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.
|Number of pages||11|
|Journal||International Journal of Advanced Computer Science and Applications|
|State||Published - 2021|
Bibliographical noteFunding Information:
The data used to support the findings of this study are available from the corresponding author upon request. This work was supported by the Sciences Research Council (CONACyT) through the research project number 262756 http://navigationgnssproject.net/index.html.
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- observation files
- satellites data