[Vaccination 2022] Design and Implementation of the RelationalAI Knowledge Graph Management System (Martin Bravenboer)
RelationalAI is the next-generation database platform for new intelligent data applications based on relational knowledge graphs. The Relational Knowledge Graph Management System (KGMS) complements the modern data stack by allowing data applications to be implemented relationally and declaratively, leveraging knowledge/semantics for reasoning, graph analytics, relational machine learning, and mathematical optimization workloads. RelationalAI as a relational and cloud native system fits naturally in the modern data stack, providing virtually infinite compute and storage capacity, versioning, and a fully managed system.
RelationalAI supports the workload of data applications with an expressive relational language (called Rel), novel join algorithms and JIT compilation suitable for complex computational workloads, semantic optimization that leverages knowledge to optimize application logic, and incrementality of the entire system for both data (IVM) and code (live programming). The system utilizes immutable data structures, versioning, parallelism, distribution, out-of-core memory management to support state-of-the-art workload isolation and scalability for simple as well as complex business logic.
In our experience, RelationalAI’s expressive, relational, and declarative language leads to a 10-100x reduction in code for complex business domains. Applications are developed faster, with superior quality by bringing non-technical domain experts into the process and by automating away complex programming tasks.
We discuss the core innovations that underpin the RelationalAI system: an expressive relational language, worst-case optimal join algorithms, semantic optimization, just-in-time compilation, schema discovery and evolution, incrementality and immutability.
This talk is part of the Vaccination Database (Booster) Tech Talk Seminar Series.
Martin Bravenboer is VP Engineering at RelationalAI where he leads the development of the RelationalAI system. Before RelationalAI, he was CTO at LogicBlox. As a postdoctoral researcher with Prof. Yannis Smaragdakis, he developed the Doop framework for strictly declarative and precise points-to analysis that uses the LogicBlox system. Martin obtained his PhD at Utrecht University in the area of language design and compiler construction.