April 2014 Summaries

Congratulations to Crunchbase on their launch of CrunchBase 2.0, a next-generation website built atop Neo4j that includes the first open and public "Business Graph". The Business Graph is a comprehensive database of startup data covering over half a million people and organizations, with connections between companies, funding, products, and individuals. This milestone marks a significant shift from CrunchBase 1.0 to 2.0, where the focus is on the connections within the data, much like Facebook's social graph has changed online interactions. The website will feature entities such as people, organizations, products, and schools, with plans for events and customer link-backs to Neo4j.com.

The new website for Neo4j users is `neo4j.com`, a single source of knowledge about working with graph databases. It collects written content, videos, and code examples from various sources to provide a comprehensive resource for building graph applications. The site will cover topics such as data modeling, language-specific drivers & frameworks, testing, and capacity planning, while maintaining its commitment to open source and collaboration. The goal is to make `neo4j.com` a companion website that complements the user's experience with Neo4j, driven by community feedback and suggestions.

Neo4j has released a maintenance update, Neo4j 2.0.2, which is part of the 2.0.X series, and users are directed to read Graphs for Everyone for more information about this series. The Neo4j team made this announcement on behalf of their representatives, and as an added incentive, they offer a free copy of O'Reilly's Graph Databases ebook to help users learn more about graph databases and how to apply them in their applications.

This Neo4j query is inefficient as it traverses the same relationships multiple times, resulting in unnecessary computation and potential performance issues. To optimize this, a caching solution can be implemented to store the results of frequently executed queries. In this example, a LoadingCache is created using Google Guava to cache the "peopleWants" and "peopleHas" data, which reduces the number of traversals required. Another cache is created for the "cities" data, but with a refresh interval to keep it up-to-date every 10 minutes. This approach can help improve performance in recommendation engines using Collaborative Filtering.