A Parameterized Study of Maximum Generalized Pattern Matching Problems
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2456
- Type
- D - Journal article
- DOI
-
10.1007/s00453-015-0008-8
- Title of journal
- Algorithmica
- Article number
- -
- First page
- 1
- Volume
- 75
- Issue
- 1
- ISSN
- 0178-4617
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
A - Algorithms
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Pattern matching is one of the most fundamental problems in computer science, whose main applications lie in the analyses of a wide variety of data reaching from textual information and image data to DNA data. We provide the first comprehensive analysis of this important problem within the context of parameterized complexity; leading to efficient algorithms for many classes of input instances. Mike Fellows, the founder of parameterized complexity, wrote in his review for the author's habilitation thesis: "I expect this to be a landmark result in this natural application area for the parameterized view."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -