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Semantic domain [passive] / the meaning and use of what is put in, taken in, or operated on by any process or system.
Figure. A component diagram showing the inputs and semantic counterparts of a balanced network model.
Data capture is defined as the process of collecting data electronically, allowing it to be stored, searched, or organised more efficiently. In a decentralised network, data capture requires the provision of immutable fields in order to capture and store collected data. In the above component diagram, all elements and characteristics of data capture are depicted in the southern hemispherical Semantic domain.
Mission and Scope (as defined in the WG charter)
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 Requests for Proposal (RFPs) and additional guidance to utility and service providers regarding 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)
Chairs
Mailing List
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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)
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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 harmonizes database modelsdata semantics. 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, promising to significantly enhance the ability to pool data more effectively for improved data science, statistics, analytics and other meaningful services.
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Blinding Identity Taxonomy (BIT)
BIT is a defensive tool created for the purpose of reducing the risk of identifying governing entities within blinded datasets. BIT contains a list of elements to be referred to by schema issuers for flagging attributes which may contain identifying information about governing entities. Once attributes have been flagged, any marked data can be removed or encrypted during the data lifecycle.
- BIT in PDF format: https://docs.kantarainitiative.org/Blinding-Identity-Taxonomy-Report-Version-1.0.pdf
- BIT in HTML format: https://docs.kantarainitiative.org/Blinding-Identity-Taxonomy-Report-Version-1.0.html
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