September 2014 Summaries

This article demonstrates how to upload Last.fm data, a music listening history database, into Neo4j using RNeo4j. The author collects scrobble data from the Last.fm API and writes it to a CSV file, which is then imported into Neo4j in blocks of 1000 rows to avoid performance issues with the batch importer. The data model includes relationships between users, tracks, artists, and genres, allowing for querying of music listening history and artist information. After creating the database, the author adds genre information for the artists by retrieving the top tags for each artist using the Artist.getTopTags endpoint and updating the corresponding artist nodes with a relationship to the genre node. The process is done in batches to ensure performance and efficiency.

Neo4j.rb, a Ruby on Rails and Rack framework replacement for Neo4j, has been released after a year of contributions. The new version, version 3.0, offers improved deployment options, support for Neo4j REST Server using Ruby MRI, and a rewritten Cypher query DSL called QueryProxy that provides ActiveRecord-like syntax and increased power. This allows developers to easily create complex queries without needing advanced knowledge of the Cypher language. Additionally, Neo4j.rb introduces ActiveRel, relationship wrapper models, and Neo4j-flavored Enumerable methods, making it more friendly for Rails developers and committed to maintaining its features with a growing community.

This week's highlighted GraphGist is Neo4Art, a project that uses Neo4j to map Van Gogh's life and art journey in a graph database. The project was recently presented at the SpringOne Conference in Dallas as part of Spring Data Neo4j and SpringBoot introductions. To learn more about graph databases, users can download a free copy of O'Reilly's Graph Databases ebook, which explores how to use graph technologies for application development.

Five reasons why you shouldn't miss out on GraphConnect 2014 SF include attending a performance by Neo4j co-founder Emil Eifrem at the SFJAZZ Center, enjoying food from award-winning restaurant The Slanted Door, taking part in Fikas (coffee breaks) and networking with other graphistas, dancing to live jazz music at the GraphParty, and meeting over 700 experts and enthusiasts of graph databases from around the world.

David Montag, a Neo4j Field Engineer, recently appeared on DevCast, discussing the limitations of traditional SQL databases in handling complex data relationships and the benefits of using graph databases like Neo4j. He emphasizes that while "one SQL database to rule them all" may have been a viable solution in the past, it is no longer sufficient in today's rapidly growing world of data, where new types of databases are needed to effectively manage relationships between nodes. Montag dives into the world of graph databases and Neo4j with DevCast host, providing insights into the capabilities and applications of this database technology.

Neo4j's search engine for Van Gogh's artworks was showcased at the SpringOne 2GX conference in Dallas, where Neo4Art was presented as a live demo. The project used Spring Data Neo4j with a persistence tier and a presentation tier implemented using more classical Spring MVC and MapQuest technologies. A REST service was also developed to make search engine queries available, using Jersey Resources. At the conference, the developers wanted to highlight the power of Spring Boot in creating production-ready applications and demonstrate how to configure Spring Data Neo4j with Spring Boot instead of using XML files or @Configuration classes. Additionally, they took advantage of the Cloud Foundry team's presence by moving Neo4Art to their cloud.

This week's GraphGist of the Week highlights iKwattro's impressive GitHub Events Analysis with Neo4j, showcasing his ability to extract valuable insights from large datasets using graph databases. His analysis reveals interesting statistics such as which repository was most forked and the average number of comments on a PR before it's merged. The achievement is recognized through various channels, including a call-out in GraphGists, an invitation to submit work for consideration, and a promotion to explore O'Reilly's free Graph Databases ebook.

GraphConnect 2014 Sponsor, Linkurious discusses mapping fraud with Neo4j. eCommerce operations are confronted with "reshipping scams" where mules are used by online fraudsters to turn their credit cards into actual goods. Graphs can help detect this fraud by comparing billing and shipping addresses and analyzing IP addresses. A small dataset is loaded into a Neo4j graph database to demonstrate how to identify suspicious patterns in online transactions. The analysis reveals that three transactions with different billing and shipping addresses are suspicious, and further investigation is needed. Network visualization helps understand the link between orders, IP addresses, and accomplices, allowing for potential freezing of transactions and alerting authorities. Linkurious will be a sponsor and panelist at GraphConnect 2014 SF, a conference focused on graph databases and applications.

Structr 1.0 has been released as a general availability version, an open-source software based on Neo4j, providing a simplified way to create mobile and web applications with features like access control, user management, file and image management, and customizable data models. The software utilizes the schema-free nature of Neo4j, allowing for runtime modifications without performance loss. Structr is designed for rapid application development, combining flexibility with ease-of-use, and its commercial version offers hosted instances starting at 30 € per month.

A graph database like Neo4j can contribute to a better Enterprise Architecture by enabling quick business decisions, flexibility in adapting to changing circumstances, and improved operational performance. Graph databases bring a natural fit with interconnected domains, cutting down implementation efforts by an order of magnitude, and simplifying complex operations. They allow for flexible data modeling, growth, and adaptation, enabling developers to work with their data structures in many different situations. Additionally, graph databases offer significant performance improvements over traditional relational databases, particularly for query patterns that require hardware/software horsepower, making them a reliable choice for enterprise architectures.

Graph databases like Neo4j are revolutionizing the way companies offer online products or services by enabling them to build highly sophisticated recommender systems. These systems can analyze customer behavior and preferences in real-time, providing personalized recommendations that maximize revenue. Graph databases give equal prominence to storing both data and relationships between entities, allowing for rich semantic context and fast query performance. This enables companies to make finely-tuned recommendations that cater to individual customers' interests and preferences, rather than relying on aggregate best-sellers. With the advent of graph databases, organizations can transform their online business with powerful recommender systems like Walmart's product recommender system and SNAP Interactive's dating app, which use Neo4j-powered engines to deliver fast response times across large social graphs.

The community has been actively contributing to the Neo4j platform, with various books, blog posts, GraphGists, and presentations shared throughout August. Several contributors have showcased their expertise in using Neo4j for different purposes such as learning the basics, building architectures, analyzing data, and modeling graphs. The contributions cover a range of topics including deep learning text classification, GitHub events analysis, and graph data modeling. These resources provide valuable insights and information for individuals looking to learn more about Neo4j and its applications in various fields.

Neo4j is a graph database that supports many well-known dating sites and apps, including Meetic, DOWN, and SNAP Interactive Inc., making it a key player at iDate 2014 in Cologne, Germany. Neo Technology Area Director CEMEA Holger Temme highlights the product's ability to add functionality and enable efficient query speed and flexibility, comparing its graph structure to family trees, mind maps, or social networks. At iDate, Stefan Armbruster will present a session on "Dating Website Empowerment via Graph Databases," discussing how companies are using graph databases to create algorithms for detecting fraud, making connections, and finding love, which is crucial for the success of dating sites. According to Temme, graph databases like Neo4j are optimized for querying connections between people, interests, or things, increasing the likelihood of interactions and helping businesses generate insights from vast customer bases. With its scalability and performance benefits, Neo4j is contributing to the growth and development of the dating industry by providing a competitive edge to companies using it.

Wes Freeman explores the connection between Neo4j, a graph database, and Chess Game Positions using a chess game mapping program called StockFish. He analyzes scores and blunders of chess masters to gain insights into the game. The project allows users to create their own GraphGist using an asciidoc platform and share it on the Wiki page or submit to the #ShowMeYourGraph Twitter Contest for a chance to win over $1k in prizes. Wes's work showcases the potential of graph technologies in understanding complex data, such as chess game positions.

Graphify is an unmanaged Neo4j extension that uses natural language text classification to train deep learning models. It provides a plug-and-play solution for extracting features from text data using a graph database, allowing for the generation of vector space models and cosine similarity-based classification results. The Graphify system consists of three main endpoints: classify unlabeled text, get similar classes, and training. The classify endpoint takes in an article of text with provided labels and returns a sorted list of matches based on cosine similarity. The get similar classes endpoint takes a class name as input and returns the most similar classes ordered by their similarity scores. The training endpoint is used to train the model using repetition-based learning, where features are learned through repetition of similar phrases in text data. Graphify provides an easy-to-use API for developers to implement this technology in their applications.