Intro to Graph Databases

Join us for a great evening as we talk graph databases and more! As this is happening at Neo4j headquarters, you'll have the opportunity to meet the team and ask us questions! We look forward to seeing you there

AGENDA:
18:00-18:30 - Welcome with light food and beverages

18:30-19:00 - When Relationships Matter with Amanda Laucher

19:00-19:20 - How to implement predictive autocomplete for search using Neo4j and Windows Azure with Kenny Bastiani

19:20-19:45 - Q&A, discussion

19:45-20:30 - Networking and drinks

ABSTRACT: When Relationships Matter
As the industry awakens to the abuse of relational data modeling, the graph datastore arises as a compelling choice for many data needs. This may not be extremely obvious to those who have been raised in the RDBMS era for use beyond social networking. Even if you aren’t working at Facebook or in a similar space (or even with jvm languages), there are many reasons you may want to look at a graph for your data needs, specifically Neo4j.

At this meetup, Amanda will discuss Neo4j and it’s uses. We’ll talk about what makes it different from aggregate data stores and then look at the query language, Cypher, and the server management tools that make it a real option for enterprises. Hopefully by the end of this session you will see graph data stores as one of the tools for your polyglot persistence needs.

SPEAKER: Amanda Laucher, Senior Consultant, Neo Technology
Amanda Laucher is currently working with Neo Technology, the company behind Neo4j. She is a language geek who is often found ranting about functional languages and great type systems. You may know her as @pandamonial on twitter or from previous conference sessions on F#, FP, DSLs, Type systems, Agile methodologies, or any number of other topics that make her passionate about software development.

ABSTRACT: How to implement predictive autocomplete for search using Neo4j and Windows Azure

Predictive autocomplete for search is one of the most valuable information retrieval features for helping users quickly search vast volumes of data. In this talk I will walk you through implementing lightning fast predictive autocomplete using Windows Azure and Neo4j in 20 minutes. I will provide you with a simple code library that will plug and play into your existing Neo4j database and give you the ability to quickly implement your own Google-like autocomplete that scales linearly to an unlimited number of queries.

SPEAKER: Kenny Bastiani, Founder, Arktera.com

Kenny Bastani is a Bay Area software developer, consultant, and the founder of Arktera.com, a natural language search engine project for Wikipedia that is built on Neo4j and Windows Azure’s distributed cloud platform. Kenny is passionate about finding novel approaches to solving complex information retrieval problems using unsupervised learning.