Abstract
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.