Intelligent/Cognitive Systems consume data that are too complex for current databases to handle. GRAKN.AI is a hyper-relational database that allows you to perform knowledge engineering to manage this complexity. Effectively, Grakn provides a Knowledge Base (aka. Knowledge Graph) as the core foundation of intelligent systems.
Grakn provides an intelligent query language, Graql: Grakn’s reasoning (through OLTP) and analytics (through OLAP) query language, which is a much higher-level abstraction over traditional query languages. This allows intelligent systems to benefit from an intuitive language the provides an expressive schema for knowledge modelling, reasoning transactions for real-time inference, distributed algorithms for large-scale analytics, and optimisation of query execution. Grakn also provides the logical integrity of SQL, the scale of relationships of Graph databases, and the horizontal scalability of NoSQL.
In this community, we discuss the underlying technology and applications of the Grakn hyper-relational database. We will also discuss the techniques of a new software engineering principle, Knowledge-Driven Development, that Grakn now enables. The group is for engineers to demonstrate their solutions and share the lessons they learnt, as well as business leaders to learn applications of Grakn in their business.
Cognitive/AI systems process knowledge that is far too complex for current databases. They require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets. In this Meetup event, we will introduce GRAKN.AI, a distributed hyper-relational database for knowledge engineering, to New York's engineering community.
Grakn provides the knowledge base foundation for intelligent systems to manage complex data. We will also introduce Graql: Grakn's reasoning (through OLTP) and analytics (through OLAP) query language. Graql provides the tools required to do knowledge engineering: an expressive schema for knowledge modelling, reasoning transactions for real-time inference, distributed algorithms for large-scale analytics, and optimisation of query execution. And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.
There will be beers, pizza, code, and most a certainly a good time!