Results 11 to 20 of about 128,390 (257)
LK-Index: A Learned Index for KNN Queries
IEEE AccessThe k-Nearest Neighbor (kNN) search is a crucial problem in database and data mining, especially in high-dimensional space. However, traditional kNN algorithms based on distance metrics and brute-force search often have low search efficiency and accuracy,
Yongxin Peng
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On Nonlinear Learned String Indexing
IEEE Access, 2023 We investigate the potential of several artificial neural network architectures to be used as an index on a sorted set of strings, namely, as a mapping from a query string to (an estimate of) its lexicographic rank in the set, which allows solving some ...
Paolo Ferragina +3 more
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Benchmarking learned indexes [PDF]
Proceedings of the VLDB Endowment, 2020 Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines.
Ryan Marcus +7 more
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Learning Multi-Dimensional Indexes [PDF]
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, 2020 Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes such as R-trees, or use complex sort orders (e.g., Z-ordering).
Nathan, Vikram +3 more
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The Potential of Learned Index Structures for Index Compression [PDF]
Proceedings of the 23rd Australasian Document Computing Symposium, 2018 Will appear in the proceedings of ADCS ...
Harrie Oosterhuis +2 more
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Are updatable learned indexes ready?
Proceedings of the VLDB Endowment, 2022 Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels ...
Chaichon Wongkham +5 more
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Updatable learned index with precise positions [PDF]
Proceedings of the VLDB Endowment, 2021 Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset.
Jiacheng Wu 0001 +5 more
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Applied Sciences, 2022 Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still ...
Yuxiang Sun, Seok-Ju Chun, Yongju Lee
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Symmetric Single Index Learning
CoRR, 2023 Few neural architectures lend themselves to provable learning with gradient based methods. One popular model is the single-index model, in which labels are produced by composing an unknown linear projection with a possibly unknown scalar link function.
Aaron Zweig, Joan Bruna
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Proceedings of the Third International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, 2020 Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data.
Andreas Kipf +6 more
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