talk-data.com talk-data.com

A

Speaker

Amy E. Hodler

2

talks

author

Filter by Event / Source

Talks & appearances

2 activities · Newest first

Search activities →
Knowledge Graphs

Applying knowledge in the right context is the most powerful lever businesses can use to become agile, creative, and resilient. Knowledge graphs add context, meaning, and utility to business data. They drive intelligence into data for unparalleled automation and visibility into processes, products, and customers. Businesses use knowledge graphs to anticipate downstream effects, make decisions based on all relevant information, and quickly respond to dynamic markets. In this report for chief information and data officers, Jesus Barassa, Amy E. Hodler, and Jim Webber from Neo4j show how to use knowledge graphs to gain insights, reveal a flexible and intuitive representation of complex data relationships, and make better predictions based on holistic information. Explore knowledge graph mechanics and common organizing principles Build and exploit a connected representation of your enterprise data environment Use decisioning knowledge graphs to explore the advantages of adding relationships to data analytics and data science Conduct virtual testing using software versions of real-world processes Deploy knowledge graphs for more trusted data, higher accuracies, and better reasoning for contextual AI

Graph Algorithms

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide,developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecksto detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today’s data Understand how popular graph algorithms work and how they’re applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark