February 2018 Summaries

Neo4j, a graph database management system, is capable of loading and writing RDF data. However, until now, RDF and OWL reasoning have been associated with fully-fledged triple stores or dedicated reasoning engines only. This post demonstrates that Neo4j can be extended by a unique reasoning technology to deliver an expressive and competitive reasoning engine for RDF, RDFS, and OWL 2 RL. The approach leverages labeled property graphs and the Resource Description Framework (RDF), which consider data as a graph. Neo4j's node labels can encode lightweight type schemas, while RDF Schema (RDFS) structures labels in hierarchies, and the Web Ontology Language (OWL) provides rule-like conditions to automatically derive new facts. GraphScale, a technology that empowers Neo4j with scalable OWL reasoning, uses abstraction refinement to build a compact representation of the graph suitable for in-memory reasoning. This approach has been shown to be sound and complete for all of RDF, RDFS, and OWL 2 RL, and it provides a competitive query performance with the previous datasets. The integration of Neo4j and GraphScale enables a transactional graph analytics system as well as a RDFS/OWL reasoning engine, which can service sophisticated semantic applications via Cypher over a materialized graph in Neo4j.

The General Data Protection Regulations (GDPR) will take effect on May 25, 2018, applying to all EU and foreign organizations handling personal data of EU residents. To meet GDPR requirements, companies must be able to track the movement of personal data across their enterprise applications, provide transparent tracking of personal information, and prove compliance with regulations. This is a challenging task due to the large amount of data involved, as personal data resides in many applications that span servers, data centers, geographies, internal networks, and cloud service providers. Graph database technology offers a natural approach to managing all of this data and its connections, enabling companies to build a GDPR compliance solution that meets these requirements.

This week in Neo4j saw the community come together to discuss various topics such as Russian Twitter Trolls, Machine Learning with Graph Databases, and Reddcoin Graph. The Neo4j GraphTour visited Berlin and London, featuring Michael, Ryan, Jennifer, and Mark at the London event. Featured Community Member Tom Zeppenfeldt was recognized for his work on building a development environment for interactive graph-based dashboards. On Reddit, Will Lyon and David Allen participated in an AMA about Russian Twitter Trolls, while Jon MacKay explained how to use Neo4j Desktop to store and analyze complex networked information. Additionally, Adam Cowley showed how to use Neo4j-OGM with Spring Boot, and Pol Dellaiera solved a problem of linking numbers such that adjacent sums are perfect square numbers using Neo4j. The week also saw the introduction of machine learning on graph databases, with David Mack connecting Neo4j with the Keras neural network library. Other notable mentions include Johannes Unterstein's community graph out of ReddCoin Twitter Tipbot usage and Tomaz Bratanic's analysis of the Paradise Papers using Neo4j's graph algorithms library. Looking ahead, the next week in Neo4j will feature a mix of Neo4j and customer speakers, as well as Graph-Powered Machine Learning and Chatbots with Neo4j and Amazon Alexa.

DevOps at LendingClub is a complex task due to its internal technology infrastructure, which includes multiple microservices and applications. However, graph technology helps manage and automate connections and dependencies through Neo4j. The team uses Neo4j for deployment and release automation, cloud orchestration, and managing the complexities of their infrastructure. They have loaded all their infrastructure into Neo4j's flexible schema, allowing them to easily add and modify views as needed. This has enabled real-time visibility into their infrastructure and allowed them to query their graph database at any time using ad hoc queries. With its scalability and flexibility, Neo4j has grown with LendingClub's infrastructure over the past three years, making it an essential tool for their DevOps efforts.

The study of network science is a crucial and rapidly evolving field that is impacting various domains, including social processes and biology. Dr. Aaron Clauset, an expert in the field, emphasizes the importance of understanding interactions and complexity in networks. He highlights the need for new tools to visualize and analyze network data, particularly in fields like computational biology where traditional approaches are being replaced by network-based methods. Machine learning is also playing a significant role in developing new techniques that can work with networks, but these methods often require careful consideration of failure modes and biases. Dr. Clauset's research group is working on probabilistic models for networks and ensemble methods to combine multiple algorithms for improved predictions. The ICON dataset provides a powerful tool for studying network structure, revealing new insights into the diversity of networks across domains. Ultimately, understanding network structure and its relationship to function is crucial for making progress in fields like neuroscience, ecology, and more.

This week in Neo4j brings several exciting updates and activities from the graph database community. The featured community member is Gábor Szárnyas, a research assistant at Hungarian Academy of Sciences, who has been actively participating in the openCypher and Neo4j communities. He's researching incremental query graphs and benchmarking such an engine, as well as analyzing multiplex networks. NBC News recently released a database of deleted Tweets from their investigation into Russian Twitter Trolls' influence on the 2016 US election, which can be explored using Neo4j. The community has also welcomed its 7,000th member to the Neo4j-Users Slack channel, where users can get help with Cypher queries and cluster configuration. Various developers have shared their projects and experiences, including Uwe Geercken's blog post on modeling Pentaho ETL jobs and flights using Neo4j, Bea Hernández's talk on using Neo4j with R, and Mark Henderson's Pypher query builder in Python. Upcoming events include talks by Joshua Yu, Yehonathan Sharvit, Tal Shainfeld, and Svetlana Yaroshevsky, and a tweet from Andrew Lovett-Barron about the potential of graph databases.

Dr. Clauset is an Assistant Professor of Computer Science at the University of Colorado Boulder, where he directs a research group that has developed the ICON dataset reference and published research on network structures, challenging some long-held misconceptions about complex systems. His team's work focuses on developing novel computational methods to understand messy datasets and applying these methods to solve real scientific problems in biological and social settings. Network science is evolving, diversifying, and expanding, enabling specialization but also reducing cross-disciplinary collaboration. However, the growth of disciplinary work around networks means that ideas from different domains are less likely to be exposed to each other, potentially leading to delays and reinvention. To address this, it's essential to study and discuss networks in general, creating common ground for researchers across disciplines.

In the retail industry, web-based retailers must handle scale and sophistication to remain competitive, a challenge Amazon has already mastered. Graph technology helps ecommerce and retail professionals overcome these challenges, including network and IT operations management. Traditional configuration management databases struggle to represent complex networks with cloud and on-premises components, but graph databases can provide a clear view of interconnected assets. System administrators can use graph databases to maintain a map of network assets, improve security, and detect vulnerabilities. Penetration testers also rely on Neo4j's index-free adjacency for predictable query response times, helping them uncover security issues in Active Directory and track potential attacks. By using Neo4j, retail IT organizations can offer seamless scale and sophistication to end-shoppers while keeping the network secure and protecting shopper data. The traditional relational databases are no longer sufficient to tackle retailer challenges within network and IT operations.

The Neo4j GraphTour is starting next week with events in Tel Aviv and Madrid, offering a chance to learn about the Graph Platform. Featured community member Tim Williamson, a Data Scientist at Monsanto Company, has been actively contributing to the Neo4j community for several years and presented on using graph databases for operationalizing insights from big data. The online meetup "Data Science in Practice: Importing and Visualizing Facebook Using Graphs" was held, showcasing how to import Facebook events into Neo4j and visualize them using d3.js. A paper on Reactome, a free, open-source, open-data knowledgebase of biomolecular pathways, was published, highlighting the use of Neo4j and Cypher to improve query efficiency. Additionally, various community members have been sharing their experiences with APOC, ETL tools, and Google Cloud Functions. The podcast featured an interview with Laura Drummer, Director of Software & Engineering at Novetta Solutions, who discussed building social networks using graph databases. Upcoming events include a mix of Neo4j and customer speakers in various locations.

This week in Neo4j has seen the release of Graph Gopher, an iPhone database browser for Neo4j, as well as a tutorial on how to import and query data from the Issuu Research Database using Neo4j. A featured community member, Suellen Stringer-Hye, was interviewed alongside Michael Hunger on the Leading Lines podcast, and Jesús Barrasa showcased how to build a graph of Thomson Reuters' OpenPermID dataset in his Neo4j is your RDF store series. Additionally, there have been tutorials on deploying Neo4j on Google Cloud Platform using Kubernetes, migrating data from MySQL to Neo4j, and creating a Microsoft Excel Add-In using VSTO to execute Cypher queries. Upcoming events include a talk on "Data Science in Practice: Importing and Visualizing Facebook Data Using Graphs" by Ray Barnard.

The tutorial demonstrates how to analyze a dataset from issuu.com using Neo4j, a graph database. It starts by explaining the concept of data analysis and the tools and techniques available for pursuing it. The authors use Neo4j to represent and visualize the data, leveraging its query language called Cypher to build queries and find answers. They import a JSON dataset into Neo4j, create nodes and relationships, add constraints and indexes, and perform various queries to derive insights from the data. The tutorial covers topics such as data modeling, querying, indexing, and constraint management in Neo4j. It provides examples of Cypher queries that can be used to analyze the dataset and extract meaningful information. The authors also discuss how to use APOC user-defined procedures to further manipulate the graph and improve query performance. Throughout the tutorial, they emphasize the importance of understanding data analysis concepts, Neo4j's data modeling capabilities, and the power of Cypher queries in extracting insights from complex datasets.