Ongoing PoCs: Difference between revisions
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This PoC proposes the development of a functional, open-source toolset to validate and score dataset quality based on the standardized Data Quality Metrics and verification formulas defined in ETSI TR 104 180 (Development and identification of Data Quality Metrics). The PoC will deliver a working, end-to-end pipeline demonstrating the automated assessment of data quality for a target dataset. | This PoC proposes the development of a functional, open-source toolset to validate and score dataset quality based on the standardized Data Quality Metrics and verification formulas defined in ETSI TR 104 180 (Development and identification of Data Quality Metrics). The PoC will deliver a working, end-to-end pipeline demonstrating the automated assessment of data quality for a target dataset. | ||
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Latest revision as of 15:11, 15 January 2026
Overview of Reports
| PoC Num | Description | Final Report |
|---|---|---|
| PoC 1 | Data Quality Validation System | Ongoing |
List of PoCs
This page provides the list of on-going ETSI TC DATA PoCs. Click on them to learn further details.
The following DATA Proofs of Concept are developed according to the ETSI TC DATA Proof of Concept Framework. DATA Proofs of Concept are intended to demonstrate viable technology. Results are fed back to the ETSI DATA Technical Committee
Neither ETSI, the TC DATA, nor their members make any endorsement of any product or implementation claiming to demonstrate or conform to TC specification. No verification or test has been performed by ETSI on any part of these DATA Proofs of Concept.
To request a ETSI DATA PoC, please contact [email protected].
PoC 1 Data Quality Validation System
Sejong University - EGM - TTA - Daejeon University - CNIT
This PoC proposes the development of a functional, open-source toolset to validate and score dataset quality based on the standardized Data Quality Metrics and verification formulas defined in ETSI TR 104 180 (Development and identification of Data Quality Metrics). The PoC will deliver a working, end-to-end pipeline demonstrating the automated assessment of data quality for a target dataset.
