It's a pretty neat algorithm from a paper in 2019 for the application "to index k-mers of DNA samples or q-grams from text documents". You can take a collection of bloom filters built for documents and then combine them together to have a single filter that will tell you which docs it maps to. Like an inverted index meets a bloom filter.
I'm using it in a totally different domain for an upcoming release in InfluxDB (time series database).
I've been following this team's work for a while and what they're doing is super interesting. The file format they created and put into the LF, Vortex, is very welcome innovation in the space: https://github.com/vortex-data/vortex
I'm excited to start doing some experimentation with Vortex to see how it can improve our products.
It's a pretty neat algorithm from a paper in 2019 for the application "to index k-mers of DNA samples or q-grams from text documents". You can take a collection of bloom filters built for documents and then combine them together to have a single filter that will tell you which docs it maps to. Like an inverted index meets a bloom filter.
I'm using it in a totally different domain for an upcoming release in InfluxDB (time series database).
There's also code online here: https://github.com/bingmann/cobs