An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.DB 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MountDB extends RocksDB with Memtable-level model reuse and a block-aware learned disk index, reporting up to 1.5X write and 2.1X read throughput over state-of-the-art on large-scale workloads.
citing papers explorer
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Evaluating Learned Spatial Indexes
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
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A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification
MountDB extends RocksDB with Memtable-level model reuse and a block-aware learned disk index, reporting up to 1.5X write and 2.1X read throughput over state-of-the-art on large-scale workloads.