js force layout, Neo4j REST API, and converting between Neo4j and D3.js data formats The

20150619 GOTO Amsterdam Conference - What Business can learn from DatingRik Van Bruggen

20150326 data innovation summit IGNITE talkRik Van Bruggen

20150121 wolters kluwer innovation pitchRik Van Bruggen

This document discusses how graphs can be used to solve big data problems by enabling insights through modeling and analyzing connections in data. It provides examples of how graphs have transformed industries like consumer web, telecommunications, and databases. Specifically, it outlines how graphs can be used to model reality, store data in high fidelity, look for connections in content, customers, users and products, and build these graph-based insights into applications for recommendations, impact analysis, dependency management, and risk analysis.

20141216 graph database prototyping ams meetupRik Van Bruggen

201411203 goto night on graphs for fraud detectionRik Van Bruggen

This document discusses how graph databases can be useful for fraud detection. It begins with an introduction to graphs and graph theory, then discusses how graph databases work and their advantages over relational databases for complex querying and modeling connected data. The document notes that fraud detection relies on real-time analysis, complex patterns, and graph algorithms to navigate relationships. It provides a short demonstration and discusses case studies where graph databases have been successfully used for fraud detection due to their ability to efficiently handle large, interconnected datasets.

Using graphs for recommendationsRik Van Bruggen

This document contains a presentation on using graph databases for recommendations. It begins with an introduction to graphs and graph theory, then discusses what graph databases are and how they are different from relational databases. It explains how graphs are well-suited for complex querying and representing connected data. The presentation describes how recommendation systems work and how graph algorithms and storing recommendation data in a graph structure provide benefits like real-time recommendations, navigating relationships between items, and efficient operations. It concludes with a demonstration, examples, and discussing future events.

20141015 how graphs revolutionize access managementRik Van Bruggen

This document discusses how graph databases can revolutionize access and identity management. It begins with an introduction to graphs and graph databases, explaining how they are well-suited for complex querying of connected data. The document then argues that graph databases allow for a more accurate representation of real-world identity relationships, which are often multi-dimensional, and enable real-time queries that eliminate the need for integration between different systems. A demonstration of a graph database is provided, followed by examples, licensing information and a question and answer section.

2 dirk vermeylen - modeling with neo4 jRik Van Bruggen

1 rik van bruggen - intro and state of the graphRik Van Bruggen

3 surya gupta - tabloid proteomeRik Van Bruggen

4 tom michiels - graph platform enablerRik Van Bruggen

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20150624 Belgian GraphDB meetup at OrdinaRik Van Bruggen

20150619 GOTO Amsterdam Conference - What Business can learn from DatingRik Van Bruggen

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20150121 wolters kluwer innovation pitchRik Van Bruggen

20141216 graph database prototyping ams meetupRik Van Bruggen

201411203 goto night on graphs for fraud detectionRik Van Bruggen

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20141015 how graphs revolutionize access managementRik Van Bruggen

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Intro to Graphs for Fedict

1. Intro to Graph Databases in a NOSQL world 19th of May 2015

2. Agenda •  About Graphs •  About Graph Databases –  About Neo4j •  Graph Querying –  Short demonstra:on •  Case Studies •  Q&A

3. Introduc.on: about Graphs

5. Meet Leonhard Euler (again?) •  Swiss mathema:cian •  Inventor of Graph Theory (1736)

6. Königsberg (Prussia) -‐ 1736

7. A B D C

8. A B D C 1 2 3 4 7 6 5

9. About Graph Databases

10. Complemen.ng Relational Databases VOLUME COMPLEXITY

11. NOT ONLY SQL

12. RDBMS Living in a NOSQL World Complexity Column Family Size Key-‐Value Store Document Databases Graph Databases 90% of Use cases Rela:onal Databases Naviga:onal Databases

13. So what is a graph database? •  OLTP database •  “end-‐user” transac:ons •  Model, store, manage data as a graph

14. What is a graph? Vertex Edge

15. What is a graph? Node Rela:onship

16. Contrast with Rela.onal Graphs are o]en referred to as “Whiteboard Friendly”. The data model reﬂects the way a domain expert would naturally draw their data on a whiteboard “The schema is the data”. Schema ﬂexibility allows the system to change in response to a changing environment

17. Neo4j is a Graph Database •  JVM based •  ACID transac:ons •  Rich Java APIs •  Query language •  Using the Labeled Property Graph model

18. Cypher: THE graph query language •  Learning from RDBMS’ evolu:on •  Introduc:on of SQL! •  Key characteris:cs •  Declara:ve: tell it what you want, not how to get it •  Expressive: Op:mize for reading •  Pagern matching: easy on your brain! •  Idempotent: state change expressed idempotently

19. Labeled Property Graph Model Author Book Reader Reader Author Book Author

20. Labeled Property Graph Summary •  Nodes •  Containers for proper:es •  Grouped together in subgraphs by “Labels” •  Proper:es •  Key-‐value pairs •  Primi:ve and array values •  Rela:onships •  Name •  Direc:on •  May also contain proper:es •  Rela:onships (ctd.) •  Must have a start node and an end node (no dangling rela:onships) •  Start node and end node can be the same (e.g. ‘self’ rela:onships) •  Nodes can be connected by more than one rela:onship

21. What are graphs good for? Complexity

22. Data Complexity complexity = f(size, semi-structure, connectedness)

23. complexity = f(size, semi-structure, connectedness) The Real Complexity

24. Semi-‐Structure

25. Semi-‐Structure Email: [email protected] Email: [email protected] Twiger: @rvanbruggen Skype: rvanbruggen USER CONTACT CONTACT_TYPE FIRST_NAME LAST_NAME USER_ID EMAIL_1 EMAIL_2 TWITTER FACEBOOK SKYPE Rik Van Bruggen 315 [email protected] [email protected] @rvanbruggen NULL rvanbruggen

26. complexity = f(size, semi-structure, connectedness) The Real Complexity

27. Examples of Connectedness

28. When Should I Use Graph Databases?? •  Densely-‐connected, semi-‐structured domains •  Lots of join tables? Connectedness •  Lots of sparse tables? Semi-‐structure •  Data Model Vola:lity •  Easy to evolve •  “Graphy” Query pagerns •  Deeps Join Complexity and Performance •  Pathﬁnding opera:ons •  Millions of ‘joins’ per second •  Consistent query :mes as dataset grows

29. Graph Querying

30. Querying a Graph •  “Graph local” vs “Graph global” •  Contextualized “ego-‐centric” queries •  “Parachute” into graph •  Start node(s) •  Found through Index lookups •  Crawl the surrounding graph •  2 million+ joins per second •  No more Index lookups: Index-‐free adjacency

31. Queries: Paern Matching Pagern

32. Short demo

33. Case Studies

34. www.neo4j.com www.meetup.com/graphdb-‐belgium [email protected] or +32 478 686800 Q&A, Conclusion, Next Steps

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