The adaptive radix tree: ARTful indexing for main-memory databases

By Viktor Leis, Alfons Kemper, Thomas Neumann. IEEE International Conference on Data Engineering, 2013

469 citations recorded by the source corpus.

How researchers see this paper

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In-Memory Big Data Management and Processing: A Survey (2015, methodology): “In addition, for memory-resident data structures, various cache-conscious indexes have been proposed such as Cache Sensitive Search Trees [81], Cache Sensitive B þ -Trees [82], Fast Architecture Sensitive Trees [85], and Adaptive Radix Trees [87].”
ALEX: An Updatable Adaptive Learned Index (2019, background): “Masstree [24] and ART (Adaptive Radix Tree) [20] combine the idea of B+Tree and trie to reduce cache misses.”
The Case for Learned Index Structures (2017, background): “een nodes. There exist also a vast array of research on index structures for text, such as tries/radix-trees [16, 37, 23], or other exotic index structures, which combine ideas from B-Trees and tries [39]. However, all of these approaches are orthogonal to the idea of learned indexes as none of them”