27 May 2021
less than a minute read
RAI’s Graph Normal Form, or GNF, enables true re-use of your organization’s data assets for a data-centric architecture.
GNF is based on the relational model, well-known for its scalability, enabling you to load and process enterprise-scale data.
RAI technology combines this flexibility and scalability to make building and querying enterprise-scale Knowledge Graphs a feasible reality for any organization.
Graph Normal Form is RAI’s implementation of Sixth Normal Form, or 6NF, the ultimate expression of the relational model.
In Sixth Normal Form, each relation has one or more key columns and just one value column.
There are no nulls, no empty rows.
GNF extends Sixth Normal Form data modelling by adding concepts and meaning to the links that connect the individual Sixth Normal Form relations.
RAI’s “dovetail” join unlocks the performance needed to make GNF practical.
Unlike more commonly used Third Normal Form, fully normalized Sixth Normal Form eliminates the need to reorganize data to suit each workload. Applications can apply multiple logical data models to the underlying relations. Unlike other cloud database systems, this is truly a single copy of your data for multiple workloads.
The relational model is known for being able to handle high row counts.
Cloud storage enables open-ended scalability for your relational data.
Fully-normalized GNF data has no empty rows, no sparse data. Your relations on disk are dense with data.
Fully normalized data means many relations, and queries require large n-way JOINs that would kill a typical database. But RAI’s “dovetail” JOIN technology processes all arms of the JOIN in parallel, operating more efficiently the more arms there are.
Semantic Optimization makes your complex data workloads more efficient, which in turn improves overall system performance and scalability.Read More
Dovetail Join is a WCOJ (Worst Case Optimal Join) algorithm, meaning we can mathematically prove that the more complicated the problem is, the faster we will go.Read More
This demo is called the “Knowledge Graph to Learn Knowledge Graphs”, or KGLKG. It parallels the authors own journey from programming in imperative languages like Java, C++, and Python to RAI's declarative language, Rel. For many years, I used those legacy languages plus SQL in an application-centric database-oriented way of solving business problems. But today I am using Rel and a data-centric business modeling approach to creating business solutions.Read More