October 2016 Summaries

The Neo4j community has shared various content, including articles, blog posts, podcasts, audio recordings, videos, libraries, and code repositories. The community is actively contributing to the development of Graph Databases, Information Security, and data analytics for Healthcare. Resources are available on how to use Neo4j with different programming languages such as Scala, .NET, and PHP. Additionally, there are tutorials on using Neo4j with Tableau and other tools like jQAssistant and jqassistant-tutorial. The community is also sharing knowledge on topics like testing, Docker, and upgrading to Spring Data Neo4j 4.2.

You can find the Neo4j Tableau connector code and documentation in our neo4j-contrib/neo4j-tableau GitHub Repository. This connector allows users to make their graph data accessible in Tableau, a popular integration request for Neo4j. The supported means of accessing graph query results from Neo4j include integration with the Tableau Web Data Connector, Neo4j Server extension to generate and publish TDE files, and a standalone tool to generate and publish TDE files from JDBC. A small web application is provided to run locally or deploy somewhere for this purpose. Users can configure the Neo4j connection information and Cypher query within the Tableau component using its URL. Additionally, there are options for higher volume and lower-level integration with Tableau, as well as a Neo4j Server extension for generating and publishing TDE files directly to the server. The connector has been tested with Tableau 9 and 10, and feedback is welcome to improve the integration.

Enterprise data management is a complex challenge that involves managing multiple departments, processes, and heterogeneous data across various systems. The traditional approach to enterprise data management often leads to departmental data management, where each department has its own IT system, leading to complexity and inefficiency. To overcome this, companies need to integrate their data, which can be achieved by creating integration hubs or a unified system that includes all data sources and processes integrated into one data model. A flexible graph platform like Structr can help achieve this by providing a unified central data model, allowing for easy creation of micro apps, and enabling fast development and deployment of applications with round-trip change times of a few minutes or hours. This approach enables companies to streamline their processes, reduce complexity, and improve user acceptance, making it an attractive solution for enterprise data management.

In this Neo4j and Scala series, the authors discuss combining Neo4j with Apache Spark for data processing. To start using Spark, developers must download Apache Spark 2.0.0 and set the `SPARK_HOME` path in their `.bashrc` file. With these steps complete, they can use the Neo4j-Spark Connector to process data from Neo4j. The authors also provide a recap of previous blog posts on getting started with Neo4j and Scala, as well as using Spark for data processing.

Scribestar, a small company with around 20 people, switched to Neo4j and experienced significant business benefits. The company targets the legal industry, specifically lawyers and associates, who rely on desktop publishing tools. By using Neo4j and RavenFS as file stores, Scribestar was able to improve data security and reduce latency. The graph database architecture allowed for efficient collaboration services, data modeling, versioning, and tuning of Cypher queries. With the switch to Neo4j, Scribestar reduced its team size by half, increased system speed and performance, and improved cycle times. The company can now visualize their data in a way that users will understand, making it easier to scale the product. In the future, Scribestar plans to continue using Neo4j, potentially switching RavenFS for alternative storage solutions if needed.

Tom Zeppenfeldt, Founder of Graphileon, a Neo4j solutions partner, shares his experience working with the graph database. He highlights the importance of Cypher, which allowed him and his non-technical team to easily work with Neo4j. The InterActor, their product, enhances the user interface for investigative journalists, enabling them to create and browse networks, tables, and charts in Neo4j. Tom notes that Neo4j's ease of use, combined with Cypher's pattern matching capabilities, made it an attractive choice. He also shares surprising results from using dynamic Cypher and emphasizes the importance of optimizing Cypher queries for performance improvements. Overall, Tom praises Neo4j's rapid development and new features, which have significantly improved his team's productivity.

MediaHound's NextQueue, a personalized movie and TV discovery web app powered by Neo4j, received an incredible response from the Reddit community upon launch, with over 4,200 upvotes and more than 20,000 visitors in just one day. Users praised the platform's ability to make meaningful connections between content traits, such as Fantasy and Sci-fi, and its recommendation engine, which uses graph algorithms to provide nuanced and diverse suggestions. The Entertainment Graph, powered by Neo4j, connects over 60,000 premium movies and shows with more than 4,500 traits, allowing for complex queries and fast graph traversals that enable personalized recommendations. Users also appreciated the platform's flexibility, ability to validate recommendations against real user data, and its focus on variety and versatility in content suggestions. The launch marked a significant success for Neo4j, demonstrating its capabilities in powering media personalization platforms and providing valuable insights into user behavior and preferences.

Monsanto, a global agricultural products company, is working to improve its genomics pipeline data analysis to address the challenges of feeding a growing population. The company aims to develop more efficient crops to meet the demands of a projected 9.5 billion people by 2050. Monsanto's scientists have been using artificial selection and hybridization techniques for decades to breed better crops, but they are now looking at new ways to analyze their data using graph databases. By leveraging graph analysis, Monsanto can quickly process large amounts of data on plant ancestry and genotype information, allowing them to make more informed decisions in their breeding pipeline. This technology has the potential to save millions of dollars a year by reducing the need for field trials and expanding the horizontal scaling of their pipeline.

In this post, we'll discuss how to migrate data from other databases like MySQL, PostgreSQL, Oracle, and Cassandra to Neo4j using Scala. We've covered the basics of getting started with Neo4j in Scala and defining user-defined procedures and APOC library usage previously. To perform data migration, it's essential to keep the Neo4j APOC Kit in the `$Neo4j_Home/plugins` folder. The post aims to guide readers through connecting and transferring data from these databases to Neo4j, starting with PostgreSQL.

In this investigation, the authors used Tom Sawyer Perspectives to uncover connections related to Kojo Annan, son of former United Nations Secretary-General Kofi Annan, who was listed in the Panama Papers document. The investigation revealed a series of intermediaries and people connecting Annan to Bukola Saraki, Nigerian Senate President, and his family members, including Toyin Saraki, wife of Senate President Bukola Saraki. Through a process of expanding connections and cleaning up results, the authors discovered a clear link between Annan and the Saraki family, revealing a complex web of relationships that sparked investigations and allegations against Senator Bukola Saraki. The investigation highlighted the power and flexibility of Tom Sawyer Perspectives in building graph and data visualization applications to view and understand Big Data.

The Offshore Leaks database is a massive collection of entities and trusts incorporated in tax havens, which can facilitate money laundering, tax evasion, and other crimes. Tom Sawyer Software specializes in helping businesses build sophisticated enterprise graph and data visualization applications to analyze their Big Data, such as the volume of information in the Offshore Leaks database. The company's flagship product, Tom Sawyer Perspectives, was used to investigate connections between individuals indicted in the 2015 FIFA corruption scandal. By visualizing the network of connections and running algorithms on the data, investigators were able to identify a potential connection between a key figure and other suspected wrongdoers, highlighting the power of graph analysis and visualization in uncovering hidden relationships.

GrapheneDB, a cloud-based Neo4j database hosting service and Bronze sponsor of the GraphConnect San Francisco event, has highlighted significant advancements in Neo4j and its own platform. Neo4j's introduction of the Bolt protocol has enhanced performance by providing a fast, consistent interface across programming languages and overcoming the limitations of the previous HTTP REST protocol. Officially maintained language drivers and the addition of custom stored procedures further extend Neo4j's capabilities, enabling more seamless and efficient data handling. GrapheneDB has also improved its service with new Insights features for better Neo4j performance visibility and introduced an API for streamlined database management, supporting various operational tasks such as environment syncing and automated backup downloads. These developments position Neo4j 3.0 as a substantial upgrade in performance and extensibility, while GrapheneDB continues to enhance its offerings for developers leveraging Neo4j in the cloud.

Data is an asset that has evolved from a disposable commodity to a valuable resource, with organizations capturing and utilizing it to remain agile in response to changing trends. The need to trust data has increased in importance, and achieving this goal at an enterprise level requires understanding critical information assets, data lineage, and the impact of change. Graph technology is seen as a key solution for managing enterprise metadata, offering a way to model complex relationships between data assets and gain confidence in the provided answers. This approach has proven effective with forward-thinking organizations and is part of a robust metadata management strategy.

Financial crime encompasses various forms of wrongdoing, including money laundering, terrorism funding, corruption, tax evasion, and insurance fraud. Perpetrators use fake identities, middlemen, complex financial schemes, and other tactics to hide their tracks. Law enforcement agencies, financial institutions, and fraud detection professionals aim to uncover evidence of wrongdoing by utilizing graph technologies like Linkurious and Neo4j. These tools offer a holistic view of entities involved in financial crime and their relationships, enabling the identification of suspicious connections in real-time. Graph-powered anti-money laundering solutions, such as Linkurious Enterprise, integrate with Neo4j to store and query complex connected data, providing an additional layer of high-level services including authentication services, user access management, graph visualization, and full-text search. These solutions enable financial institutions to identify patterns, conduct advanced investigations, and visualize data using Cypher queries and graph browsers. By leveraging these tools, organizations can effectively combat financial crime and stay ahead of emerging threats.

Databridge is a fully-featured ETL tool designed for Neo4j, offering a declarative approach to data import. It allows users to define graph data models using schema descriptors and resource descriptors, which are then used to map CSV or other tabular data sources to the desired nodes, edges, labels, and properties in the graph. The tool provides various features such as filters, data composition, strategies for handling duplicate keys, and data converters, making it suitable for both SMEs and larger enterprises. Databridge is compatible with all versions of Neo4j from 2.0 onwards and offers a built-in profiler to optimize huge data imports, as well as a command-line shell for easy import tasks. It has been tested in the field with several clients and represents a step change in data import for Neo4j.

The author is frustrated with the lack of understanding about climate change and its impact, despite being bombarded with information. They wanted to find a way to easily understand and visualize the numbers involved in carbon emissions, but struggled with traditional database systems like SQL and Excel. The author discovered Neo4j, a graph database, which provided a flexible structure for connecting and aggregating data. However, they still needed a user-friendly interface, which is where Structr comes in - an application framework for Neo4j that simplifies the development process and allows non-technical users to focus on the business logic. With Structr, the author was able to create a system that captures various units of measure combinations, aggregates data mathematically, and presents numbers in a user-friendly way, ultimately saving time and money while minimizing risks.

The GraphAware Natural Language Processing (NLP) plugin, developed as part of the GraphAware Enterprise Reco plugin for Neo4j, is used to deliver high-quality recommendations to end users by combining content-based and ontology-based cosine similarities with a collaborative filtering approach. The plugin models documents using three dimensions: Content, Tag, and User, providing different views of the document's meaning. It uses a combined approach to compute document similarity, creating three vectors for each document based on its internal content, tags, and user interactions. These vectors are then used to calculate a combined similarity score, which represents new knowledge extracted from the data available in the graph database. The plugin is open-sourced under GPL and aims to provide a complete end-to-end customized search framework with other plugins available on the GraphAware products page.