intro to graphs for hr analytics
DESCRIPTION
This is the talk that I did at the Belgian Data Science meetup on how graph databases could help with HR Analytics.TRANSCRIPT
Agenda • About Graphs • About Graph Databases • Why Graph Databases ma=er for HR Analy@cs – Short demonstra@on
• Case Studies • Q&A
Introduc@on: about Graphs
Meet ���Leonhard Euler • Swiss mathema@cian • Inventor of Graph Theory (1736)
Königsberg (Prussia) -‐ 1736
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About Graph Databases
So what is a graph database?
• OLTP database – “end-‐user” transac@ons
• Model, store, manage data as a graph
What is a graph? Node
Rela@onship
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
What are graphs good for?
Complex Querying
Examples of complex queries? 1. Semi-‐structure in datasets
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– Normaliza@on introduces complexity
– Forces developers to develop all kinds of logic to deal with this variability in their applica@on logic
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
Examples of Connectedness
Graph Querying
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
Queries: Pa=ern Matching
Pa=ern
Short demo: HR Analy@cs
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
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It always starts with a MODEL
Then for some Queries
• Network Analy@cs – Degree Centrality – Betweenness Centrality – PageRank
• Recommenda@ons – Triadic closures – Complex pa=ern matching
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Use Cases (neo4j.com/use-‐cases)
Customers (neo4j.com/customers)
Graph Gists (h=p://gist.neo4j.org/)
Neo Technology, Inc Confidential
Neo4j License Overview
Developer!Seats!
($6K*/Developer/Year)
Test!Instances!
($6K/Instance/Year)
Production!Instances!
(Bundle / Core Pricing)
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
Neo4j versions / licenses
Personal < Startup / Departmental < Enterprise deployment models Open source & Commercial license terms available
Specific OEM models
Future trainings & events!
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Neo Technology www.neotechnology.com Neo4j www.neo4j.org [email protected] or +32 478 686800
Q&A, Conclusion, Next Steps