October 2015 Summaries

The author has been building educational resources using the example of an asset portal with Neo4j.rb. They have created a Rails engine called `graph_starter` to quickly set up a basic UI for entities, allowing users to override it when needed. The engine can be installed by adding it to the Gemfile and mounting the engine in the routes. Asset models such as Product and Vendor are defined using Neo4j.rb ActiveNode models with special methods to control how GraphStarter works. A rake task is used to import data, and once set up, a Rails server can be started to access the UI. The author plans to continue improving the `graph_starter` gem.

When building scalable applications, developers often struggle with choosing the right database technology to enhance functionality or performance. Polyglot persistence promises to allow developers to take advantage of different persistence layers to improve their application's capabilities. MongoDB and Neo4j are two popular databases that can be used together to achieve this goal. A document database like MongoDB is suitable for storing structured data, while a graph database like Neo4j excels at generating recommendations based on user interactions. However, integrating these two databases can add complexity to the application. To address this challenge, a new community project called Neo4j Doc Manager has been developed, which enables real-time synchronization of documents from MongoDB to Neo4j, allowing developers to build polyglot applications with minimal additional complexity.

The Global Impact Investing Network (GIIN) is a non-profit membership organization that supports the growth of impact investing, a field in which financial institutions and individuals invest their money for social and environmental impact. Researchers used Neo4j graph databases to visualize GIIN members' food and agriculture investments, revealing connections and potential opportunities for entrepreneurs, investors, and network managers. The graphs show overlap between GIIN members' portfolios, as well as co-investments with non-GIIN members, which could lead to new revenue streams for GIIN. However, the research is limited by publicly available data and caveats such as missing investment portfolios and sparse information on co-investors. Despite these limitations, Neo4j enabled the researchers to take initial steps in exploring impact investing networks and providing tools for entrepreneurs and network managers.

The article highlights various resources available for learning about and working with the Neo4j graph database, a key technology in the field of graph database technology. The community-driven platform offers articles, videos, and websites that provide insights and tutorials on using Neo4j for different purposes such as banking fraud analysis, organizational network analysis, and Java application scanning. The resources are designed to help users sharpen their skills with the world's leading graph database, and the community is encouraged to share their knowledge through Twitter using the #Neo4j hashtag. By joining the largest graph database ecosystem, users can access a wealth of information and connect with other Neo4j enthusiasts.

Neo4j 2.3 is a significant release that focuses on building bigger and better applications, with new features such as a fully off-heap cache, faster query optimizer, schema improvements, and official support for Docker. The release introduces several key enhancements including intelligent applications at scale, eliminating the JVM-based object cache, smarter Cypher query planner, and improved performance in concurrent read scaling. Additionally, Neo4j 2.3 includes features such as property existence constraints, Spring Data Neo4j 4.0, Windows PowerShell support, faster backups, upgrades, and bulk loading of data, making it an exciting release for developers and users alike.

The text discusses the need for a common graph query language, building on the analogy with SQL. It highlights the benefits of having a shared language, including reusability across platforms and encouraging healthy competition. The author believes that Cypher is the most suitable candidate for this role, citing its wide adoption, real-world validation, and human-readable syntax. The openCypher project aims to make Cypher available to everyone, with the goal of promoting growth in the graph processing and analysis space, much like SQL did for relational databases. The project promises to deliver four key artifacts: reference documentation, a technology compatibility kit, a reference implementation, and a language specification, all under permissive licenses. The author invites readers to join the growing openCypher community and contribute to the evolution of Cypher.

The speaker just made a great point. Did you write it down? That slide just showed a perfect summary. Did you take a photo? You now realize what your data model should be. Can you sketch it out? Unless you’re adept at juggling laptops, smartphones and physical notebooks, you may have only been able to pick one or two of these epiphany moments. Until now. If you’re attending GraphConnect San Francisco, it’s time to try Paper by FiftyThree. Put It on Paper Paper for iPhone and iPad allows you to capture your ideas with unbeatable speed and ease. Whether it’s writing a note, taking a photo, drawing a sketch — or any combination of those — Paper aims to be the best tool for you to do that. And it’s all in a single app, on a single device. As you attend each session of GraphConnect, use Paper to collaborate with other attendees. Follow the GraphConnect account to find official conference notes, then use your iPhone or iPad to capture and share your own! (Tag them #GraphConnect, and we’ll automatically re-share them from the GraphConnect account.) So, What Is Paper? Ideas. They strike at any moment and come in many forms. Screenshots or photos that we want to remember, checklists, loose sketches, stream-of-consciousness text, diagrams, emails to ourselves – whatever your idea and whenever it hits, Paper’s cutting-edge tools will capture and communicate it with speed and polish. Paper is a beautiful way to organize your thoughts, allowing you to capture ideas and store them like a wall of sticky notes, neatly arranged and connected to each other. Whether you’re remodeling your home, taking lecture notes, writing a travel list or creating a presentation, Paper is the perfect springboard for your ideas. Here are just a few more of Paper’s awesome features: Swipe to Format Take, Annotate and Spotlight Photos Share Your Work Pencil Built for touch, Paper makes creating and organizing notes lightning fast. Simply swipe your finger to create a To-Do list, or tap and hold items to reprioritize your list. It’s never been this easy to create shopping lists or organize talking points. Photos are an incredibly powerful form of capturing ideas and inspiration in the world. Paper now lets you take photos, spotlight areas to call out details and write directly on top of them. Ideas become meaningful once the world knows them. Paper makes it simple to export your ideas to PowerPoint, PDF, Keynote and Dropbox, or share them with friends across social media. Connect Pencil – FiftyThree’s award-winning Bluetooth stylus – for a faster and more natural way to sketch out your ideas on Paper. Learn More at GraphConnect San Francisco Want to learn more? I’ll be sharing more about Paper’s backend — powered by Neo4j — in my GraphConnect talk, “Advanced Neo4j at FiftyThree.” Expect a deep dive into technical details and lessons learned around locking, consistency, scaling, and more. See you there, and don’t forget to download Paper and follow the GraphConnect account before the conference! I look forward to seeing your beautiful notes. Click below to register for GraphConnect San Francisco and join Aseem Kishore and many other advanced Neo4j users at the world’s leading graph database conference.

The application, built with Tom Sawyer Perspectives, displays crime incidents in Seattle between 2007 and 2009, using a Neo4j graph database. The visualization shows relationships between offenders, offenses, and their connections to specific times and locations. The application offers various views, including a timeline view that allows users to explore data spanning relationships, locations, and time. It also provides over thirty powerful graph analysis algorithms to interpret the data and pinpoint relevant information, such as identifying top criminal offenders and middlemen in the network.

The text discusses the application of graph databases in master data management (MDM), enabling a rich, multi-dimensional view of entities across domains and business units. It highlights the importance of finding a balance between storing data in the graph and leaving it solely in enterprise systems to minimize costs. The CAP theorem is mentioned as a framework for designing architectures that prioritize consistency, availability, or partition tolerance. Spectrum Data Federation is introduced as a solution that allows businesses to define logical views of data, map them to physical sources, and publish virtual data sources for use throughout the system. This enables agility in managing data, with the ability to seamlessly integrate federated data with graph databases. The text also explores the challenges associated with getting a project off the ground and the flexibility offered by designing a hybrid system from the perspective of the CAP theorem. Ultimately, it suggests that finding the right balance between storing data in the graph and linking it virtually to federated data is crucial for success.

The International Consortium of Investigative Journalists (ICIJ) launched a major investigation into the Swiss Leaks scandal, which revealed over $100 billion spread across about 100,000 clients in the Swiss branch of HSBC. The investigation involved over 150 reporters from more than 45 countries analyzing a dataset composed of over 60,000 files, including account files and company structures with multiple layers of ownership and interconnectedness. ICIJ used a graph database, specifically Neo4j, to organize and explore the massive leak, which included more than 270,000 nodes of data and 400,000 relationships. The investigation showed that HSBC staff were complicit in these seemingly questionable transactions and layers of accounts, using tactics such as code names and secrecy to obfuscate ownership. Linkurious, powered by Neo4j, was used to visualize the data and make it easier for journalists to analyze and understand the results. The investigation is a significant example of how graph databases can be used in big data journalism to uncover complex stories and reveal hidden patterns.

Neo4j is a graph database that can be intimidating to potential users, with many people citing common misconceptions about its capabilities and suitability for their work. To overcome this, the concept of side projects can be used as an entry point, allowing individuals to explore Neo4j in a low-risk environment and gain experience with its features and applications. By building a project that aligns with their interests and passions, developers can learn new technologies, demonstrate expertise, and potentially enhance their careers. The text suggests various ideas for side projects involving graphs, such as analyzing movie preferences or creating a graph-driven blog, and provides examples of real-world projects like OmNomHub to inspire others. Ultimately, making time for a side project can be extremely rewarding and is encouraged by Neo4j's free trial and the GraphConnect San Francisco conference.

The first officially supported version of Spring Data Neo4j, a project that enables developers to work with Neo4j embedded in the Spring Framework for enterprise and personal projects, is now available. This new release was made possible by the efforts of GraphAware's team, including Vince, Adam, Luanne, Michal, and Pivotal's Spring Data project lead Oliver Gierke. The project started as a personal initiative in 2009 and has undergone significant changes since its inception, with the goal of providing an object-graph-mapping library that makes extensive use of Neo4j's Cypher query language. The new version, Spring Data Neo4j 4.0, is built on top of a plain Java-Object-Graph-Mapper and provides pluggable transport layers for future development. It also introduces support for the beloved Spring Data Repositories and provides an easy-to-use example project repository as a starting point for developers to get started with the new release.

The GraphConnect San Francisco event features Lightning Talks, which provide a platform for community members, Neo4j employees, and sponsors to share rapid-fire presentations on various graph database topics. These talks are designed to be short, lasting around 10-20 minutes, making them an ideal way to introduce new concepts or illustrate quick hacks without requiring a long time commitment. The schedule includes sessions on a range of subjects, including project planning, Neo4j on .NET, Meteor.js powered by Neo4j, graph visualization, and more, ensuring that attendees can find something that interests them, regardless of their background or expertise in the field.

The development of CyGraph, a tool for cyber warfare analytics, visualization, and knowledge management, aims to address the challenges of information overload in security analytics. This tool brings together isolated data and events into an ongoing overall picture for decision support and situational awareness, prioritizing exposed vulnerabilities mapped to potential threats in the context of mission-critical assets. CyGraph incorporates an attack-graph model that maps the potential attack paths through a network, including various network attributes that potentially contribute to attack success. The dynamically evolving attack graph provides context for reacting appropriately to attacks and protecting mission-critical assets. Leveraging Big Data Architecture, CyGraph uses a flexible property-graph formulation implemented in Neo4j, a NoSQL database optimized for graphs, to model schema evolution with available data sources and desired analytics. This approach enables the creation of additional nodes, relationships, and properties without requiring schema changes or other database renormalizing. With its comprehensive knowledge base, CyGraph provides insight into mission impact, including potential attack-pattern relationships that fill in gaps between known vulnerabilities and threat indicators.

Linkurious, a Neo4j user interface tool, has released an updated version of its Enterprise platform with improved features for sharing and working with graph data. The new release allows users to publish interactive visualizations online, which can be accessed by anyone and easily collaborated on. Additionally, the platform now provides easier integration with third-party applications through simple URLs, enabling users to create new visualizations directly from these links. This update also includes production-ready graph visualization capabilities, supporting challenging security constraints and generating an audit trail of all operations made within the platform. The company will unveil this new version at GraphConnect San Francisco next week.

The countdown to GraphConnect San Francisco has begun, with just 7 days left until the event. KeyLines will be showcasing its latest release, KeyLines 2.11, which includes a raft of new features and improvements for Neo4j graphistas, developers, data scientists, and end-users. The focus on graph visualization is highlighted, particularly in geospatial graphs, where KeyLines Geospatial offers an exploration tool to empower users to ask more sophisticated questions. The tool has been demonstrated with a dataset showing activity variations associated with both time and location, such as the Boston Hubway cycle sharing scheme data, providing insights into usage patterns and balancing act for bike distribution truck routes. KeyLines will be present at GraphConnect San Francisco, offering advice on how to start graph visualization projects and networking opportunities with other leaders in the field.

Graph visualization is a critical aspect of software development, especially for Neo4j users. Tom Sawyer Software, founded in 1992, has been a pioneer in graph visualization and layout technology. The company's flagship product, Tom Sawyer Perspectives, provides a comprehensive enterprise software stack with customizable data visualization applications. With expertise in graph technologies, Tom Sawyer differentiates itself from competitors by offering advanced features such as schema management, federated data integration, and synchronized rule-driven views of data. The company is expanding its offerings to provide businesses with scalable and flexible applications that can decipher and sift through their rapidly accumulating data. As a close partner of Neo Technology, Tom Sawyer Perspectives includes full integration to connect to Neo4j databases, allowing users easy access to their data. Looking ahead, the company plans to improve its software, add new features such as a graph data model, and enhance enterprise capabilities, including user and role management.

When Christian, Kai and I launched Structr 1.0 in September 2014, more than a year of intense development followed, resulting in over 12 months of activity as we headed to GraphConnect San Francisco next Monday. Structr is known for its speed, allowing users to create custom applications quickly, with typical tasks taking just a couple of minutes to complete. The secret behind this speed lies in its architecture, which leverages Neo4j's flexibility and performance, reducing the complexity of the system. By storing data in graph databases, Structr automates more processes and makes it easier to develop applications, making it an automation platform for typical application development tasks. Structr 2.0 has introduced several new features, including improved support for CMIS, filesystem UI, SSH/SCP access, JavaScript server-side scripting, custom schema methods, and more. The company has experienced significant growth, with the number of business opportunities increasing steadily, and has successfully completed larger projects, such as a research study for a German federal authority and a supply-chain data management system for Schleich GmbH. With GraphConnect San Francisco on October 21st, users can join us to learn more about Structr and its capabilities.

I'm a developer who knows what it's like to set up Neo4j in production, having faced challenges with graph data modeling and coding Cypher. I co-founded a Neo4j hosting startup to help others avoid the mistakes I made. GrapheneDB is our solution, making Neo4j hosting easy from the start, supporting smaller companies, startups, and individual developers. We take care of operations so you can focus on development, offering 24/7 expert support and a free plan to get started with Neo4j without installing it. By choosing us, you'll avoid the complications of running your own server and be able to focus on building applications with Neo4j.

The author has been working on linking resident records from the 1901 and 1911 Irish censuses together to identify the same residents. The data model used for the imported data is linear, with houses and residents as nodes. To improve record linkage scoring, the author has developed a method that uses local data space around objects to match records. This involves creating relationships between residents based on their properties and using graph-based comparisons to evaluate family relationships. The author has implemented this method using Neo4j, a powerful tool for analyzing data relationships naturally and quickly. By leveraging Neo4j's ability to create relationships on the fly, the author is able to find the best solution quickly and efficiently.

The authors of this article worked with Neo4j, a graph database, to improve the performance of their company's data management. They chose Neo4j for its ability to handle complex relationships and large volumes of time-sensitive information. The team learned about graph data modeling, indexing, and query optimization techniques to enhance performance. They implemented a time-based versioned model using Neo4j's native support for dates and times, which allowed them to keep an audit trail of changes to the model. The authors also created indexes on key properties, parameterized their Cypher queries, and used the Cost planner to optimize their queries. They found that these techniques significantly improved query execution times, reducing database hits by up to 90%. The team also discussed the challenges of working with the Neo4j team from Australia, including logistical issues and limited external speculation about upcoming features. Additionally, they shared their experience with integrating Neo4j with Java 8 and Spring Boot, implementing a custom migration tool called Flygraph. Overall, the authors found that working with Neo4j was an rewarding experience, despite its challenges and limitations.