TY - JOUR
T1 - Streaming of Global Navigation Satellite System Data from the Global System of Navigation
AU - Barbosa-Santillán, Liliana Ibeth
AU - Sánchez-Escobar, Juan Jaime
AU - Barbosa-Santillán, Luis Francisco
AU - Meneses-Viveros, Amilcar
AU - Gao, Zhan
AU - Roa-Gil, Julio César
AU - León-Paredes, Gabriel A.
N1 - Publisher Copyright:
© 2021. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - The Big Data phenomenon has driven a revolution in data and has provided competitive advantages in business and science domains through data analysis. By Big Data, we mean the large volumes of information generated at high speeds from various information sources, including social networks, sensors for multiple devices, and satellites. One of the main problems in real applications is the extraction of accurate information from large volumes of unstructured data in the streaming process. Here, we extract information from data obtained from the GLONASS satellite navigation system. The knowledge acquired in the discovery of geolocation of an object has been essential to the satellite systems. However, many of these findings have suffered changes as error vocalizations and many data. The Global Navigation Satellite System (GNSS) combines several existing navigation and geospatial positioning systems, including the Global Positioning System, GLONASS, and Galileo. We focus on GLONASS because it has a constellation with 31 satellites. Our research’s difficulties are: (a) to handle the amount of data that GLONASS produces efficiently and (b) to accelerate data pipeline with parallelization and dynamic access to data because these have only structured one part. This work’s main contribution is the Streaming of GNSS Data from the GLONASS Satellite Navigation System for GNSS data processing and dynamic management of meta-data. We achieve a three-fold improvement in performance when the program is running with 8 and 10 threads.
AB - The Big Data phenomenon has driven a revolution in data and has provided competitive advantages in business and science domains through data analysis. By Big Data, we mean the large volumes of information generated at high speeds from various information sources, including social networks, sensors for multiple devices, and satellites. One of the main problems in real applications is the extraction of accurate information from large volumes of unstructured data in the streaming process. Here, we extract information from data obtained from the GLONASS satellite navigation system. The knowledge acquired in the discovery of geolocation of an object has been essential to the satellite systems. However, many of these findings have suffered changes as error vocalizations and many data. The Global Navigation Satellite System (GNSS) combines several existing navigation and geospatial positioning systems, including the Global Positioning System, GLONASS, and Galileo. We focus on GLONASS because it has a constellation with 31 satellites. Our research’s difficulties are: (a) to handle the amount of data that GLONASS produces efficiently and (b) to accelerate data pipeline with parallelization and dynamic access to data because these have only structured one part. This work’s main contribution is the Streaming of GNSS Data from the GLONASS Satellite Navigation System for GNSS data processing and dynamic management of meta-data. We achieve a three-fold improvement in performance when the program is running with 8 and 10 threads.
KW - extraction
KW - GLONASS
KW - metadata
KW - observation files
KW - satellites data
KW - streaming
UR - http://www.scopus.com/inward/record.url?scp=85102066049&partnerID=8YFLogxK
U2 - 10.14569/IJACSA.2021.0120297
DO - 10.14569/IJACSA.2021.0120297
M3 - Article
AN - SCOPUS:85102066049
SN - 2158-107X
VL - 12
SP - 773
EP - 783
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 2
ER -