Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

Astsatryan, Hrachya; Lalayan, Arthur; Kocharyan, Aram; Hagimont, Daniel

doi:10.12694/scpe.v22i4.1945

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…Building upon these findings, our future endeavors will focus on extending the available comprehensive scalable platform with new air pollution services [12]. This platform will integrate data from various sources, including archives of remote-sensing images and in-situ measurements.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Air Pollution of NO2 over Armenia and Belarus

Abrahamyan,

Grigoryan,

Astsatryan

et al. 2023

Proceedings of Computer Science and Information Technologies 2023 Conference

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Over time, the population and anthropogenic activities increase, adversely affecting our atmosphere. In particular, an increase in air temperature leads to a change in the number of various gases that fill our atmosphere, the growth and decrease of which can significantly affect our environment. That is why air quality evaluation and prediction have become an important research area. Earth Observations (EO) provide robust monitoring to address environmental challenges using satellite and in-situ data. The study aims to evaluate nitrogen dioxide (NO 2 ) air pollutants for both territories Armenia and Belarus using the SENTINEL 5P Tropospheric Monitoring Instrument (TROPOMI) remote sensing platform and groundbased measurements during Sep 2018 -Aug 2019 considering the synaptic conditions and data availability. The SENTINEL 5P datasets of NO 2 during 2018-2019 have been used for the studies obtained from the Google Earth Engine (GEE). The studies show that NO 2 excesses in their troposphere for the regions of Armenia and Belarus. This increase is correlated with the rise in urban population, causing dramatic adverse effects on the atmosphere. Compared to traditional methods, the study may substantially increase the government's and policymakers' capability to take timely action on anthropogenic activities in the mentioned cities to mitigate the emission of NO 2 .

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Section: Discussionmentioning

confidence: 99%

Evaluation of Air Pollution of NO2 over Armenia and Belarus

Abrahamyan,

Grigoryan,

Astsatryan

et al. 2023

Proceedings of Computer Science and Information Technologies 2023 Conference

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“…In pursuit of pinpointing the most suitable data compression method tailored to specific use cases, the findings of an insightful study (Astsatryan et al, 2021) recommend a comprehensive assessment methodology. This assessment encompasses the estimation of data processing execution times, a meticulous consideration of an array of data compression techniques, and the diverse spectrum of distributed computing clusters characterized by varying node counts and resource allocations.…”

Section: Storage Optimizationmentioning

confidence: 99%

Optimized Multi-Modular Services: Empowering Earth Observation Data Processing

Lalayan,

Astsatryan,

Poghosyan

et al. 2024

Informatica

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The significance of earth observation data spans diverse fields and domains, driving the need for efficient management. Nevertheless, the exponential increase in data volume brings new challenges that complicate processing and storing data. This article proposes an optimized multi-modular service for earth observation data management in response to these challenges. The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data. By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs.

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“…The usage of Big Data frameworks, such as Apache Hadoop or Spark, is also quite promising and widely implemented, as the frameworks can provide efficient and distributed environments for big data processing [17]. The paper [18] shows the effectiveness of an adaptive Spark-based remote sensing data on-demand processing method on the cloud, with the data storage Hadoop Distributed File System (HDFS), which is more efficient in means of execution time than Hadoop.…”

Section: Motivationmentioning

confidence: 99%

Scalable Data Processing Platform for Earth Observation Data Repositories

Astsatryan

Lalayan

Giuliani

2023

SCPE

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Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

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Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

Cited by 6 publications

References 20 publications

Evaluation of Air Pollution of NO2 over Armenia and Belarus

Evaluation of Air Pollution of NO2 over Armenia and Belarus

Optimized Multi-Modular Services: Empowering Earth Observation Data Processing

Scalable Data Processing Platform for Earth Observation Data Repositories

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