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Burst Tries: A Fast, Efficient Data Structure for String Keys (2002)
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| Venue: | ACM Transactions on Information Systems |
| Citations: | 43 - 10 self |
BibTeX
@ARTICLE{Heinz02bursttries:,
author = {Steffen Heinz and Justin Zobel and Hugh E. Williams},
title = {Burst Tries: A Fast, Efficient Data Structure for String Keys},
journal = {ACM Transactions on Information Systems},
year = {2002},
volume = {20},
pages = {192--223}
}
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Abstract
Many applications depend on efficient management of large sets of distinct strings in memory. For example, during index construction for text databases a record is held for each distinct word in the text, containing the word itself and information such as counters. We propose a new data structure, the burst trie, that has significant advantages over existing options for such applications: it requires no more memory than a binary tree; it is as fast as a trie; and, while not as fast as a hash table, a burst trie maintains the strings in sorted or near-sorted order. In this paper we describe burst tries and explore the parameters that govern their performance. We experimentally determine good choices of parameters, and compare burst tries to other structures used for the same task, with a variety of data sets. These experiments show that the burst trie is particularly effective for the skewed frequency distributions common in text collections, and dramatically outperforms all other data structures for the task of managing strings while maintaining sort order.
Keyphrases
string key burst trie efficient data structure burst try data structure significant advantage skewed frequency distribution many application efficient management distinct string distinct word sort order good choice index construction data set hash table near-sorted order text collection binary tree new data structure large set
