January 2020 Summaries

Ernst & Young has brought graph technology into the financial services realm, utilizing it for various use cases such as data lineage, prospecting, recommendation engines, and customer segmentation. Omar Azhar, Senior Manager Advanced Analytics at EY, discusses his work with Neo4j, a popular graph database, which he uses to solve graph-type problems prevalent in financial services. He highlights the benefits of using graphs, including visualization of complex data structures, identification of critical systems or applications, and analysis of trade routes to detect potential trade embargoes. Azhar also emphasizes the growing interest in graph technology among executives and clients, driven by its ability to address complex data-related problems. Looking ahead, he anticipates significant advancements in graph-based machine learning, particularly in areas like topology and network science.

Global Graph Celebration Day (GGCD) is an annual event held on April 15th to honor Leonhard Euler, the Swiss mathematician who invented graph theory. The Neo4j community and graph enthusiasts worldwide organize events to celebrate Euler's birthday, which has become a global movement with over 60 events in six continents. The event encourages participation through organizing local events, presenting or helping organize alongside meetups, attending events, and participating in various activities such as hackathons, games, and whiteboard brainstorming sessions. The Neo4j Developer Relations Team provides resources and support to help organizers plan their events, which are listed on the GlobalGraphCelebrationDay.com website and included in a community graph.

The Neo4j GraphTour is a series of free events being held across Europe, starting in February, where speakers from the Neo4j team and local customers will share their experiences with graph database technology. The one-day event will cover topics such as native graph technology, graph algorithms, and how to improve operations, ensure compliance, increase revenue, and catch fraudsters using Neo4j's Graph Platform. Local customers will showcase their success stories, and attendees can also receive one-on-one help from Neo4j consultants at the GraphClinics. The event is free to register, and more cities are being added to the itinerary, with locations in the US to be announced soon.

Healthcare organizations often struggle to see the entire system due to disconnected data, leading to high costs and inefficiencies in making quality predictions for individual care improvements. Graph databases, which are underutilized in healthcare, offer a promising solution by effectively connecting diverse and variable healthcare data, which traditional relational databases struggle to manage. Optum, part of UnitedHealth Group, is working to integrate disparate datasets from acquisitions into a comprehensive healthcare knowledge graph, aiming to improve predictive models and patient outcomes. The organization highlights the challenges of data variability, privacy, and integration across silos while emphasizing the importance of explainable AI and model-based machine learning in healthcare. Graph technologies, combined with other approaches like natural language processing and machine learning, are seen as critical to addressing complex healthcare problems and fostering cross-domain solutions that enhance data connectivity and access. Through metaphors like the neighborhood walk, open world assumption, and knowledge triangle, Optum seeks to communicate the advantages of graph databases to healthcare executives, who play a crucial role in deciding the systems to build.

Neo4j is being used to solve a "who done it?" problem and build various projects, including a QuickGraph of the Australian Open tennis tournament. The Neo4j community has been actively engaging with the platform through online meetups, blog posts, and presentations at conferences like GraphConnect. Adam Cowley, a featured community member, is working as a Senior Professional Services Consultant at Neo4j, sharing his knowledge on topics such as temporal types, sharding, and journey planning with Cypher. Additionally, Jennifer Reif has created a developer guide on managing multiple databases in Neo4j, while Hongping Liang visualizes data from the Personal Genome Project using the Neo4j Browser. The community is also exploring features like sharding and QuickGraphs to build various projects and applications.

The text appears to be a summary of various developer-related content, including tutorials, blog posts, and conference talks. Pat Brown and Karim Shehadeh discuss using Neo4j to discover the "soul" of a product at Under Armour, while Nathan Smith finds an optimal solution for Sudoku using Neo4j. Adam Cowley builds a real-time UI with Vue.js and Kafka, and Luanne Misquitta explains post-union processing. The text also mentions community members, including Oscar Arcia, who is featured as this week's member, and Michael Porter, who will be the next featured member. Additionally, the text highlights various projects and resources related to Neo4j, such as graph databases, meetups, and tutorials.

The interview with Jennifer Reif, a Neo4j developer, highlights the unique aspects of graph databases like Neo4j. She shares her excitement about spurring "aha moments" and contributions from the Neo4j community, which she believes is essential for building a new perspective and thinking outside the box. Jennifer works on various integration projects, including redesigning developer guides to better meet user needs. She looks forward to exploring reactive capabilities in Spring Data Neo4j and showcasing its benefits to other developers. Her favorite aspects of Neo4j are the numerous integrations with various tools and systems, as well as the flexibility of its model and Cypher language. Jennifer finds graph technology particularly useful in medicine and science, analyzing connections between atoms and molecules, and mapping IT networks. She emphasizes that learning Neo4j requires adapting to new concepts and thinking creatively, which can lead to "aha moments" when users grasp these ideas. Jennifer advises newcomers to approach Neo4j with an open mind, embracing its unique aspects rather than trying to fit it into existing knowledge gaps. The Neo4j community is highlighted as a valuable resource for contributions, blog posts, and user feedback, which helps the development team understand user needs and identify areas for improvement. Ultimately, Jennifer sees graph technology growing in importance as the world becomes increasingly interconnected, with artificial intelligence and machine learning playing a significant role in its future development.

Alexander Jarasch, head of data and knowledge management at the German Center for Diabetes Research in Munich, Germany, discusses the challenges of integrating diverse research data to better prevent and treat diabetes. The organization is using a Neo4j graph database to connect and visualize data from various disciplines, locations, and species, enabling researchers to answer complex questions and identify new insights into diabetes prevention and treatment. The goal is to extend the graph with publicly available literature data, including peer-reviewed articles, to improve pattern recognition and machine learning techniques for individualized treatment and prevention of diabetes.

The German Center for Diabetes Research is using a graph database, specifically Neo4j, to connect and visualize data from various disciplines and locations. The goal is to prevent and treat diabetes by analyzing large amounts of data, including genomic, proteomic, and metabolomic information, as well as clinical trial data. The researchers aim to identify patterns and connections between different types of data to better understand the metabolic nature of diabetes and develop new treatments. They are also working on extending their graph database with publicly available literature data to improve pattern recognition and machine learning techniques for disease prevention and treatment.

The NODES 2019 conference featured a series of videos showcasing various applications of graph algorithms and Neo4j. Joe Depeau explained how to apply graph algorithms through classic teen films, while Nathan Smith attempted to solve a Sudoku using the K-1 Coloring Graph Algorithm in Neo4j. Other contributors included Jesús Barrasa analyzing Christmas messages from European heads of state, Mike Solomon exploring Twitter engagement data, and Greg Shackles analyzing .NET dependencies with F# and Neo4j. The conference also highlighted the work of featured community member Michael Porter, who reached the top spot on the Neo4j Ninja leaderboard.

Our How-To Video Series on Neo4j development aims to help beginners get started with the platform, covering topics such as Neo4j Desktop and Neo4j Browser. The series provides a comprehensive introduction to creating databases with Neo4j AuraDB and other essential tools for graph-based development. With new episodes added weekly, viewers can subscribe to our Neo4j YouTube channel to stay up-to-date on the latest graph tech goods.

A new year is a great time to reflect on the past and look ahead to the future in the graph database space. According to Neo4j colleagues Amy Hodler, Michael Hunger, and Amit Chaudhry, key predictions for 2020 include increased adoption of graph-feature engineering to boost machine learning accuracy, commercial applications of graph embeddings beyond image analysis, and a shift towards considering graphs as a standard format for adding context to machine learning. The data supply chain will play an increasingly important role in ethical and responsible AI, with frameworks like the EU Ethics Guidelines being developed. Developers will face challenges such as managing complex cloud systems, ensuring accountability and responsibility around algorithmic biases, and adopting new features and languages in popular runtimes. Graph analytics will grow in importance, and GQL (Graph Query Language) is expected to emerge as a standard for graph database vendors, leading to increased interoperability, portability, and skill-set availability.