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r*-trees work doesn't work well when the number of dimensions stored in the index is much higher than the logarithm of the number of indexed entries, and this i
by mattashii 3y ago
r*-trees work doesn't work well when the number of dimensions stored in the index is much higher than the logarithm of the number of indexed entries, and this is a prevailing property of divide-and-conquer spatial index types when the keyspace is divided based on a single dimension at a time.
As vectors regularly have 100+ dimensions, normal spatial indexing methods applied to vectors wouldn't be very efficient for anything with much less than 2^100 index entries; which is quite suboptimal for most datasets that you would want to have indexed.
- KirillPanov 3y agoAlso the distance metric for r*-trees is just plain wacky for anything other than low-dimensional Euclidean space. Even if you could make it perform well, it would not do what you want.
- modulovalue 3y agoAre you saying this because r-trees expect a proper metric space, and people have the need to index datasets over non-metric spaces?
- pornel 3y agoThe curse of dimensionality creates a seemingly paradoxical situation where you have a vast vast search space, but everything is incredibly close to each other. Space subdivision algorithms become ineffective.