|Version 3 (modified by 14 years ago) ( diff ),
Data De-duplication Blue Print
We often get duplicate data in a system, especially if we do Bulk Imports from other data sources but also because many users have the tendancy to enter new records, rather than reusing existing records.
- Identifying Duplicate Records (using the Jaro Winkler Distance)
- Comparing the fields used in the "represent" of this table is a good start.
- In order to determine if the records are in fact duplicate, the user should have the option to open up the records and somehow see where they are referred to.
- Merging Duplicate Records (see wireframe)
- Replacing Duplicate Records (must be work with offline instances over sync too)
A complete specifications can be found at http://wiki.sahanafoundation.org/doku.php/foundation:gsoc_kohli:import:duplicates
Different Processes Identifying Duplicate Records
Some resources may have unique processes for identifying duplicates:
Identifying duplicate locations really should involve significant use of maps for context of the two point being checked, perhaps fields showing great-circle distance from each other, and if we have hierarchy polygons available, then performing spatial analysis to see if it is the same town in the same region or two towns that share the same name, but are in different regions? Whereas peoples names may use Soundex, addresses, phone number etc. Document deduping could use SHA1 checksum analysis of the file to detect dupes (e.g. there is a very low probability of two files sharing the same SHA1 hash), and think that an SHA1 hash should be calculated for a document or image file at time of upload.
- A basic Locations de-duplicator is now in Trunk. (gis/location_duplicates) This tool will replace all references to Location A (Old) to Location B (new) and delete Location A.
- It does not provide a method for finding duplicate records
- If will not work across data in multiple (synced) instances