February 2023 Summaries

The text is a newsletter or promotional material for the Neo4j community, highlighting various upcoming workshops, events, and releases related to graph data science and Neo4j. It features speakers and topics such as Cypher data modeling, routing applications, cybersecurity threat intelligence analysis, healthcare microbial knowledge graphs, and more. The newsletter also includes announcements about new releases, courses, and tools like Hume 2.17 and Grapho, an XR visualisation tool for graph data manipulation. Additionally, it mentions a tweet from Tomaz Bratanic showcasing a repository with a knowledge graph-based chatbot using GPT3 and Neo4j, and lists upcoming events such as AuraDB sessions, Neo4j Live: Infrastructure Mapping, and meetups around the world.

GraphSummit Series is a global education and networking event series for data leaders, developers, and data scientists, with European leg starting in London and visiting 14+ cities across Europe. The event will include the latest updates from Neo4j, customer stories, hands-on workshops, and opportunities to network with peers and experts. Attendees can expect to learn about graph technology, its applications, and how it drives businesses forward, as well as hear success stories from customers such as Technip Energies and La Poste. The event is designed for business and technical professionals at all levels of graph experience, with limited places available, so registration is recommended.

The Neo4j drivers team has introduced a new API that simplifies the experience of new users getting started with Neo4j, offering an equivalent of an automatic transmission mode to the existing manual transmission-like interface. This new API shields many complex aspects from the user while retaining them for sophisticated use cases and is idiomatic depending on the language. The changes include results being delivered to memory without requiring an additional step, session and transaction objects are called by executeQuery automatically, retry behavior is the same but without requiring lambdas or callbacks, bookmarks are passed automatically, and configuration options are set by default but accessible for change using a QueryConfig object. The new API reduces code complexity and provides control to the developer as their requirements change, with plans to remove the experimental tags and promote its use for new users in the future.

The latest iteration of Neo4j's Partner Solution for AWS provides a quick and easy way to deploy a fully-managed Neo4j cluster on the AWS platform, allowing customers to set up a Neo4j cluster in just a few clicks without complex configuration or management. The updated solution now includes Neo4j 5 and offers customization options, making it suitable for various use cases. It deploys an AWS VPC with three public subnets, a network load balancer, and an internet gateway, and can be deployed using the AWS CloudFormation framework. The solution provides features such as graph data science platform and Bloom data visualization framework, enabling customers to experiment, test, and experience the power of Neo4j.

With a focus on data relationships, Arcurve's Director of AI and ML Technology and Senior Data Scientist use Graph Data Science to combine structured and unstructured data, offering a more natural way to answer critical data science questions like prioritization, anomalies, and predictions. They leverage Neo4j Graph Data Science to break complex files into data, find patterns in the resulting structure, and create comprehensive "knowledge assets." These knowledge assets can help organizations identify similarities between legal documents, improve the accuracy of financial impact measures, and prioritize abandoned well sites for remediation based on public safety and environmental risk.

Spring Data Neo4j is a part of the larger Spring Data family and provides easy configuration and access to Neo4j Graph Databases from Spring applications. The platform has been migrated back to the Discourse platform after receiving feedback, and users can expect improved user experience over the coming weeks. To promote engagement, local events are being rolled out in addition to online streams, featuring various speakers and topics such as data lineage, knowledge graphs, GraphQL, and graph data science. Additionally, resources like tutorials and books are available for those interested in learning more about Neo4j and its applications.

Tom Asel from Tangible Concepts joined me recently to discuss Wardley Maps, a visual representation of a business's value chain. A Wardley Map shows the progression from raw materials to customer value and is used to analyze and understand a system's components. The map has core concepts such as anchor, visibility, and evolution, which help identify opportunities for improvement or innovation. Tom demonstrated how Wardley Maps can be analyzed in Neo4j to find hidden knowledge and created Parsley to better analyze their maps. Parsley allows users to import Wardley Maps into Neo4j, losing the map's visibility but gaining connections between components, enabling deeper analysis of the map. With Parsley, Tom plans to use pattern matching and graph algorithms to gain access to the knowledge hidden in the maps.

The Neo4j GraphAcademy is providing free training for Q4 2022, which includes various topics such as hands-on tutorials and sessions on using GraphQL, AI for graph modeling, and knowledge graph adoption. Additionally, the company has introduced a new feature called negative relationships, allowing developers to train link prediction models more quickly. The Neo4j GraphAcademy is also offering a free survey for developers building modern applications with GraphQL. Furthermore, there are several upcoming meetups around the world, including sessions on using AuraDB and discovering new technologies. The company is encouraging collaboration between data scientists and programmers through various initiatives, such as knowledge graph adoption and API strategy benefits in real life.

Meet Bertrand Provost, a Senior Data Scientist at Le Groupe La Poste, a company that operates various services with 1.3 million daily customers and delivers 18 billion items annually. He uses Neo4j to model user journeys on the bank website, detect fraud, and mitigate cyber criminality. Using Neo4j has provided surprising results, particularly with its graph visualization capabilities. To get started with Neo4j, Bertrand advises seeking professional services for data ingestion, as it can be challenging without them. He envisions a future where graph technology is used to model natural language processing, enabling predictions on price and sentencing, among other applications.

As a Customer Success Manager at Neo4j, the author leverages ChatGPT to create and import sample datasets into their graph technology platform. Using Visual Studio Code and node.js, they utilize ChatGPT's capabilities to generate scripts that produce usable data models and transactions between accounts in a simple banking scenario. The generated scripts are then imported into Neo4j using packaged node.js scripts or Cypher queries, with the author making some minor adjustments before importing large datasets. After completing this process, which took less than an hour, the author notes that while ChatGPT is incredibly powerful, there is still a need for underlying knowledge of systems and processes to refine and improve the generated output.

There's plenty more within!` Gal Engelberg presents an ontology-driven data federation architecture that groups discovered application flaws by attack techniques and corresponding countermeasures. Tomaz Bratanic explains the new Cypher syntax, including inline filtering options and syntax for defining unique constraints, existential subqueries, and count subqueries. Neo4j Live features sessions on graph data science, visualization, recommendation systems, app development, and interview-style discussions about the importance of graph query language. The upcoming events include Discover AuraDB with Michael and Alexander, Neo4j Live: SustainGraph – A Knowledge Graph for Sustainable Development, a meetup on graph databases in action, and Going Meta – A Series on Graphs, Semantics, and Knowledge Ep. 13. `Don't forget to retweet, if you like it!` quotes Thomas Larsen (@tlarsendataguy) as saying Neo4j is an easy DB for application development that refactors as easily as code.

Neo4j Graph Data Science has released version 2.3, which includes new algorithms such as a Knowledge Graph Embedding (HashGNN) for generating embeddings on heterogeneous graphs, improved performance and integration with various graph algorithms, undirected relationship types support, and enhancements to machine learning predictions on imbalanced or skewed datasets. The release also introduces common data science community datasets and provides a new procedure for writing labels based on node properties. Additionally, Cypher Aggregation is being targeted as the primary surface for projecting graphs from the database using Cypher, while Shard Local Algorithm Execution With Composite Databases allows for algorithm execution to be coordinated across multiple shards. The next major release will include fully managed graph data science as a service, AuraDS, on Amazon Web Services and Microsoft's Azure Platform.

The latest release of Neo4j Data Importer introduces a new feature called file filtering, which allows users to apply simple filters to files without the need for pre-processing. This enables loads in more scenarios, including loading data from aggregate node lists and relationship lists where information on all nodes and relationships is encapsulated in just two files. The feature supports exact string matches and can be applied to both nodes and relationships files. Users can now filter out irrelevant data by applying filters to the file, which gives feedback as you type on how many matches were found. This feature is available in AuraDB Free and Professional, and users can provide feedback through the Neo4j Developer Blog.