Learning Algorithms for Automatic Data Structure Design
Summary: Learning-based search for automatic key-value data-structure design guided by workload specs, navigating a design space of ~10^100 possibilities. Outputs an abstract syntax tree for code generation, delivering near-optimal designs in seconds. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Demi Guo
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076824554 |
| 101 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049778866 |
| 562 | Tree Indexing on Solid State Drives | 2010 | VLDB | 0.00020035385 |
| 608 | Monkey: Optimal Navigable Key-Value Store | 2017 | SIGMOD | 0.00019233548 |
| 1,113 | Cache-Oblivious String B-trees | 2006 | PODS | 0.00013885755 |
| 2,153 | The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models | 2018 | SIGMOD | 9.418541e-05 |
| 2,387 | Bed-Tree: An All-Purpose Index Structure for String Similarity Search Based on Edit Distance | 2010 | SIGMOD | 8.9075095e-05 |
| 2,606 | Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn | 2019 | CIDR | 8.4621503e-05 |
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