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SkillsCast

Using GRAKN.AI for Big Complex Biomedical Data

21st May 2019 in London at CodeNode

This SkillsCast was filmed at Connected Data London May

The success or failure of any modern organisation relies on the way they leverage their data. However, most institutions and organisations have no way to aggregate the magnitude and complexity of their disparate data catalogs. They require a unified representation of their data which represents their specific domain truthfully as well as conceptually.

In other words, 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 Manchester's engineering community.

Systems biology is one of the domains that produces huge amounts of data and presents integration challenges due to their complex nature. As understanding the complex relationships among these biological data is one of the key goals in biology, we will demonstrate how Grakn is used to integrate disparate biological data into a knowledge graph that leads to valuable new insights of our data at scale.

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. In addition, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.

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Thanks to our sponsors

Using GRAKN.AI for Big Complex Biomedical Data

Tomas Sabat

Tomas is the COO of GRAKN.AI, the knowledge graph for intelligent systems. He works on introducing the world on how to use a Grakn knowledge graph to build cognitive and AI systems, working in industries such a biotech, finance and cyber-security.

SkillsCast

The success or failure of any modern organisation relies on the way they leverage their data. However, most institutions and organisations have no way to aggregate the magnitude and complexity of their disparate data catalogs. They require a unified representation of their data which represents their specific domain truthfully as well as conceptually.

In other words, 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 Manchester's engineering community.

Systems biology is one of the domains that produces huge amounts of data and presents integration challenges due to their complex nature. As understanding the complex relationships among these biological data is one of the key goals in biology, we will demonstrate how Grakn is used to integrate disparate biological data into a knowledge graph that leads to valuable new insights of our data at scale.

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. In addition, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Using GRAKN.AI for Big Complex Biomedical Data

Tomas Sabat

Tomas is the COO of GRAKN.AI, the knowledge graph for intelligent systems. He works on introducing the world on how to use a Grakn knowledge graph to build cognitive and AI systems, working in industries such a biotech, finance and cyber-security.