Performance improvement techniques for geospatial web services in a cyberinfrastructure environment - A case study with a disaster management portal

High population growth, urbanization, and global climate change drive up the frequency of disasters, affecting the safety of people's lives and property worldwide. Because of the inherent big-data nature of this disaster-related information, the processes of data exchange and transfer among physically distributed locations are increasingly challenging. This paper presents our proposed efficient network transmission model for interoperating heterogeneous geospatial data in a cyberinfrastructure environment. This transmission model supports multiple data encoding methods, such as GML (Geography Markup Language) and GeoJSON, as well as data compression/decompression techniques, including LZMA and DEFLATE. Our goal is to tackle fundamental performance issues that impact efficient retrieval of remote data. Systematic experiments were conducted to demonstrate the superiority of the proposed transmission model over the traditional OGC Web Feature Service (WFS) transmission model. The experiments also identified the optimized configuration for data encoding and compression techniques in different network environments. To represent a real-world user request scenario, the Amazon EC2 cloud platform was utilized to deploy multiple client nodes for the experiments. A web portal was developed to integrate the real-time geospatial web services reporting with real-time earthquake related information for spatial policy analysis and collaborative decision-making.