A Faster Implementation of Online Run-Length Burrows-Wheeler Transform
April 18, 2017 Β· Declared Dead Β· π International Workshop on Combinatorial Algorithms
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tatsuya Ohno, Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto
arXiv ID
1704.05233
Category
cs.DS: Data Structures & Algorithms
Citations
14
Venue
International Workshop on Combinatorial Algorithms
Last Checked
3 months ago
Abstract
Run-length encoding Burrows-Wheeler Transformed strings, resulting in Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive strings. We propose a new algorithm for online RLBWT working in run-compressed space, which runs in $O(n\lg r)$ time and $O(r\lg n)$ bits of space, where $n$ is the length of input string $S$ received so far and $r$ is the number of runs in the BWT of the reversed $S$. We improve the state-of-the-art algorithm for online RLBWT in terms of empirical construction time. Adopting the dynamic list for maintaining a total order, we can replace rank queries in a dynamic wavelet tree on a run-length compressed string by the direct comparison of labels in a dynamic list. The empirical result for various benchmarks show the efficiency of our algorithm, especially for highly repetitive strings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted