October 2017 Summaries

The new release of Neo4j Graph Platform, version 3.3, has several key improvements and upgrades across its native graph database, including significant enhancements to write performance, Cypher interpreter speed, and data import functionality. The platform also introduces a new Data Lake Integrator toolkit for importing and exporting HDFS files into graphs, and a simplified ETL process for morphing data from relational databases into graph data. Additionally, the Graph Analytics component has been improved with a fast graph algorithms library, and Cypher for Apache Spark has been enhanced to bind ease of writing Cypher with a massively scalable in-memory graph analytics processor. The Enterprise Edition includes several new features such as Neo4j Desktop, which provides a free local instance of Neo4j Enterprise Edition for development, and numerous improvements to the database kernel that expand database availability during upgrades.

Neo4j recently hosted its GraphConnect conference in New York City, which featured keynotes by Neo4j CEO Emil Eifrem and Chief Scientist Dr Jim Webber. The conference covered the latest developments in graph databases, including the release of Neo4j 3.3, which focuses on empowering users to work with native graphs at scale more effectively and faster. This new version includes features such as data import, visualization, and analytics on top of the database. Additionally, a new version of graph algorithms has been released, supporting huge graphs and various clustering algorithms. The Neo4j Graph Platform also saw the release of APOC fall, which includes new features like procedures to access other databases via Bolt and support for multi-value JSON sources. Other notable events included a featured community member spotlight on Ray Bernard and Jennifer Webb, co-founders of Suprfanz, who showcased their tool using data science techniques and graph theory with Neo4j to generate live event attendance from social media platforms, email, and SMS.

The Neo4j Graph Platform is designed to help organizations introduce graph technology and Neo4j beyond just developers to new users, such as data scientists, data analysts, business users, and executives. The platform aims to address the evolving needs of customers in deploying Neo4j within complex IT infrastructures. It includes features like Neo4j Desktop, a mission control console for developers, and the Neo4j Graph Analytics library, which supports graph algorithms like community detection, path finding, and centrality analysis. The platform also offers data integration tools, including Neo4j ETL and the Data Lake Integrator, to ease moving data in and out of graphs. Additionally, it provides a range of visualization tools and partners with companies like Linkurious, Tom Sawyer Software, Tableau, Jet Brains, and KeyLines to help business analysts and users discover and visualize graph connections. The platform is designed to operationalize connections revealed as "big data analytic insights" and turn them into high-value graph applications, enabling a workflow loop between developers, big data IT teams, and data scientists.

The text discusses IBM's CAPI (Central Accelerator Platform Interface) and OpenCAPI technologies, which enable the acceleration of various applications on their Power Systems servers. The company has partnered with Neo4j to create accelerated solutions for its graph databases, leveraging FPGA (Field-Programmable Gate Array) technology. CAPI SNAP is a toolset that allows application developers to port their code to the CAPI platform and quickly get acceleration results, easing the transition to FPGAs. With this partnership, Neo4j's large graph databases can be accelerated with faster data filtering and memory access, providing significant benefits in terms of performance and latency. The text also mentions upcoming POWER9 launches and new accelerated database products, as well as hands-on tutorials for CAPI SNAP developers.

This week in Neo4j, the main event is GraphConnect NYC 2017, taking place on October 24th at Pier 36, NYC. The conference features a variety of great talks, including "How Graphs Changed The Way Hackers Attack", "Full stack development with GraphQL and Neo4j", and "News Networked: Using Neo4j’s Graph Database to Increase Scalability in Fake News Detection". The featured community member is Tim Ward, CEO of CluedIn, who will be presenting on building the connected enterprise with machine learning and Neo4j. Additionally, there are online meetups, including one on analysing Debian packages with Neo4j, and a call for participation for the GraphDevRoom at FOSDEM 2018. The Neo4j Knowledge Base has an article on query plan caching, and Neo4j will be sponsoring GraphQL Summit in San Francisco on October 25th and 26th.

The importance of cybersecurity has become increasingly clear as technology advances, with the risk of unauthorized access, change or destruction posing significant threats to individual and collective safety, security and human and economic well-being. The latest major cybersecurity incident at Equifax highlights the severity of this issue, with over 143 million US consumers potentially impacted by stolen personal information. Cybersecurity has become a big data problem, requiring flexible and scalable data solutions that can analyze vast amounts of rapidly evolving information. Graph databases, such as Neo4j, are an excellent match for this data, offering significant business value in terms of efficient querying and analysis, which leads to more informed decisions. The STIX specification, developed by MITRE, is a major advance for the cybersecurity industry, enabling collaborative threat analysis, automated threat exchange and detection capabilities. GraphAware, a leading Neo4j consultancy, is working to implement the STIX specification, recognizing that graph technology is the foundation of cybersecurity's future.

The complexity of subcontracting poses a significant challenge for companies that outsource or partner with contractors, resulting in limited visibility into the risk and compliance of their subcontractor community. This complexity can be modeled using a graph database, which was chosen by ISN due to its ease of installation, high performance, and small footprint, making it ideal for rapid building, deployment, and testing. The use of Neo4j graph database allowed ISN and Expero to quickly design and develop the system, leveraging its HTTP/S REST API and simple GUI console to inspect and analyze the data.

The Neo4j Object Graph Mapping library (OGM) and Spring Data Neo4j (SDN) have been updated to version 3.0 and 5.0, respectively. The new features in OGM 3.0 include dynamic properties, schema-based loading, ID management, field access only, and configuration changes and improvements. SDN 5.0 is built on top of Java 8, the new Spring Framework 5.0, and Spring Data 2.0, providing better causal cluster support, reactive repositories, auditing support, and improved modeling and querying capabilities. The team welcomes feedback from users to improve future versions, and a short survey is available to provide input.

The Neo4j community is excited about the upcoming GraphConnect NYC event, which will feature training classes, workshops, and technical sessions on building graph apps and various topics such as algorithms, causal clustering, AI, and more. The event also includes a DevZone with experts from the Neo4j team to review graph models and chat with fellow developers. Additionally, the community is welcoming new members, including Axel Morgner, Co-Founder and CEO of Structr, a graph-based low-code development environment for building data-centric web and mobile applications that uses Neo4j as its backend datastore. The SDN/OGM team has released OGM 3.0 and SDN 5.0 with improved features such as better loading strategies, custom id handling, and dynamic properties to support causal clusters. Other notable news includes the release of BloodHound version 1.4, a tool that uses graph theory to reveal hidden relationships within an Active Directory environment. The community is also sharing various projects, such as eth_graph, which syncs the Ethereum blockchain to Neo4j, and rdf2multimodel, a tool that imports RDF triples into Neo4j.

The author of the text used Twitter data to create a demo that answers questions about users' conversations on specific topics. The demo uses Neo4j, a graph database, to store and query the Twitter data. The author designed a data model with nodes for users, tweets, hashtags, and countries, and relationships between them. They imported Twitter data into Neo4j using the python-twitter library and then visualized the results on a world map using Datamaps. The demo allows users to input a hashtag and view the top eight most used hashtags along with their impact in different countries. The author plans to add more features, such as selecting data from a given time frame and leveraging machine learning algorithms to find hidden patterns. They also started exploring Neo4j's new graph algorithms, including Connected Components and Strongly Connected Components.

The automotive industry is undergoing a significant paradigm shift due to the rise of new technologies, including self-driving cars and electric vehicles. The fear of disruption from established players like Tesla has led to a sense of panic among big automotive OEMs, despite having had over 100 years to evolve their products and technologies. However, this industry has only recently started to prioritize innovation, with millions of engineers feeling safe in large and conservative ecosystems that inhibit creativity. To address this, patedo was created as a tool to help engineers find and evaluate relevant patent information, interact with it in unprecedented ways, and pre-justify their ideas. With its advanced features, including full-text search, filters, and graphical reports, patedo aims to become a driver for innovation, enabling automotive OEMs to move from defending market share to shaping the future of green mobility with better products.

The problem of data silos is a significant challenge in many organizations, where data from different sources is fragmented and not easily accessible. To overcome this issue, NoSQL databases and technologies have been developed to integrate data more rapidly, at greater scale, and with more agility. However, these new technologies require the acquisition of new skills, which can be a barrier for some organizations. FactGem provides a solution by allowing non-technical users to integrate disparate silos of data without writing any code, making it easier for business users to access unified reports on previously disconnected data. The platform uses WhiteboardR, MappR, and MonitR to create a unified model, map data sources, and load data into FactGem, where it is immediately accessible and queryable. Additionally, FactGem's Enterprise Neo4j offering allows users to take advantage of the graph database technology directly using Cypher. By providing an easy-to-use interface and drag-and-drop data integration, FactGem helps non-technical users quickly see the value of graphs and ultimately invest in adopting graph technology.

Decision making is often a complex task that depends on multiple interactions between variables, actions, and actors in various contexts. Hugo Araujo, an entrepreneur and consultant, sought to develop a tool that could help him manage information and connect the dots in complex systems, such as natural ecosystems and human organizations. He turned to the Neo4j graph database, which provides a naturally connected way of storing, retrieving, and computing data. By leveraging this technology, Hugo created 7VORTEX, a tool inspired by nature for managing information, creating beautiful graph-based presentations, and deriving systemic knowledge. The tool uses weighted networks as energy exchanges between nodes to model complex relationships and provide important systemic insights. Additionally, 7VORTEX has been integrated with Natural Language Processing (NLP) to leverage semantic connections of the data and provide recommendations on which tags to add to a bubble or suggest which bubble should be next in a presentation. The tool also features graph-based storytelling and analysis capabilities, such as breadth-first and depth-first narrative modes, and advanced analytical features like automatic graph partitioning and path detection. By harnessing the power of Neo4j and NLP, 7VORTEX has become central to Hugo's consultancy work, contributing to many successful projects and enabling him to share how everything is connected with his clients.

The Neo4j community is highlighted as the focus of this week's update, with featured member Michal Bachman, founder of GraphAware, being recognized for his contributions to the platform. Several online meetups are scheduled, including one on managing component complexity at Autodesk and another on analyzing Debian packages using Neo4j. New tools and projects have been released, such as neo4j-nlp for combining graphs and Natural Language Processing, and neocaster, a .NET micro "ORM" in the spirit of Dapper but for Neo4j. Additionally, digitalOcean posted a video from Ray Bernard's talk on using Neo4j to drive attendance over social media platforms, email, and SMS. The company is also hiring for various roles, including engineering, pre-sales, professional services, and developer relations teams.