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

Abstract

Available online xxxxKeyword: Geospatial cyberinfrastructure (GCI) Rapid response Disaster management GeoJSON GML WMS WFS Service-oriented architecture (SOA) a b s t r a c t 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.

Lsiviewer 2.0 – A Client-Oriented Online Visualization Tool For Geospatial Vector Data

“…This result clearly indicates that, though the size of Table 4. System specifications of the instances used for testing LZMA is lesser than DEFLATE, the time taken to compress the data on the server is more in LZMA method, which complements the conclusions derived by (Li et al, 2015). Based on these observations that the data processing time is equally crucial to the data compression ratios achieved, the application implements the DEFLATE method instead of LZMA.…”

“…GeoJSON is better at encoding point data while GML is good at encoding polyline and polygon data (Li et al, 2015). Although GML has advantage over GeoJSON in polygon and polyline encoding, we had to choose GeoJSON deliberately for the reasons described in (Li et al, 2015). This observation is shown in performance comparison section.…”

“…The other popular data interchange format is GML, a XML-like format introduced by OGC which provides encoding for points, polylines, polygons and other complex spatial data structures. GeoJSON is better at encoding point data while GML is good at encoding polyline and polygon data (Li et al, 2015). Although GML has advantage over GeoJSON in polygon and polyline encoding, we had to choose GeoJSON deliberately for the reasons described in (Li et al, 2015).…”

Discovering Land Cover Web Map Services from the Deep Web with JavaScript Invocation Rules

“…The GetMap operation enables users to obtain static maps from multiple servers by submitting parameters that include layer names, bounding boxes, format, styles, etc. [3,16]. The GetFeatureInfo operation requests metadata for the selected features [16].…”

“…A web map service (WMS) is an international standard protocol for publishing and accessing geo-referenced maps on the web [1][2][3]. This standard has facilitated the integration, access and value-added applications of geospatial information [1,4,5].…”