Rank-Select Indices Without Tears
September 07, 2017 Β· Declared Dead Β· π Workshop on Algorithms and Data Structures
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tim Baumann, Torben Hagerup
arXiv ID
1709.02377
Category
cs.DS: Data Structures & Algorithms
Citations
14
Venue
Workshop on Algorithms and Data Structures
Last Checked
3 months ago
Abstract
A rank-select index for a sequence $B=(b_1,\ldots,b_n)$ of $n$ bits is a data structure that, if provided with an operation to access $Ξ(\log n)$ arbitrary consecutive bits of $B$ in constant time (thus $B$ is stored outside of the data structure), can compute $\mathit{rank}_B(j)=\sum_{i=1}^j b_i$ for given $j\in\{0,\ldots,n\}$ and $\mathit{select}_B(k)=\min\{j:\mathit{rank}_B(j)\ge k\}$ for given $k\in\{1,\ldots,\sum_{i=1}^n b_i\}$. We describe a new rank-select index that, like previous rank-select indices, occupies $O(n\log\log n/\log n)$ bits and executes $\mathit{rank}$ and $\mathit{select}$ queries in constant time. Its derivation is intended to be particularly easy to follow and largely free of tedious low-level detail, its operations are given by straight-line code, and we show that it can be constructed in $O(n/\log n)$ time.
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