Introduction of the Data Modeling & Representation Working Group
Presentation/Discussion - Authentic Data - Simple in Principle, ...
Agenda Items and Notes (including all relevant links)
Time
Agenda Item
Lead
Notes
5 min
Start recording
Welcome & antitrust notice
Introduction of new members
Agenda review
Chairs
Antitrust Policy Notice:Attendees are reminded to adhere to the meeting agenda and not participate in activities prohibited under antitrust and competition laws. Only members of ToIP who have signed the necessary agreements are permitted to participate in this activity beyond an observer role.
News or events of interest to Governance Stack WG members:
Future Topics
Requirements on Authentic Data:
Use of identifiers for Data and Authorities used to sign the data. Is this KERI or something simpler
General model for packaging data, it's identifier(s), etc. Model for
Interplay between Data and Governance management and authority (who signs?)
Detailing the (Authentic) Data Lifecycle and how it is different from current practice
Detailing iterative development of a dataset (infrequently a "linear" process) (presentation?)
Transformation & translation - mapping data from one schema to another. Requirements for an SSI Trust/Authentic model
Role of Ontologies (presentation)
Experience with layered/OCA schemas (presentation)
20 mins
Authentic Data
Chairs
Authentic Data - A Published Dataset - available for use/consumption by 3rd parties is built on data and data governance used to design and build a re-usable dataset from "first principles"
Authentic Data is data that has been crypto-signed by an "authority" (role) using their private key for which users of the data can verify using the "authority"s public key.
Creating publishable/sharable data is via a Data Lifecycle where the data is initially captured/collected, then checked for input and consistency errors, cleaned of outliers, duplicates and checked for overall correctness. Each of those stages needs to be persisted and linked to the dataset that is published for 3rd party use that are part of the data provenance (trust) chain
The Data needs to be designed with respect to structure, metadata, and "fitness for purpose". Governance needs to be designed to ensure accuracy, consistency and correctness
Governance drives requirements for error, consistency and accuracy as an active part of the data lifecycle. Data Governance is the strategy, Data Stewardship is oversight the data lifecycle
Discussion at:
14 mins: Kevin: Need a definition of "authentic"
authentic: having an origin supported by unquestionable evidence; authenticated; verified: (in other words, securely attributable to a cryptonymous identifier)