{"id":464838,"date":"2024-10-20T10:37:13","date_gmt":"2024-10-20T10:37:13","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-iso-iec-5259-42024\/"},"modified":"2024-10-26T19:35:05","modified_gmt":"2024-10-26T19:35:05","slug":"bs-iso-iec-5259-42024","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-iso-iec-5259-42024\/","title":{"rendered":"BS ISO\/IEC 5259-4:2024"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
2<\/td>\n | undefined <\/td>\n<\/tr>\n | ||||||
7<\/td>\n | Foreword <\/td>\n<\/tr>\n | ||||||
8<\/td>\n | Introduction <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | 1 Scope 2 Normative references 3 Terms and definitions <\/td>\n<\/tr>\n | ||||||
11<\/td>\n | 4 Symbols and abbreviated terms 5 Data quality process principles 6 Data quality process framework 6.1 General <\/td>\n<\/tr>\n | ||||||
13<\/td>\n | 6.2 Data quality planning <\/td>\n<\/tr>\n | ||||||
14<\/td>\n | 6.3 Data quality evaluation 6.4 Data quality improvement 6.5 Data quality process validation <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | 6.6 Using the DQPF 7 Data quality process for ML 7.1 General <\/td>\n<\/tr>\n | ||||||
16<\/td>\n | 7.2 Data requirements <\/td>\n<\/tr>\n | ||||||
17<\/td>\n | 7.3 Data planning 7.4 Data acquisition <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 7.5 Data preparation 7.5.1 General 7.5.2 Supervised ML 7.5.3 Unsupervised ML 7.5.4 Semi-supervised ML <\/td>\n<\/tr>\n | ||||||
19<\/td>\n | 7.5.5 Dataset composition 7.5.6 Data labelling 7.5.7 Data annotation <\/td>\n<\/tr>\n | ||||||
20<\/td>\n | 7.5.8 Data quality assessment <\/td>\n<\/tr>\n | ||||||
21<\/td>\n | 7.5.9 Data quality improvement <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | 7.5.10 Data de-identification <\/td>\n<\/tr>\n | ||||||
24<\/td>\n | 7.5.11 Data encoding. 7.6 Data provisioning 7.6.1 General 7.6.2 Supervised ML 7.6.3 Unsupervised ML 7.6.4 Semi-supervised ML 7.7 Data decommissioning <\/td>\n<\/tr>\n | ||||||
25<\/td>\n | 8 Data labelling methods and process 8.1 General 8.2 Data labelling principles 8.3 Data labelling methods <\/td>\n<\/tr>\n | ||||||
26<\/td>\n | 8.4 Data labelling process 8.4.1 General 8.4.2 Labelling specifications 8.4.3 Labelling participant roles <\/td>\n<\/tr>\n | ||||||
27<\/td>\n | 8.4.4 Labelling tools or platforms 8.4.5 Labelling task establishment 8.4.6 Labelling task assignment <\/td>\n<\/tr>\n | ||||||
28<\/td>\n | 8.4.7 Labelling process control 8.4.8 Labelling result quality checking 8.4.9 Labelling result revision <\/td>\n<\/tr>\n | ||||||
29<\/td>\n | 9 Roles of participants 9.1 General 9.2 Data planner 9.3 Data originator 9.4 Data collector 9.5 Data engineer 9.6 Data holder 9.7 Data user <\/td>\n<\/tr>\n | ||||||
30<\/td>\n | 10 Data quality process for semi-supervised ML 10.1 General 10.2 Data requirements 10.3 Data planning 10.4 Data acquisition 10.5 Data preparation 10.6 Data provisioning <\/td>\n<\/tr>\n | ||||||
31<\/td>\n | 10.7 Data decommissioning 11 Data quality process for reinforcement learning 11.1 General 11.2 Data requirements 11.3 Data planning 11.4 Data acquisition 11.5 Data preparation 11.5.1 General process <\/td>\n<\/tr>\n | ||||||
32<\/td>\n | 11.5.2 Data recording 11.6 Data provisioning 11.7 Data decommissioning 12 Data quality process for analytics 12.1 General 12.2 Data requirements 12.3 Data planning <\/td>\n<\/tr>\n | ||||||
33<\/td>\n | 12.4 Data acquisition 12.4.1 General 12.4.2 Data loading 12.4.3 Data storage 12.5 Data preparation 12.5.1 General 12.5.2 Data cleaning 12.5.3 Data transformation <\/td>\n<\/tr>\n | ||||||
34<\/td>\n | 12.5.4 Data aggregation 12.5.5 Data quality assessment 12.5.6 Data quality improvement <\/td>\n<\/tr>\n | ||||||
35<\/td>\n | 12.6 Data provisioning 12.7 Data decommissioning <\/td>\n<\/tr>\n | ||||||
36<\/td>\n | Bibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence. Data quality for analytics and machine learning (ML) – Data quality process framework<\/b><\/p>\n |