What is a graph database?

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1 What is a graph database? A graph database is a data store that has been optimized for highly connected data. Storing connected data in a flat tabular format is time and resource intensive, usually requiring the developer to resort to joining tables and foreign keys. A graph database overcomes this problem by storing and querying data in a graph format, i.e. as a collection of objects and relationships usually called nodes and edges and properties. When data is modeled in this way, we are able to traverse the graph, using a query, to obtain answers to certain questions. You can read more about graph databases on our website: What is Neo4j? Neo4j is a robust, scalable native graph database, developed by Neo Technologies. Neo4j 2.0 was released in December 2013 and includes a developer workbench, a basic visualization tool and its own graph query language (Cypher). A variety of different licenses are available, ranging from a free open source option to an enterprise subscription. Neo4j is currently the most widely used graph database in the world, with over a decade in production and tens of thousands of deployments from startups to Global 500 companies. Some of the reasons for Neo4j s popularity include its:

2 Robustness all transactions are fully ACID Scalability Neo4j can store graphs of several billion elements on one machine Speed graph traversal with Neo4j is fast, to a depth of 1000 levels in milliseconds Why visualize Neo4j? The highly connected structure of graph data is inherently well suited to network visualization, which is much simpler for a human to understand than raw data.! See patterns more clearly inside a graph data structure, there are usually common patterns. Using visualization, the human brain can recognize and decode these patterns much faster and more accurately than a machine alone.! Explore your data visualization allows users to explore and traverse their database, allowing them to gain a more in-depth and meaningful understanding of the data.! Answer questions thanks to its graph structure and high performance, Neo4j is particularly well suited to certain kinds of problems, including path finding, recommendations and social graphs. All of these can be enhanced by visual analysis techniques, such as automatic layouts, filtering and social network analysis. Neo4j and KeyLines KeyLines is flexible enough to work with almost any kind of database, but is a particularly good fit for Neo4j. 1. Users interact with their KeyLines chart using their web browser. Each interaction, e.g. click, right-click, hover, etc., raises a query to the database. This is generated by KeyLines in the form of an AJAX request. 2. The AJAX request is parsed to a Cypher query, which is sent to the Neo4j database server. 3. Neo4j returns the required data as a JSON object.

3 4. KeyLines renders the JSON data in the browser, using the HTML5 Canvas element or a Flash fallback. Benefits of the KeyLines/Neo4j architecture Speed The exact speed depends on the volume of elements being called to the chart, but visualizing a Neo4j database with KeyLines is fast. Visual querying as users interact with KeyLines, new data can be automatically called and inserted back into the chart. This effectively allows KeyLines to be used as a visual query engine, meaning users can intuitively explore their data without learning any query languages. Browser-based KeyLines is a browser-based technology. End users do not need to install any software or plugins before they get started. Also, as graphics are rendered client side, the required bandwidth is reduced and dedicated visualization servers are not required. Getting started with KeyLines and Neo4j KeyLines is compatible with any graph database, but is especially easy to connect to Neo4j. Below we ve summarized the generic steps that are involved to set this up, but if you need more information or if your situation is more complex, just get in touch. Note: You will need to contact us for access to the KeyLines SDK: Download the Neo4j server: Install is as a server running on port This usually happens automatically, but you can test that it installed and is running correctly by navigating to the following URL in Chrome or Firefox (Internet Explorer is not supported): This should give you a new empty database. The REST interface is already running at: This is how KeyLines submits cypher queries to and get the results as a JSON file. Type :play movie graph in the console to get some sample cypher code. This can be pasted back in the console to generate some data based around actors in the Matrix movies (this is the same dataset used in the KeyLines demo, below). Check out the sample code used in the demo, below, to tweak the visual styling and createnode and createlinks functions to produce the results you want. A KeyLines / Neo4j demo The KeyLines SDK includes a demo showing how KeyLines can be applied to a Neo4j database. Navigate to Demos > Neo4j. This demo uses data from a Neo4j database about movies and actors, showing how users film reviews can be used as a recommendation using a graph structure.

4 Upon loading, you will see two nodes: Micha (a user) who has given a 5-star review to the film The Matrix. Double-clicking on the Matrix node will expand to show the actors credited to the film. Males are represented as blue nodes, females as pink nodes. Double-clicking any node will expand it further, adding new data as new nodes and links inserted into the existing chart. The layout algorithms can be used to re-arrange the nodes. Here, a structural layout isolates actors appearing in both The Matrix and The Matrix Reloaded. For each action, KeyLines generates a Cypher query (Cypher is the Neo4j query language) Full source code for the demo is available by clicking the source code button at the top of the page More information For more information about visualizing your Neo4j database with KeyLines, or to evaluate the KeyLines SDK, please get in touch:

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