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ONS Strategy: When NOT to use database queries

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Surname/tag: ONE_NAME_STUDIES
Profile manager: Chris Gorman private message [send private message]
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Gostwick Name Study


So rarely do I read or see discussed the strategy for a One Name Study.

Most are familiar with using logic in genealogy (I hope), and many people are familiar with using databases to help us with this logic.

When is the wrong time to use a query result for evidence in your one name study?

The answer is simple, but the technique may not be self evident. You shouldnt use queries when you arent close to having every single name in your database!

Running a query, finding a match, then finding 10 more people to put in the database, indicates you arent ready.

In Quality Assurance, we look at 'defect discovery rates' and 'fix rates', looking for the place where the two cross like a big X. This tells us where we are both for stability and for schedule.

With your ONS, there isnt a fix rate, but you do have a rate of discovery of new people with the 'name' you are studying.

e.g. 10 people search for 'Jones' surnames in Texas, USA, they find a rate of X new people per week. As that number of new finds approaches zero, you know you are close to being ready to apply statistics for query logic.

Close, but not ready. Consider that you will leverage placenames as a critical part of your query. Any inconsistency in placenames will degrade the potential for query matches.

Next Up: Sanitize EVERY placename. Find every spelling mistake. Normalize every single town name...even it means just placing that 'corrected' placename into the description. Queries can find description text just fine.


Example of where my database sits:

Gostwick ONS discovery rate: [2021: +1 new profile, 14th century, Berwick] (post 20th century are not included in find rates)

Gostwick Placename Sanitization: maybe 85%. Its a moving target since it is influenced by every fact entry. Over one 'person week' invested in placename error correction, with most errors fixed, and most placenames 'aliased/tokenized'


~Chris





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