Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

Abstract

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.

Personalized Web Services Composition Using Artificial Intelligence

“…In this context, the discovery and planning of personalized composition remain difficult problems to solve. In this regard, several approaches have been proposed [7][8] and consider that Artificial Intelligence (AI) can optimize the composition of services. These works deal with the composition problem without taking into account the personalization aspect.…”

Simplifying Land Cover-Geoprocessing-Model Migration with a PAMC-LC Containerization Strategy in the Open Web Environment

“…Furthermore, additional computations are needed to adapt data to different model requirements, such as the spatial scope, resolution, and for mat of the data [24]. Data interaction between models should come first in the collabora tion and "dialogue" processes of models, then should come automated or semi-automatic deployment of algorithms or models, and finally reliable transmission and storage capa bilities should be guaranteed [25].…”

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

“…Third, the set of local Pareto-optimal services is extracted from each set of relevant services; finally, the Pareto-optimal compositions are computed using a progressive search. In [27], an automated planning algorithm called Graphplan was proposed to address the composition of land cover services. The key idea of the proposed framework consists of creating an ontology for describing the tasks, the input/output data, and the atomic services, then a planning graph is created using the forward search of the planning algorithm.…”

“…-H 4 (the majority interval heuristic) was inspired by [9]. In this ranking, we compute the median interval of each service and perform pairwise comparisons of the services using Equation (27). The services having the highest number of wins are retained in Top-K elements.…”