This is the talk that I did at the Belgian Data Science meetup on how graph databases could help with HR Analytics.

1. An
Intro
to
Graphs
for
HR
Analy)cs
rik@neotechnology.com

2. Agenda
• About
Graphs
• About
Graph
Databases
• Why
Graph
Databases
ma=er
for
HR
Analy@cs
– Short
demonstra@on
• Case
Studies
• Q&A

3. Introduc@on:
about
Graphs

5. Meet
Leonhard Euler
• 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. So
what
is
a
graph
database?
• OLTP
database
– “end-­‐user”
transac@ons
• Model,
store,
manage
data
as
a
graph

11. What
is
a
graph?
Node
Rela@onship

12. Contrast
with
Rela@onal
Graphs are often 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

13. What
are
graphs
good
for?
Complex
Querying

14. Examples
of
complex
queries?
1.
Semi-­‐structure
in
datasets
1
– Normaliza@on
introduces
complexity
– Forces
developers
to
develop
all
kinds
of
logic
to
deal
with
this
variability
in
their
applica@on
logic

15. Examples
of
complex
queries:
2.
Connectedness
in
data
Lots
of
normalized
rela@onships
between
the
different
en@@es,
forces
developers
to
do
• Deep
joins
• Recursive
joins
• Pathfinding
opera@ons
• “open-­‐ended”
queries

16. Examples
of
Connectedness

17. Graph
Querying

18. 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

19. Queries:
Pa=ern
Matching
Pa=ern

20. Short
demo:
HR
Analy@cs

21. Domains
that
jump
out
• The
REAL
Enterprise
Social
Network
– Be=er
understanding
of
the
“coffee-­‐room”
network
• Recruitment
– Micro-­‐targe@ng
– Social
integra@on
• Competency
management
– Smart
matching
– Taxonomies
– Op@miza@on
algorithms
21

22. It
always
starts
with
a
MODEL

23. Then
for
some
Queries
• Network
Analy@cs
– Degree
Centrality
– Betweenness
Centrality
– PageRank
• Recommenda@ons
– Triadic
closures
– Complex
pa=ern
matching
23

24. Use
Cases
(neo4j.com/use-­‐cases)

25. Customers
(neo4j.com/customers)

26. Graph
Gists
(h=p://gist.neo4j.org/)

27. Neo4j
versions
/
licenses
Neo4j License Overview
Developer!
Seats!
Personal
Startup
/
Departmental
Enterprise
deployment
models
($6K*/Developer/Year)
Test!
Instances!
($6K/Instance/Year)
Production!
Instances!
(Bundle / Core Pricing)
Open
source
Commercial
license
terms
available
Specific
OEM
models
Instances whose purpose is to
ensure that the software accessing
Neo4j is meeting specification.!
!
(e.g. System Test, Integration Test,
UAT, Performance Test, Staging)
Instances that store and process
data in a way that benefits and
advances an organization’s goals.!
!
May be accessed by applications
and/or end users
Includes access by programmers
to licensed test instances, and
private instances on the
programmer’s personal machine
for the sole purpose of writing,
debugging, or testing software
designed to access Neo4j
*Or otherwise, depending on the Bundle, and negotiation

28. Future
trainings
events!
2

29. QA,
Conclusion,
Next
Steps
Neo
Technology
www.neotechnology.com
Neo4j
www.neo4j.org
rik@neotechnology.com
or
+32
478
686800