[2406.00221] Counting on General Run-Length Grammars
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Data Structures and Algorithms

arXiv:2406.00221 (cs)
[Submitted on 31 May 2024 (v1), last revised 22 Oct 2024 (this version, v3)]

Title:Counting on General Run-Length Grammars

Authors:Gonzalo Navarro, Alejandro Pacheco
View PDF HTML (experimental)
Abstract:We introduce a data structure for counting pattern occurrences in texts compressed with any run-length context-free grammar. Our structure uses space proportional to the grammar size and counts the occurrences of a pattern of length $m$ in a text of length $n$ in time \(O(m\log^{2+\epsilon} n)\), for any constant \(\epsilon > 0\). This closes an open problem posed by Christiansen et al.~[ACM TALG 2020] and enhances our abilities for computation over compressed data; we give an example application.
Subjects: Data Structures and Algorithms (cs.DS)
Cite as: arXiv:2406.00221 [cs.DS]
  (or arXiv:2406.00221v3 [cs.DS] for this version)
  https://doi.org/10.48550/arXiv.2406.00221

Submission history

From: Alejandro Pacheco [view email]
[v1] Fri, 31 May 2024 22:40:42 UTC (81 KB)
[v2] Wed, 24 Jul 2024 16:01:17 UTC (82 KB)
[v3] Tue, 22 Oct 2024 11:42:20 UTC (293 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.DS
< prev   |   next >
new | recent | 2024-06
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)