HIRE is a hybrid learned index that achieves up to 41.7x higher throughput under mixed workloads and reduces tail latency by up to 98% compared to state-of-the-art learned and traditional indexes.
Why are learned indexes so effective but sometimes ineffective?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.DB 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
LiLIS is a lightweight prototype that combines learned indices with spatial partitioning in distributed frameworks to support point, range, kNN, and join queries with reduced latency and construction cost.
citing papers explorer
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HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads
HIRE is a hybrid learned index that achieves up to 41.7x higher throughput under mixed workloads and reduces tail latency by up to 98% compared to state-of-the-art learned and traditional indexes.
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LiLIS: A Lightweight Distributed Learned Index Framework for Spatial Decision Analysis
LiLIS is a lightweight prototype that combines learned indices with spatial partitioning in distributed frameworks to support point, range, kNN, and join queries with reduced latency and construction cost.