...
This WG currently meets weekly on Tuesdays. See the Meeting Page for the meeting schedule, agenda, and meeting notes. For a calendar invite with complete Zoom information, please send email to the mailing list above.
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.
Core components
Overlays Capture Architecture (OCA)
(Presentation and live demo / Tools tutorial / .CSV parsing tutorial)
OCA is an architecture that presents a schema as a multi-dimensional object consisting of a stable stable schema base and interoperable and interoperable overlays. Overlays are 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 harmonizes database models. It is a solution to semantic harmonization between data models and data representation formats. As a standardized global solution for data capture, OCA facilitates data language unification so that harmonized data can be pooled , promising to significantly enhance the ability to pool data more effectively for improved data science, statistics, analytics and other meaningful services.
OCA schema bases contain a "blinding_attr" flagging block to enable schema issuers to flag attributes that could potentially unblind the identity of a governing entity.
...