The skd-tree partitions space into multiple slices per node along one dimension, compresses splitters, and applies a constant number of SIMD instructions per node to reduce levels and computations for multi-dimensional queries.
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3 Pith papers cite this work. Polarity classification is still indexing.
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RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
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.
citing papers explorer
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In-memory Multidimensional Indexing Using the skd-tree
The skd-tree partitions space into multiple slices per node along one dimension, compresses splitters, and applies a constant number of SIMD instructions per node to reduce levels and computations for multi-dimensional queries.
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RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
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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.