Geospatial Service Chain Research Articles

LYRIC: Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud

With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namely <i>LYRIC</i> (dead <u>L</u> ine and budget aware spatio-temporal quer <u>Y</u> p <u>R</u> ocessing <u>I</u> n <u>C</u> loud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11 percent, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches.

Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases.

Cloud- and Agent-Based Geospatial Service Chain: A Case Study of Submerged Crops Analysis During Flooding of the Yangtze River Basin

More intelligent construction of geospatial service chains and more efficient execution of such service chains remain major challenges in distributed geospatial analysis. This study addresses these challenges using a Cloud- and agent-based approach for automatic and intelligent construction of a geospatial service chain in the Cloud environment. A spatial agent infrastructure comprising fundamental services and an agent interface is designed, implemented, and deployed. Our approach involves a strategy for selecting and aggregating appropriate agents and Web-processing services (WPS) by evaluating their availability. This strategy ensures successful construction of a geospatial service chain in the Cloud environment, even when there is a lack of requisite geospatial services in the system. Moreover, the method can significantly increase the speed of a service chain in distributed environments and retains high stability when more requests are submitted over various network conditions. This is because the computing mobility and intelligence of the agent help to avoid transfer of large volumes of spatial data and keep the load balanced during construction and execution of the service chain. A prototype system for analysis of submerged crops during flooding of the Yangtze River basin demonstrates the advantages of our approach over existing methods.

A hybrid matching method for geospatial services in a composition-oriented environment

With the development of Internet and GIS, large volumes of spatial data, powerful computing resources and many spatial data processing functions are published in the form of Web services. Finding suitable geospatial services in the composition-oriented environment is a crucial task. The semantic Web provides a kind of technology to find and compose various service resources automatically through the Web. This paper proposes a hybrid method for the semantic matching of geospatial services. The method includes two parts. Part 1 puts forward a multi-level semantic matching approach, which matches single geospatial service at four levels: classification, input/output, precondition/effect and the quality of service (QoS). This multi-level matching approach makes single service matching quicker and more accurate. Part 2 puts forward a matching algorithm for a geospatial service chain based on the context. The algorithm adopts a trace algorithm, taking account of the effect of the context. It restricts the input/output parameters of the current service by the input/output parameters of service chain, pre-service and sub-service. It matches the atomic service dynamically in a composition-oriented environment, and accurately converts the abstract model of geospatial services into an executable geospatial service chain. A case study of the flood analysis for the Poyang Lake illustrates the effectiveness of our context-based matching method for geospatial services.