March 2018 Summaries

The Neo4j community is highlighting the work of Jeffrey Miller, a Senior Consultant who has delivered presentations on Neo4j graph technology and humanitarian healthcare projects. The community is also showcasing various Neo4j-related projects, including Max De Marzi's meeting room scheduling task demo, Timothée Mazzucotelli's Python front-end for Neo4j, Zeqi Lin's data import repositories, and Stefan-Gabriel Muscalu's New Relic-Neo4j metrics integration. Additionally, the community is discussing new features like geo-spatial indexes in Neo4j 3.4, a Data Visualizer for larger graphs using vis.js, and an upcoming update to Neo4j Desktop with the neo4j-graphql extension. The community is also sharing Easter-themed tweets and events, including the upcoming PyData London conference on April 29th.

In order to achieve compliance with the EU's General Data Protection Regulation (GDPR), organizations must invest in graph database technology, such as Neo4j, which can help identify and model personal data elements, develop and test GDPR-compliant systems, and visualize the flow of personal data across systems. By following four practical steps outlined in this series, companies can build a connections-first perspective on their data that delivers value to their bottom line while staying ahead of evolving regulations.

This week in Neo4j highlights the latest developments and news from the graph database community. Featured community member Andy Robbins is recognized for his contributions to the Neo4j community, including work on the BloodHound security engineering tool. The newsletter also covers various resources for learning about GraphQL and Neo4j, including a Star Wars-themed example application and a mapping layer between GraphQL and Neo4j. Additionally, there are articles on knowledge graphs and modern AI, as well as updates on the Open Beer DB graph and a new library for interacting with Neo4j using Kafka. The newsletter concludes with information about upcoming events and a tweet of the week from Carter Rabasa.

Theo is a Neo4j driver for Swift that provides an easy-to-use API for interacting with the graph database. The latest version, Theo 4.0, has made significant improvements, including a results-oriented API and support for Swift 4.0, while removing the deprecated REST client. The new API uses a Result type to handle errors and exceptions, making it easier to reason about and debug. With its clean design and minimal learning curve, Theo 4.0 is suitable for developers of all experience levels. Future updates will focus on bugfixes, support for new Neo4j versions, and minor improvements based on community input. As a companion project, Bolt-Swift provides an alternative way to connect to Neo4j using the Swift programming language, while PackStream-Swift offers a serialization format that can be used with Bolt. Developers are encouraged to provide feedback through Stack Overflow or the neo4j-users Slack channel.

The European Union's General Data Protection Regulation (GDPR) is a significant concern for companies worldwide, with penalties ranging from twenty million euros to four percent of global sales in the event of non-compliance. Graph database technology is being explored as a solution to overcome GDPR compliance challenges, particularly in addressing the complex data lineage problems posed by personal data. Unlike relational databases and most NoSQL technologies, graph databases are designed for connected-data applications like GDPR, enabling powerful data visualization tools that allow companies to model and track sensitive data through their systems. A native graph technology platform, such as Neo4j, is considered superior for GDPR compliance due to its ability to handle complex data relationships, providing clear answers about personal data and satisfying regulators and consumers who demand proof of compliance.

The Neo4j community has been actively engaged, with Iryna Feuerstein being featured as a community member for her work on modeling and importing German laws into the graph. Max De Marzi gave a sneak peek of property-based access control in Neo4j 3.4, which will be released soon. The community also has resources available to get started with Cypher, including a video course by Esteve Serra Clavera. Additionally, there are updates on projects such as Neo4j-GraphQL, Extending R for Neo4j, and the Indie Music Network. On the podcast, Dilyan Damyanov discussed path analysis and how Snowplow Analytics uses graphs to track user movement in marketing funnels. The community is also looking forward to upcoming events, including a webinar on modeling and importing data from the Yelp Dataset Challenge into Neo4j.

Pypher is a suite of lightweight Python objects that allow users to express Cypher queries in pure Python, providing a simple and intuitive interface for building Cypher queries. It covers all Cypher use-cases with an interface similar to Cypher and is designed to be easily expandable for future updates. Pypher can build out complex Cypher queries without the need for convoluted string concatenation, automatically binding parameters and making queries safer by reducing the chances of Cypher injection. However, it may require learning some special syntax and watch out for assignments, Python keywords, bound parameters, and property access when using Pypher. The project is living and evolving with the Cypher language, allowing users to report bugs or suggest improvements through a GitHub ticket.

Graph Gopher is a tool that allows users to interact with their Neo4j graphs through easy browsing and quick entry for new nodes and relationships. It provides a full Cypher client at your fingertips, enabling fast editing of existing data. The application was created by exploring different ways to browse Neo4j graphs and was initially inspired by the classic navigation interface used in Gopher, the navigation paradigm used to explore the internet before web browsers. Graph Gopher is built using Swift, an open-source language used for iOS apps, and supports Bolt, a protocol used by Neo4j. The application aims to be faster than loading up the Neo4j browser and can be accessed on various devices, including mobile devices, where it provides features such as multi-device support, session configuration transfer between devices, and connectivity over flaky network connections. The future of Graph Gopher will be driven by user feedback, with new features and updates to be added based on user needs and use cases.

This week in Neo4j has seen several exciting developments and announcements. Meredith Broussard, an Assistant Professor at New York University, is this week's featured community member, recognized for her work on data-driven reporting, computational journalism, and data visualization. Additionally, there have been two stories about real-time recommendation engines - one using Neo4j to build a personalized recipe recommendation engine by Gousto, and another showing how to build a recommendation engine for event promoters using graph algorithms. The community has also seen updates on the Neo4j Operational Dashboard, a JavaScript OGM called Neode, and a series of posts on graph data modeling. Furthermore, Michel Caradec's workshop on implementing graph theory with Neo4j was well-received by the community. Upcoming events include Data-journalisme et Panama Papers, GraphQL, curieux d’en savoir plus? and Joshua Yu, Brandon Richards. Finally, Khairul Anwar's tweet about discovering Neo4j was a highlight of the week.

The project aims to combine RDKit and Neo4j to support research in medicinal chemistry and materials sciences by integrating data from various sources, including recipe and characterization data. The integration of these tools could facilitate the joining of large amounts of complex data, enabling researchers to focus on the relationships between "things" rather than just their individual properties. This approach is based on the concept of knowledge graphs, which have proven to be versatile and effective in managing complex data. By leveraging Neo4j's graph database capabilities and RDKit's functionality for chemical analysis, the project seeks to provide a powerful toolset for researchers to efficiently process and analyze large amounts of data related to chemical research and development.

Neo4j has launched its 1000th certified professional, Gábor Szárnyas, a Featured Community Member, and has updated its certification program to reflect the new features introduced in Neo4j 3.x series. The certification test consists of 80 questions that must be answered within an hour, with a score of 80% or higher required for passing, and is free for all candidates. To prepare for the test, candidates can use resources such as long-lived topics like graph database basics, property graph model specifics, Cypher syntax, and data modeling problems, as well as new features in Neo4j 3.x series. A link to an official certificate can be obtained by sending an email to [email protected] for those who have already passed the test.

In the upcoming JavaLand event, attendees will have the opportunity to learn about Graph Data and Neo4j from experts in the field. Iryna Feuerstein will introduce a workshop on graph data, while Dirk Mahler and Stephan Pirnbaum will discuss how to tackle monolithic software systems by breaking them down into microservices. Gerrit Meier will host a BOF session on Neo4j & Graph Data Banken, where attendees can ask questions and learn about various topics such as Cypher, Spring Data Neo4j, deployment, clustering, modelization, graph algorithms, data analysis with Python, and visualization. Markus Harrer will give a talk on using Neo4j and jQAssistant for software system analysis, while Johannes Unterstein will discuss container orchestration and the use of Mesos' infrastructure to manage large container setups. Finally, Michael Hunger will lead a full-day workshop where attendees can learn how to create personalized recommendations using Neo4j.

Twitter's 2016 U.S. presidential election interference involved a known Russian troll factory, the Internet Research Agency, using fake accounts and hashtags to propagate false news and influence the outcome. The accounts were mostly retweets of others, amplifying messages rather than posting original content. Three categories of accounts emerged: typical American citizens, local media outlets, and local political parties. Analyzing the data revealed an amplification network with three distinct communities and influential accounts. The Mueller indictment named two Twitter accounts specifically, @TEN_GOP and @March_For_Trump. To monitor and prevent future abuse, social media platforms and governments should use connections-first approaches to analyze datasets, using graph algorithms like PageRank to detect patterns of behavior.

The Neo4j community has been active in recent days, with new releases of APOC and various projects and updates from users. The APOC library has crossed the 500 GitHub star milestone and includes new features such as HDFS support, aggregation functions, and path functions. Users have also been working on various projects, including finding duplicate users, using Cypher for Gremlin, and building recipe ontologies with Neo4j. The community has also featured a notable member, Irene Iriarte Carretero, who presented at GraphConnect NYC 2017 and GraphTour London event, and discussed her work on using Neo4j to build recipe ontologies. The podcast also featured an interview with Jonathan Schmidt, founder and CTO of Waykonect, which uses Neo4j for intelligent vehicle management. Upcoming events include a mix of Neo4j and customer speakers at various locations.

The latest APOC release has made several significant improvements, including easier installation in Neo4j Desktop and enhanced functionality for data manipulation, indexing, aggregation, and path expansion. The new release includes features such as support for S3 URL loading, HDFS URL writing, and compound labels in label filters. Additionally, the release adds new text functions like `apoc.text.code()` and `apoc.text.hexCharAt()`, as well as data integration capabilities with MongoDB and generic XML import. Other notable additions include graph refactoring improvements, collection functions, and a new code of conduct for contributors. The release also includes bug fixes and performance optimizations across various APOC modules.