Hierarchical Semi-Sparse Cubes-Parallel Framework for Storing Multi-Modal Big Data in HDF5

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F23%3A00581668" target="_blank" >RIV/67985815:_____/23:00581668 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11104/0349775" target="_blank" >https://hdl.handle.net/11104/0349775</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3323897" target="_blank" >10.1109/ACCESS.2023.3323897</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Semi-Sparse Cubes-Parallel Framework for Storing Multi-Modal Big Data in HDF5

Popis výsledku v původním jazyce

In this article, we present a revised version of the Hierarchical Semi-Sparse Cube (HiSS-Cube) framework. It aims to provide highly parallel processing of combined multi-modal multi-dimensional big data. The main contributions of this study are as follows: 1) Highly parallel construction of a database built on top of the HDF5 framework. This database supports parallel queries 2) design of a database index on top of HDF5 that can be easily constructed in parallel 3) support of efficient multi-modal big data combinations. We tested the scalability and efficiency on big astronomical spectroscopic and photometric data obtained from the Sloan Digital Sky Survey. The performance of HiSS-Cube is bounded by the I/O bandwidth and I/O operations per second of the underlying parallel file system, and it scales linearly with the number of I/O nodes.

Název v anglickém jazyce

Hierarchical Semi-Sparse Cubes-Parallel Framework for Storing Multi-Modal Big Data in HDF5

Popis výsledku anglicky

In this article, we present a revised version of the Hierarchical Semi-Sparse Cube (HiSS-Cube) framework. It aims to provide highly parallel processing of combined multi-modal multi-dimensional big data. The main contributions of this study are as follows: 1) Highly parallel construction of a database built on top of the HDF5 framework. This database supports parallel queries 2) design of a database index on top of HDF5 that can be easily constructed in parallel 3) support of efficient multi-modal big data combinations. We tested the scalability and efficiency on big astronomical spectroscopic and photometric data obtained from the Sloan Digital Sky Survey. The performance of HiSS-Cube is bounded by the I/O bandwidth and I/O operations per second of the underlying parallel file system, and it scales linearly with the number of I/O nodes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

119876-119897

Kód UT WoS článku

001100997000001

EID výsledku v databázi Scopus

2-s2.0-85174830822