# Dynamic Z-Fast Tries

@inproceedings{Belazzougui2010DynamicZT, title={Dynamic Z-Fast Tries}, author={Djamal Belazzougui and Paolo Boldi and Sebastiano Vigna}, booktitle={SPIRE}, year={2010} }

We describe a dynamic version of the z-fast trie, a new data structure inspired by the research started by the van Emde Boas trees [12] and followed by the development of y-fast tries [13]. The dynamic z-fast trie is a very simple, uniform data structure: given a set S of n variable-length strings, it is formed by a standard compacted trie on S (with two additional pointers per node), endowed with a dictionary of size n - 1. With this simple setup, the dynamic z-fast trie provides predecessors…

## 30 Citations

Faster Dynamic Compact Tries with Applications to Sparse Suffix Tree Construction and Other String Problems

- Computer Science
- 2013

The dynamic compact trie data structure can be used to solve online construction of the sparse suffix tree (SST) in O((n= )(f (n) + log )) time and O(n log ) bits for evenly taken space r = (log n) with more general bounds.

Applications of Succinct Dynamic Compact Tries to Some String Problems

- Computer Science
- 2013

This paper's recent work on succinct dynamic compact tries that stores a set of strings of total length n in O(n log ) space supporting pattern matching and in- sert/delete operations in O((jPj= )f (n) time) is reported.

c-trie++: A dynamic trie tailored for fast prefix searches

- Computer ScienceInformation and Computation
- 2021

Packed Compact Tries: A Fast and Efficient Data Structure for Online String Processing

- Computer ScienceIWOCA
- 2016

A new data structure called the packed compact trie (packed c-trie) which stores a set S of k strings of total length n in bits of space and supports fast pattern matching queries and updates and discusses applications.

Top tree compression of tries. (English)

- Computer Science
- 2022

A compressed representation of tries based on top tree compression that works on a standard, comparison-based, pointer machine model of computation and supports eﬃcient preﬁx search queries and develops several interesting data structures that work on a pointer machine and are of independent interest.

Speeding Up Compact Trie Structures on Word RAM and Its Applications

- Computer Science
- 2012

This paper presents a faster compact trie for storing a set of K strings with total length N letters in O(N log σ +K logN) bits that supports prefix search (from an arbitrary node) and insert operations in O (⌈P/α⌉ √ w) worst-case time and any linearspace dictionary for integers with fast predecessor and Insert operations, while keeping the linear space.

Top Tree Compression of Tries

- Computer ScienceISAAC
- 2019

This work shows how to preprocess a set of strings of total length $n$ over an alphabet of size $\sigma$ into a compressed data structure of worst-case optimal size $O(n/\log_\sigma n)$ that determines if $P$ is a prefix of one of the strings in time $O(\min(m\log \sigma,m + \log n)$.

Packed Compact Tries: A Fast and Efficient Data Structure for Online String Processing

- Computer ScienceIEICE Trans. Fundam. Electron. Commun. Comput. Sci.
- 2017

The experiments show that the packed c-tries are faster than the standard compact tries and the sparse suffix tree for a string of length $n$ over prefix codes with $k$ sampled positions, such as evenly-spaced and word delimited sparse suffix trees, can be constructed online in worst-case time.

Top Tree Compression of Tries

- Computer ScienceAlgorithmica
- 2021

This work presents a compressed representation of tries based on top tree compression that works on a standard, comparison-based, pointer machine model of computation and supports efficient prefix search queries and develops several interesting data structures that work on a pointer machine and are of independent interest.

An In-Depth Analysis of Data Structures Derived from van-Emde-Boas-Trees

- Computer Science
- 2015

This thesis gives an overview of several different data structures for the w-bit word RAM on bounded integer universes, i.e. U = {0, . . . , 2 − 1}.

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