la_vector
ALEX
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la_vector | ALEX | |
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1 | 1 | |
35 | 648 | |
- | 1.2% | |
0.0 | 2.8 | |
over 1 year ago | about 2 months ago | |
C++ | C++ | |
Apache License 2.0 | MIT License |
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la_vector
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PGM Indexes: Learned indexes that match B-tree performance with 83x less space
Hi Jouni!
You may find interesting these other papers of ours:
- The ALENEX21 paper "A 'learned' approach to quicken and compress rank/select dictionaries" (http://pages.di.unipi.it/vinciguerra/publication/learned-ran..., https://github.com/gvinciguerra/la_vector), where we introduce a compressed bitvector supporting efficient rank and select queries, which is competitive with several well-established implementations of succinct data structures.
- The ICML20 paper "Why are learned indexes so effective?" (http://pages.di.unipi.it/vinciguerra/publication/learned-ind...) where we prove that, under some general assumptions on the input data, the space of the PGM-index is actually O(n/B^2) whp (versus Θ(n/B) of classic B-trees).
ALEX
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PGM Indexes: Learned indexes that match B-tree performance with 83x less space
Also, a learned index from Microsoft: https://github.com/microsoft/ALEX
What are some alternatives?
RadixSpline - A Single-Pass Learned Index
PGM-index - 🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
SOSD - A Benchmark for Learned Indexes