Category Theory for Semantic Data Interoperability
Intervenant⋅e⋅s
Résumé
Every engineering organization struggles to exchange and consolidate data reliably across their many data sources. Attempting to build integrations between all relevant systems is impractical with current mainstream practices, because of the quadratic relationship between applications and the number of integrations between them. To avoid the exploding cost of scaling ad-hoc integrations, organizations may attempt to standardize the application semantics. This approach hasn’t worked in practice because it's impractical to get many people from diverse domains to agree on a single perspective, and nuanced domain-specific meaning is always sacrificed.
Category Theory is a mathematical language for encoding and computing semantic structures across contexts, and has been used by mathematicians and scientists to formally communicate meaning across domains. Recent developments out of MIT have worked out how to apply the approach to data schemas and are leading to a paradigm shift in semantic data interoperability. There are several benefits, but two key benefits are compositionality and machine verification. Integrations can be added together and checked for integrity, which means that a quadratic number of integrations can be inferred from a linear input with mathematical guarantees that data won’t be corrupted. A third benefit is that the approach is agnostic to any specific data structure and can interoperate across all of them (SQL, XML, Json, Graph, RDF, etc). In this talk we will outline a classic challenge facing industrial engineering and how approaches built from Category Theory by Conexus can offer a new paradigm of solutions.
Références
Presentation Video (Similar to proposed): https://conexus.docsend.com/view/9kt93xqcx2kugdsa Password: datamesh
Presentation Slides (Similar to proposed): https://conexus.docsend.com/view/2stvuqt4xjpb3bbx Password: datamesh
Research Papers: https://conexus.com/resources/papers/
Auteurs/Autrices
Ryan Bio: Ryan Wisnesky obtained B.S. and M.S. degrees in mathematics and computer science from Stanford University and a Ph.D. in computer science from Harvard University, where he studied the design and implementation of provably correct software systems . Previously, he was a postdoctoral associate in the MIT department of mathematics, where he developed the CQL query language based on category theory. He currently leads open-source and commercial development of CQL as CTO of Conexus AI . He maintains an active collaboration with the information-integration department of IBM Research, where he contributed to the Clio, Orchid , and HIL projects.
Kenny Bio: Kenny is responsible for ensuring that new Conexus products developed from Categorical Mathematics align with market needs. He has a history of founding, building and investing in technology startups with an emphasis on developing product-market fit. In 2014 he was the lead architect and designer of Vandrico's Wearables Database, a knowledge graph of emerging wearable technology at the time. His professional interests revolve around understanding patterns of human behavior and motivation, and building products that propagate knowledge.