May 2019 Summaries

In an emerging field known as RegTech, graph databases like Neo4j are being increasingly used to tackle complex compliance problems by analyzing larger networks and connections, enabling the identification of suspicious patterns and relationships that were previously difficult to detect. The founder of KERBEROS Compliance, Julian Schibberges, attributes his choice of Neo4j to its inherent understanding of graph databases and its company's origins in this field. By leveraging Neo4j's capabilities, Schibberges and his team have been able to gain new insights into complex networks and structures, leading to a deeper understanding of compliance issues. As the RegTech industry continues to grow, Schibberges believes that graph databases will play an increasingly important role in solving compliance problems, particularly in identifying and verifying relationships between companies, people, and documents.

StreamSets is a data integration tool that was founded in 2014 to address the growing need for a tool that could handle streaming and big data. The company has more than two million downloads worldwide and offers a highly-flexible platform for DataOps that operationalizes the flow of data around enterprises. StreamSets can connect to over 50 different data stores and streaming systems, making it a Swiss Army knife for data. One of its customers, a financial institution, used Neo4j for analysis, and StreamSets found that working with Neo4j was straightforward. The company built a simple MDM data architecture that reads data from MySQL, Salesforce, and flat .CSV files, unifying them in Neo4j to create a graph of the data. This allows for powerful Cypher queries that perform searches that cannot be done in a relational database. StreamSets also uses Neo4j's JDBC driver, which is high-quality and performs well, and has been used by other companies such as GlaxoSmithKline and Cox Automotive to bring together data from various systems. The company's tool can read data from a wide variety of sources, write to Neo4j via Cypher and the JDBC driver, and has been used for use cases such as cyber security, IoT, and data lake replatforming.

Graphs are a versatile and dynamic tool that can help solve complex problems in various domains, including government use cases. In this blog series, we explore how graphs can be used to analyze complex data sets and provide immediate answers to critical questions. Lockheed Martin Space is one such organization using Neo4j, a graph database, to connect all its data silos and gain visibility into the entire product ecosystem. By storing relationships between data and systems in a graph database, users can submit queries that traverse the database, yielding immediate results instead of weeks of research. This enables organizations to perform impact analysis, drive efficiencies for troubleshooting, and identify potential process improvements. With Neo4j, government agencies and organizations can overcome their toughest challenges and fulfill their mission.

This week's episode covers various topics in the Neo4j community, including graph technology and its impact on AI, Neo4j logging and monitoring with the ELK stack, a new release of the Halin monitoring tool, talks at KubeCon and Spring IO, and an overview of Neo4j Streams and its integration with Apache Kafka. The episode also features a spotlight on Simon Goring, a featured community member who is working on large spatio-temporal data sets and bridging end-users and developers to improve cyberinfrastructure. Additionally, there's a tweet of the week celebrating LARUS' 15th birthday.

I recently returned from Venice, Italy, where I had the pleasure of celebrating the 15th anniversary of my friends and partners from AgileLarus. The event was a great success, with presentations on graph algorithms and integrations with Neo4j and Apache Kafka. Our team enjoyed sharing knowledge with students at the University of Venice and presenting to partners and customers of LARUS. I also had the opportunity to see da Vinci's "Vetruvian Man" and other rare drawings in the Accademia Gallery, which was an amazing experience. The event marked a significant milestone for LARUS, as they became one of Neo4j's first partner, training partner, and solutions providers. Since then, we have been working together on various projects, including the Neo4j JDBC driver and Apache Zeppelin integration. I'm looking forward to our continued collaboration, which will include Neo4j GraphQL integration and official support for Neo4j customers with Confluent's Kafka integrations. Working with Lorenzo and his team has been an enjoyable and productive experience, and I hope we continue our partnership for a long time.

The conversation revolves around graph theory and its application in a real-world scenario. A dog named Armstrong protects his territory from an evil Kitty Army, and to optimize his patrol route, he uses a graph database approach inspired by Leonhard Euler's Seven Bridges of Königsberg problem. By modeling the territory as a graph with intersections and streets as nodes and relationships, Armstrong can track his walks and identify areas that need more attention. He then applies a shortest path algorithm to find an optimal route from his home to a target intersection, taking into account the "newness" or time since the last walk on each relationship. The algorithm output is used to plan a return route, avoiding previously walked paths and including only unplanned intersections. This example demonstrates how graph databases can be used for pathfinding in various scenarios, such as Armstrong's daily patrol routes.

In the presentation, Jessica Dembe and Patrick Elder from Blackstone Federal discuss how they used Neo4j to automate formerly inefficient processes for the United States Department of Homeland Security (DHS) in their Enterprise Architecture Information Repository (EAIR). The EAIR is a database that stores information on various systems used by DHS, but it was becoming difficult to navigate due to the large volume of data and disparate relationships between different systems. Dembe and Elder found that Neo4j's graph visualization capabilities made it easy to understand and access the data, allowing them to connect disparate pieces of information and show how they relate to each other. This enabled them to provide a more intuitive and user-friendly interface for enterprise architects and decision-makers, who could now easily navigate the complex relationships between systems, investments, and goals. With Neo4j, Dembe and Elder were able to transform themselves from an "information aggregator" to an "information provider", providing a more comprehensive and actionable view of DHS's enterprise architecture.

Graphs are versatile and dynamic, enabling organizations to solve complex problems that cannot be solved in any other way. Using real-world government use cases, this blog series explains how graphs are used to analyze and visualize cybersecurity threats, track vulnerabilities, and provide situational awareness. By leveraging Neo4j's graph database capabilities, organizations can perform deep complex queries and gain insights into their data. The tool, CyGraph, developed by MITRE Corporation, brings together isolated data points into an ongoing overall picture for decision support, prioritizing exposed vulnerabilities and suggesting the best course of action to respond to attacks. Graph databases are versatile and enable government agencies to fulfill their missions in innovative ways.

This week's featured community member is Mayank Gupta, a Certified Neo4j Developer who created a YouTube channel creating Neo4j tutorial videos in Hindi. Igor Rozani taught the meetup about the Pokémon universe using graphs, while Andrea Santurbano showed how to produce and consume Kafka data streams directly via Cypher with Streams Procedures. Tomaz Bratanic explored the Depth First Search algorithm, and Jennifer Reif discussed why you'd use Kettle for Neo4j data import. The community also shared various tweets and blog posts on topics such as influencer detection in the Graph Database community and performance differences between "Relationship as Types" vs "Relationship as Properties".

Graph technology is a non-relational technology that emerges from relational databases, offering a different tools-for-different-jobs approach. It allows traversing and analyzing relationships between entities in various domains such as delivery routing, social graph traversal, and IoT use cases. The symbiotic relationship with other technologies like artificial intelligence can enhance its capabilities. Graph technology needs to go mainstream, requiring developers to be more conscious of the wider context in which they build projects for ethical and self-interested reasons, always asking "why?"

The American Enterprise Institute (AEI) uses a Neo4j database supplemented by a cloud search index, as well as AWS and Azure document stores, to analyze conflicts in the Middle East, Africa, and Europe. They partnered with the Institute for the Study of War to create the Critical Threats Project, which aims to model a transformation in open-source intelligence generation and use. AEI's work includes mapping networks such as the Salafi-jihadi network in the Sahel region of Africa, uncovering previously unknown connections between the network and local tribal and ethnic groups. The team found that graph databases like Neo4j are optimal for integrating and analyzing diverse datasets, and they concluded that these systems should become core backbones for analytical organizations within the government and beyond. They also emphasized the importance of designing software tools to match existing workflows as precisely as possible, and the need for seamless integration with user interfaces.

We are excited to announce that GraphConnect, a major conference on graph technology, will take place in Times Square, New York City from April 20-22, 2020. The event will feature two full days of conference sessions, doubling the number of excellent sessions on graph technology and use cases. This year's GraphConnect is part of a larger series of events, including GraphTour stops throughout Europe and North America, as well as a global Neo4j Online Developer Expo & Summit in early October. The event promises to bring together the Neo4j community for premier keynotes and deeply technical talks on graph technologies. We are proud to offer an even bigger and better GraphConnect this year, following a series of successful events in previous years.

Graphs are a versatile and dynamic tool that can solve complex problems, as demonstrated by real-world government use cases. The U.S. Army and In-Q-Tel (IQT), a nonprofit working with America's intelligence agencies, have successfully utilized Neo4j to address challenges in supply chain management, maintenance cost management, and technology evaluation. IQT's network of connections with various entities allowed them to evaluate tech innovations across multiple sectors, but manual processes were time-consuming and ineffective. Recognizing the need for automation, they adopted Neo4j, a graph database that enabled them to break down product sets into core capabilities, identify relationships between objects, and develop technology solutions by searching through vast amounts of data integrated under one common taxonomy. Thanks to Neo4j, IQT's technical staff can now generate new ideas faster and better evaluate technology trends, ultimately enhancing their mission fulfillment capabilities.

This week, developers are learning about using graphs for real-time inventory management and network topology automation, exploring public contracting data, and filtering connected dynamic forms. They're also diving into Neo4j-OGM and Spring Data Neo4j, with a focus on choosing unique identifiers for nodes. The RDBMS to Graph page has been overhauled, providing an ultimate resource for learning about relational data import options in Neo4j. Additionally, community member Francesca Ferretti is featured, showcasing her expertise as a Cypher nerd and graph hero. There's also a crash course on analyzing open contracting data using Neo4j by Dagoberto José Herrera Murillo, and a tweet of the week highlighting the versatility of graphs even in everyday objects like hotel carpet.

Neo4j Bloom is a data visualization tool that can illustrate fraudulent patterns in financial transactions, as demonstrated by Managing Editor Jocelyn Hoppa in a previous video. The platform features a diverse range of users from around the world who are utilizing graph database technology for various applications, including applied machine learning research and advising, understanding connections between different aspects of eating, and mapping out privileges and attack paths in enterprises for cybersecurity. These individuals share their experiences and insights on how Neo4j is being used to drive innovation and solve complex problems in fields such as finance, healthcare, and technology. With the launch of the Global Graph Celebration Day, Neo4j has brought together a community of users who are pushing the boundaries of what is possible with graph database technology.

The U.S. Army uses Neo4j to manage its complex supply chain and maintenance costs, reducing infrastructure costs and improving security. With Neo4j, the Army has a more flexible and robust view of parts requirements and costs across systems, components, and subcomponents, enabling rapid storage, exploration, and visualization of logistics and cost data. This allows for deeper analysis and faster insights, saving time and resources for analytics teams. The graph database's flexibility and scalability make it an ideal solution for government agencies facing complex challenges.

This week's focus is on the financial services industry and its applications of Neo4j graph database technology. A full-stack Neo4j Certified developer, Michael Porter, shares his expertise on using Neo4j with GRANDstack.io to analyze complex data in the oil and mineral industry. In another development, Joe Depeau discusses graphs in banking integration with AI and machine learning technologies, while Andrea Santurbano explains efficient Neo4j data import using Cypher scripts. The topic of IoT data modeling is also explored through a worked example by Muntasir Joarder. Furthermore, Amy Hodler shares her work on graph-based AI and testing software at Mapillary, and the latest release of Halin, a Neo4j monitoring tool, introduces new features such as a tasks screen, log file viewer, and advisor improvements.

Compliance with anti-money laundering regulations is a significant challenge in industries like sports-betting and real estate, requiring strict auditing and reporting requirements. KERBEROS Compliance built a solution using the Neo4j graph database and Structr application platform to ease compliance for these companies, leveraging visualization capabilities to effectively show complex data to customers and lawyers. Christian Tsambikakis, founder of KERBEROS Compliance, chose Neo4j due to its ability to visualize large amounts of data, citing the need to demonstrate data in an effective manner to stakeholders. He believes graph databases have yet to reach their full potential in the non-financial sector, particularly in industries like compliance, where companies are now beginning to adopt these technologies.

The text discusses how a graph database like Neo4j can be used to model and query the London Underground network, allowing for efficient pathfinding and journey planning. The author created a simple "Meta-Graph" representing stations as nodes connected by relationships labeled with line names, and then loaded data from an external source to populate the graph. They demonstrated how to find the shortest paths between two stations using geospatial queries and weighted shortest path algorithms. The author also touched on potential extensions to this system, such as incorporating bus and rail connections, limiting transfers, or returning cheapest journeys by zone. The text highlights the benefits of using a graph database for real-world network analysis and provides a glimpse into the complexity and scale of the London Underground network.