Gast ::
| Hauptseite > Publikationsdatenbank > Polytope: an algorithm for efficient feature extraction on hypercubes > print |
| Hauptseite > Publikationsdatenbank > Polytope: an algorithm for efficient feature extraction on hypercubes > print |
| 001 | 1052199 | ||
| 005 | 20260203123451.0 | ||
| 024 | 7 | _ | |a 10.1186/s40537-025-01306-3 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2026-00834 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2026-00834 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Leuridan, Mathilde |0 P:(DE-Juel1)207974 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Polytope: an algorithm for efficient feature extraction on hypercubes |
| 260 | _ | _ | |a Heidelberg [u.a.] |c 2025 |b SpringerOpen |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1769067995_24863 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Data extraction algorithms on data hypercubes, or datacubes, are traditionally only capable of cutting boxes of data along the datacube axes. For many use cases however, this returns much more data than users actually need, leading to an unnecessary consumption of I/O resources. In this paper, we propose an alternative feature extraction technique, which carefully computes the indices of data points contained within user-requested shapes. This enables data storage systems to only read and return bytes useful to user applications from the datacube. Our main algorithm is based on high-dimensional computational geometry concepts and operates by successively reducing polytopes down to the points contained within them. We analyse this algorithm in detail before providing results about its performance and scalability. In particular, we show it is possible to achieve data reductions of up to 99% using this algorithm instead of current state of practice data extraction methods, such as meteorological field extractions from ECMWF’s FDB data store, where feature shapes are extracted a posteriori as a post-processing step. As we discuss later on, this novel extraction method will considerably help scale access to large petabyte size data hypercubes in a variety of scientific fields. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a Earth System Data Exploration (ESDE) |0 G:(DE-Juel-1)ESDE |c ESDE |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Hawkes, James |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Smart, Simon |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Danovaro, Emanuele |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 4 |u fzj |
| 700 | 1 | _ | |a Quintino, Tiago |0 P:(DE-HGF)0 |b 5 |
| 773 | _ | _ | |a 10.1186/s40537-025-01306-3 |g Vol. 12, no. 1, p. 243 |0 PERI:(DE-600)2780218-8 |n 1 |p 243 |t Journal of Big Data |v 12 |y 2025 |x 2196-1115 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1052199/files/s40537-025-01306-3.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1052199 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)6952 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2025-01-06 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2025-01-06 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2025-01-06 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2025-01-06 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b J BIG DATA-GER : 2022 |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2025-08-21T14:03:27Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2025-08-21T14:03:27Z |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2025-08-21T14:03:27Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |d 2025-11-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2025-11-06 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b J BIG DATA-GER : 2022 |d 2025-11-06 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|