HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cubes preserving uncertainties

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F21%3A00548649" target="_blank" >RIV/67985815:_____/21:00548649 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/21:00357098

Výsledek na webu

<a href="https://doi.org/10.1016/j.ascom.2021.100463" target="_blank" >https://doi.org/10.1016/j.ascom.2021.100463</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ascom.2021.100463" target="_blank" >10.1016/j.ascom.2021.100463</a>

Jazyk výsledku

angličtina

Název v původním jazyce

HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cubes preserving uncertainties

Popis výsledku v původním jazyce

In this study, we developed a new software infrastructure called Hierarchical Semi-Sparse Cube (HiSS-Cube) based on Hierarchical Data Format version 5. HiSS-Cube enables visualization and machine learning using combined heterogeneous data and it was designed to be scalable for big data. HiSS-Cube allows data from multiple domains (imaging, spectral, and timeseries data) to be combined and the construction of a multi-resolution semi-sparse data cube that preserves the uncertainties of scientific measurement at all resolutions. The functionality of HiSSCube was verified based on a subset of the Sloan Digital Sky Survey Stripe 82 survey. We compared the times and volumes for visualizations and machine learning data exported to HiSS-Cube and the original format (FITS). Using these data, we demonstrated that HiSS-Cube is faster by several orders of magnitude. HiSS-Cube supports export to the VOTable format and it is compatible with common Virtual Observatory tools. The source code for our prototype HiSS-Cube is available from GitHub and the data are available from Zenodo.

Název v anglickém jazyce

HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cubes preserving uncertainties

Popis výsledku anglicky

In this study, we developed a new software infrastructure called Hierarchical Semi-Sparse Cube (HiSS-Cube) based on Hierarchical Data Format version 5. HiSS-Cube enables visualization and machine learning using combined heterogeneous data and it was designed to be scalable for big data. HiSS-Cube allows data from multiple domains (imaging, spectral, and timeseries data) to be combined and the construction of a multi-resolution semi-sparse data cube that preserves the uncertainties of scientific measurement at all resolutions. The functionality of HiSSCube was verified based on a subset of the Sloan Digital Sky Survey Stripe 82 survey. We compared the times and volumes for visualizations and machine learning data exported to HiSS-Cube and the original format (FITS). Using these data, we demonstrated that HiSS-Cube is faster by several orders of magnitude. HiSS-Cube supports export to the VOTable format and it is compatible with common Virtual Observatory tools. The source code for our prototype HiSS-Cube is available from GitHub and the data are available from Zenodo.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Astronomy and Computing

ISSN

2213-1337

e-ISSN

2213-1345

Svazek periodika

36

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

100463

Kód UT WoS článku

000691531100017

EID výsledku v databázi Scopus

2-s2.0-85106464689