Rec. ITU-T Y.3174 (02/2020) Framework for data handling to enable machine learning in future networks including IMT-2020
Summary
History
FOREWORD
Table of Contents
1 Scope
2 References
3 Definitions
     3.1 Terms defined elsewhere
     3.2 Terms defined in this Recommendation
4 Abbreviations and acronyms
5 Conventions
6 Introduction
7 High-level requirements for data handling
     7.1 High-level requirements
          7.1.1 Storage requirements in ML data collection
          7.1.2 ML-data-collection-level
          7.1.3 ML-data-collection-timing
          7.1.4 ML-data-collection-statistics
          7.1.5 ML-data-collection-data model
          7.1.6 ML-data-collection-specification
     7.2 High-level requirements for ML data output
          7.2.1 ML-data-output-levels
          7.2.2 ML-output-data model
          7.2.3 ML-output-policy
          7.2.4 ML-output-timing
     7.3 High-level requirements for ML data processing
          7.3.1 ML-processing-models
          7.3.2 ML-processing-data
          7.3.3 ML-processing-levels
          7.3.4 ML-processing-KPI
8 High-level framework for data handling
     8.1 Framework components and interfaces
          8.1.1 Reused components defined in ITU-T Y.3172
          8.1.2 Newly defined data handling architectural components
               8.1.2.1 ML metadata store
               8.1.2.2 API-g
               8.1.2.3 API-s
               8.1.2.4 ML data broker control plane
               8.1.2.5 ML data broker user plane
               8.1.2.6 ML database
          8.1.3 Data handling framework supporting aspects
               8.1.3.1 ML model repository
     8.2 High-level architecture
     8.3 Sequence diagrams for ML data handling
          8.3.1 Instantiation of data handling framework
          8.3.2 Addition of new SRC in data handling framework
          8.3.3 DM not present in the ML metadata store
9 Security considerations
Appendix I  Example realizations of the data handling framework
Appendix II  Mapping of requirements and capabilities of cloud computing based big data
Bibliography