The hyper-relational database for knowledge-oriented systems

9th October 2017 in London at CodeNode

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AI systems process knowledge that is far too complex for current databases. They require more expressive data schemas and intelligent query languages to provide a strong abstraction over complex data and their relationships. In this talk, we will discuss how GRAKN.AI, a distributed hyper-relational database, enables knowledge-oriented systems to work with complex data that serves as a knowledge base.

We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides a much higher-level abstraction over traditional query language. And finally, we will review the challenges of data management when developing Cognitive and AI systems, and how we solve them using Grakn and Graql as the database and query language.


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The hyper-relational database for knowledge-oriented systems

Haikal Pribadi

Haikal is the Founder and CEO of GRAKN.AI, the database for AI. His interest in the field began at the Monash Intelligent Systems Lab, where he built an open source driver for the Parallax Eddie Robot which was then adopted by NASA. After which, he completed a Master’s degree in AI from the University of Cambridge. Haikal was also the youngest Algorithm Expert behind Quintiq’s Optimisation Technology behind some of the world’s largest supply chain systems in transportation, retail and logistics.