Let’s say your collection of Anytown graveyard records have a whole lot of disparate columns of data: most records have the given name and/or surname (but some don’t), some have an exact date of death while others have just a year (or nothing at all), some list a spouse’s or parent’s first name (but not everyone), some have inscription text, some have location data…
And then your collection of Anytown obituaries have some, but not all, of the same data possibilities, plus some new fields like the name of the newspaper and the date of publication.
Making an online database out of each of these records sets would be tricky enough. But what if you want to have only one online search engine for this data, searching both databases simultaneously, and finding possible links between their records? After all, a lot of the people mentioned in the Anytown obituaries wound up buried in the Anytown cemeteries, right? How can we find those people — with only one search?
That’s where LeafSeek comes in. LeafSeek lets you combine databases of all shapes and sizes into one big unified searchable interface that finds possible connections between your datasets. You can limit your search to one dataset, or encompass them all. Common data types like given names, surnames, and towns are recognized among multiple fields in multiple databases. Other fields, like record years and occupations, can be broken out into searchable facets to find links between different pieces of data.
LeafSeek can also handle data that’s stored in many different formats, such as:
- Adobe PDF files
- CSV (comma-separated value) files
- JSON files
- Microsoft Excel or other spreadsheet files
- Microsoft Word documents or other word processing documents
- MySQL databases
- …and more!
So whether you keep your data in a big spreadsheet or already have an online database system, you can import your data into LeafSeek quickly.
(As a benchmark, we’ve found that indexing about 200,000 records from 60 different MySQL databases takes less than five minutes! And that’s just for a one-time indexing job to initially load the data into LeafSeek. Regular searches of that data by users usually take less than 25 milliseconds to complete on the back-end, without the server breaking a sweat.)