Data semantics is the study of the meaning and use of specific pieces of data in computer programming and other areas that employ data. When studying a language, semantics refers to what individual words mean and what they mean when put together to form phrases or sentences. In data semantics, the focus is on how a data object represents a concept or object in the real world. The term “Decentralized Semantics” is a representation of “data semantics” within an age of distributed ledger technology (DLT) solutions which continue to mould the current decentralisation movement.
Mission and Scope
The mission of the WG is to define a data capture architecture consisting of immutable schema bases and interoperable overlays for Internet-scale deployment. The scope of the WG is to define specifications and best practices that bring cohesion to data capture processes and other Semantic Standards throughout the ToIP stack, whether these standards are hosted at the Linux Foundation or external to it. Other WG activities will include creating template RFPs and additional guidance to utility and service providers regarding discussions and implementations in this domain. This WG may also organise Task Forces to escalate the development of certain components if deemed appropriate by the majority of the WG members and in line with the overall mission of the ToIP Foundation.
Conveners
- Paul Knowles (Human Colossus Foundation)
- Robert Mitwicki (Human Colossus Foundation)
Interested Members (add your name and organization if you may be interested in joining this proposed WG)
- Philippe Page
- Haydar Majeed
- Rieks Joosten
- Lucy Yang
- Sal D’Agostino
- Zan McNaught
- Mike Bennett
- Colin Wallis
- John Walker
- Mark Lizar
- Burak Serdar
- Scott Whitmire
- Dennis Landi
- Darrell O’Donnell
- Kalyan Kulkarni
- Zeljko Milinovic
- Crt Ahlin
- Davide Calvi
- James Hazard
- Michael Corning
- Carsten Stöcker
- Juan Caballero
- Jim St.Clair
- Eric Drury
- Nathan George
- Jan Lindquist
- Vipin Bharathan
- Kamlesh Nagware
- Vinod Panicker
- Steven Milstein
- Vitor Jesus
- Iain Henderson
- Casandra Grundstrom
- George Aristy
- John Wunderlich
- Joaquin Salvachua
- Andre Kudra
- Marie Wallace
- Wayne Chang
- Drummond Reed
- Georg Philip Krog
- Wenjing Chu
- Anadi Pandharkar
Description
The post millennial generation has witnessed an explosion of captured data points which has sparked profound possibilities in both Artificial Intelligence (AI) and Internet of Things (IoT) solutions. This has spawned the collective realization that society’s current technological infrastructure is simply not equipped to fully support de-identification or to entice corporations to break down internal data silos, streamline data harmonization processes and ultimately resolve worldwide data duplication and storage resource issues. Developing and deploying the right data capture architecture will improve the quality of externally pooled data for future AI and IoT solutions.
Overlays Capture Architecture (OCA)
OCA is an architecture that presents a schema as a multi-dimensional object consisting of a stable schema base and interoperable overlays. Overlays are task-oriented linked data objects that provide additional extensions, coloration, and functionality to the schema base. This degree of object separation enables issuers to make custom edits to the overlays rather than to the schema base itself. In other words, multiple parties can interact with and contribute to the schema structure without having to change the schema base definition. With schema base definitions remaining stable and in their purest form, a common immutable base object is maintained throughout the capture process which enables data standardization.
OCA facilitates a unified data language so that harmonized data can be pooled for improved data science, statistics, analytics and other meaningful services.