An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS

• Main Authors: Junqiang Zhang, Lirui Xu, Yi Zhang, Gang Liu, Lingjun Zhao, Yuzhu Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-09-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: data processing; geological big data; service model
• Online Access: https://www.mdpi.com/2220-9964/8/9/392

With the rapid development of information processing technology and continuously evolving technological hardware and software, the scale of spatial data has grown exponentially. This has necessitated that stricter requirements be placed on the way data is processed. The traditional single-machine centralized data processing method is gradually being replaced by the network-based cloud geographic information system (GIS) mode. However, the information processing method of the business system in the existing spatial information network environment is difficult to expand, which limits the application of the business system. This paper proposes an on-demand and extended model for the GIS data processing procedure that considers the three-way separation of algorithm development, business processes, and the operational interface, and can implement an on-demand expansion of business processes by process modeling the business and task scheduling the workflow engine. This can expand the scope of business systems and improve the efficiency of business system construction.