Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.
Making in-memory learned indexes efficient on disk.Proceedings of the ACM on Management of Data, 2(3):151:1–151:27, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
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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Residual-Entropy Accounting for Routed Atom-Budgeted Learned Indexes
Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.
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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.