Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33
- Type
- D - Journal article
- DOI
-
10.1186/s40537-019-0200-9
- Title of journal
- Journal of Big Data
- Article number
- 37
- First page
- 1
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-1115
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2019
- 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
-
3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper presents a new method of memory rearrangement for pattern-mining algorithms which allows efficient vectorisation of stencil-like loops. In the paper the new method’s efficiency is demonstrated through application to the elastic wave propagation problem; the method’s wider significance was demonstrated via collaboration with Novosibirsk State University (NSU) by demonstration of the novel parallelization methods (SIMD vectorisation, multicore, and cluster) to the problems of computational geophysics and astrophysics (see “Multilevel parallelization: Grid methods for solving direct and inverse problems”, Titarenko et al.. at Russian Supercomputing Days 2016, https://link.springer.com/chapter/10.1007/978-3-319-55669-7_10 )
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -