Introduction
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Dataset Aging Methodology
Once we have the latest last modified date dates for all of a dataset's resources , we can compare it to and the last date the dataset was manually confirmed as updated in the UI if available. The , we can calculate the latest of those datesall of them, which we refer to as “last modified date” from here on, . This is used to calculate the dataset’s age and combined with the update frequency, we can ascertain the freshness of the dataset.
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Update Frequency | Dataset age state thresholds (how old must a dataset be for it to have this status) | |||
|---|---|---|---|---|
Fresh | Not Fresh | |||
Up-to-date | Due | Overdue | Delinquent | |
Daily | 0 days old | 1 day old due_age = f | 2 days old overdue_age = f + 2 | 3 days old delinquent_age = f + 3 |
Weekly | 0 - 6 days old | 7 days old due_age = f | 14 days old overdue_age = f + 7 | 21 days old delinquent_age = f + 14 |
Fortnightly | 0 - 13 days old | 14 days old due_age = f | 21 days old overdue_age = f + 7 | 28 days old delinquent_age = f + 14 |
Monthly | 0 -29 days old | 30 days old due_age = f | 44 days old overdue_age = f + 14 | 60 days old delinquent_age = f + 30 |
Quarterly | 0 - 89 days old | 90 days old due_age = f | 120 days old overdue_age = f + 30 | 150 days old delinquent_age = f + 60 |
Semiannually | 0 - 179 days old | 180 days old due_age = f | 210 days old overdue_age = f + 30 | 240 days old delinquent_age = f + 60 |
Annually | 0 - 364 days old | 365 days old due_age = f | 425 days old overdue_age = f + 60 | 455 days old delinquent_age = f + 90 |
Never | Always | Never | Never | Never |
Live | Always | Never | Never | Never |
As Needed | Always | Never | Never | Never |
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