Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Oft...

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Bibliographic Details
Main Authors: Alexander Feoktistov, Sergey Gorsky, Roman Kostromin, Roman Fedorov, Igor Bychkov
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/11/1/8
Description
Summary:Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.
ISSN:2220-9964