Self-Adaptive Goal-Driven Web Service Composition Based on Context and QoS

Khanfir, Emna; Djmeaa, Raoudha Ben; Amous, Ikram

doi:10.1109/icebe.2017.39

Cited by 4 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Service recommendation is a part of it. There are many common web service recommendation algorithms, such as service recommendation based on QoS [17,27,12], service recommendation based on collaborative filtering [40,9], and service recommendation based on semantic similarity. In this paper, We use a service recommendation method based on semantic similarity, and dynamic programming algorithm and variable length genetic algorithm are used for combination service.…”

Section: Impact Of Operation Numbermentioning

confidence: 99%

Web services recommendation leveraging semantic similarity computing

Hu¹,

Cheng²,

Zhou³

2018

Mathematical Foundations of Computing

View full text Add to dashboard Cite

With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users' requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.

show abstract

Section: Impact Of Operation Numbermentioning

confidence: 99%

Web services recommendation leveraging semantic similarity computing

Hu¹,

Cheng²,

Zhou³

2018

Mathematical Foundations of Computing

View full text Add to dashboard Cite

show abstract

Q-Graphplan: QoS-Aware Automatic Service Composition With the Extended Planning Graph

et al. 2020

View full text Add to dashboard Cite

With the progress of web technologies, web services with abundant functionalities, such as video transmission, location, navigation, etc., are becoming more and more pervasive. Automatic web service composition aims to automatically combine selected elementary web services from a finite service set by matching the input and output parameters given an initial state and a goal state. Considering the end-to-end Quality-of-Service(QoS) of each web service, the service composition problem becomes an optimization problem to find the optimal solution. This paper maps this problem to an automatic planning problem and proposes Q-Graphplan based on the classical graphplan, an efficient planner for solving classical planning problems. First, we construct a planning graph based on the dependency relationships of the web services and extract essential heuristics according to the reachability analysis. Second, we convert this planning graph to a directed path generation graph. Finally, we extract the optimal solution from the path generation graph using a backward A* algorithm with the heuristics of the planning graph. Furthermore, our approach avoids redundancies when constructing the planning graph and improves the searching effectiveness in extracting solution. We conduct experiments on the WSC-2009 dataset to compare performance against present approaches, and the results show the efficiency and effectiveness of our proposed approach. INDEX TERMS Service composition, AI planning, QoS-aware, heuristic search. I. INTRODUCTION

show abstract

Goal Modelling Meets Service Choreography: A Graph Transformation Approach

Bachras

Kontogiannis

2020

2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC)

View full text Add to dashboard Cite

Self-Adaptive Goal-Driven Web Service Composition Based on Context and QoS

Cited by 4 publications

References 12 publications

Web services recommendation leveraging semantic similarity computing

Web services recommendation leveraging semantic similarity computing

Q-Graphplan: QoS-Aware Automatic Service Composition With the Extended Planning Graph

Goal Modelling Meets Service Choreography: A Graph Transformation Approach

Contact Info

Product

Resources

About