ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume I-4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 175–179, 2012
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 175–179, 2012

  20 Jul 2012

20 Jul 2012


L. You2,1, Z. Gui3,1, W. Guo1, S. Shen1, and H. Wu1 L. You et al.
  • 1LIESMARS, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China
  • 2School of Mathematics and Computer Science, Hubei University, 11 Xueyuan Road, Wuhan, 430062, China
  • 3Center of Intelligent Spatial Computing for Water/Energy Science, 4400 University Dr., Fairfax, 22030, VA, USA

Keywords: Geospatial Web Services Composition, Web Service Agents, Real-Time Monitoring

Abstract. Geospatial web services composition becomes one of the main solutions for complex computing in the GIS realm with the development of information interoperability and advanced IT technologies. Standard geospatial web services only have two simple statuses: success or failure. However the procedures for geospatial information processing and analysis always feature intensive data, complex computation, and long processing times. Thus, standard geospatial web services composition only provides basic functions and cannot fully satisfy geospatial information processing and analysis needs. The problems with standard geospatial web services are numerous, including difficulties in controlling and monitoring geospatial web services, composition optimization, and error tracking. The execution status of geospatial web services composition must be monitored becoming the basis for further optimization of the geospatial web services composition model. This paper proposes a framework for geospatial web service composition that supports real-time status monitoring. This framework effectively integrates geospatial web services with status interfaces and dynamic monitoring technology. Experiments show that this framework can effectively monitor execution information, reminding users of bottleneck information thus providing the foundation for further improvements in the model's execution efficiency.