Devoxx UK 2019
from Wednesday 8 May to Friday 10 May 2019.
Jennifer Reif is a Developer Relations Engineer at Neo4j, conference speaker, blogger, and an avid developer and problem-solver. She holds a Master’s degree in Computer Management and Information Systems and has worked with large enterprises to organize and make sense of widespread data assets and leverage them for maximum business value. She has worked with a variety of commercial and open source tools and enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.
See also https://firstname.lastname@example.org
In this connected world, traditional data stores often make it difficult to find valuable relationships. By making them a key component of the model, contextualizing a set of data becomes incredibly simple.
In this session, we will walk through what a graph database is and how it can transform your applications and data. We will explore creating, querying, and displaying data and learn how to use simple tools to interact with the database. We will cover the whiteboard-friendly model and the basics of the Cypher query language. Live demos will show developers how to interface with the database and the data in it.
Join us to learn how graph databases are used to improve the data world and help developers easily extract/import connected data!
Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack the strongest predictors of behavior - relationships. Just as humans require contextual information to make better decisions, so do machine-learning algorithms. Combining ML processing with a graph data structure can help fill in the missing contextual information and improve our predictions. In this session, we will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future. We will see how to fine-tune the elements we measure and understand the results for decisions or further adjustments. Learn how to exploit the power of connected data to improve prediction analysis!