research-article

Web Service Compositions with Fuzzy Preferences: A Graded Dominance Relationship-Based Approach

Authors:

Karim Benouaret,

Djamal Benslimane,

Mahmoud Barhamgi,

Zakaria Maamar,

Quan Z. ShengAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 13, Issue 4

Article No.: 12, Pages 1 - 33

https://doi.org/10.1145/2576231

Published: 01 July 2014 Publication History

Abstract

Data-driven Web services build on service-oriented technologies to provide an interoperable method of interacting with data sources on top of the Web. Data Web services composition has emerged as a flexible solution to answer users’ complex queries on the fly. However, as the number of Web services on the Web grows quickly, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. User preferences are a key factor that can be used to rank candidate services/compositions and retain only the best ones. In this article, we present a novel approach for computing the top-k data service compositions based on user preferences. In our approach, we model user preferences using fuzzy sets and incorporate them into the composition query. We use an efficient RDF query rewriting algorithm to determine the relevant services that may be used to answer the composition query. We match the (fuzzy) constraints of the relevant services to those of the query and determine their matching degrees using a set of matching methods. We then rank-order the candidate services based on a fuzzification of Pareto dominance and compute the top-k data service compositions. In addition, we introduce a new method for increasing the diversity of returned top-k compositions while maintaining as much as possible the compositions with the highest scores. Finally, we describe the architecture of our system and present a thorough experimental study of our proposed techniques and algorithms. The experimental study demonstrates the efficiency and the effectiveness of our techniques in different settings.

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Cited By

Taktak HBoukadi KZouari FGhedira Guégan CMrissa MGargouri F(2024)A knowledge-driven service composition framework for wildfire predictionCluster Computing10.1007/s10586-023-03997-w27:1(977-996)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-023-03997-w
Benouaret KSacharidis DBenslimane DHadjali A(2021)Selecting Services for Multiple Users: Let’s Be DemocraticIEEE Transactions on Services Computing10.1109/TSC.2018.287569114:5(1531-1544)Online publication date: 1-Sep-2021
https://doi.org/10.1109/TSC.2018.2875691
Hair AOuchitachen H(2021)Selection of Composite Web Services Based on QoSBusiness Intelligence10.1007/978-3-030-76508-8_12(143-159)Online publication date: 16-May-2021
https://doi.org/10.1007/978-3-030-76508-8_12
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Index Terms

Web Service Compositions with Fuzzy Preferences: A Graded Dominance Relationship-Based Approach
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 13, Issue 4

July 2014

89 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/2656491

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2014 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 July 2014

Accepted: 01 December 2013

Revised: 01 September 2013

Received: 01 October 2011

Published in TOIT Volume 13, Issue 4

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Cited By

Taktak HBoukadi KZouari FGhedira Guégan CMrissa MGargouri F(2024)A knowledge-driven service composition framework for wildfire predictionCluster Computing10.1007/s10586-023-03997-w27:1(977-996)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-023-03997-w
Benouaret KSacharidis DBenslimane DHadjali A(2021)Selecting Services for Multiple Users: Let’s Be DemocraticIEEE Transactions on Services Computing10.1109/TSC.2018.287569114:5(1531-1544)Online publication date: 1-Sep-2021
https://doi.org/10.1109/TSC.2018.2875691
Hair AOuchitachen H(2021)Selection of Composite Web Services Based on QoSBusiness Intelligence10.1007/978-3-030-76508-8_12(143-159)Online publication date: 16-May-2021
https://doi.org/10.1007/978-3-030-76508-8_12
Yang RWang D(2020)Hierarchical Aggregation for Reputation Feedback of Services NetworksMathematical Problems in Engineering10.1155/2020/37483832020(1-12)Online publication date: 7-May-2020
https://doi.org/10.1155/2020/3748383
Sadeghiram SMa HChen G(2020)A User-Preference Driven Lexicographic Approach for Multi-Objective Distributed Web Service Composition2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308222(791-797)Online publication date: 1-Dec-2020
https://doi.org/10.1109/SSCI47803.2020.9308222
Zhang YWu LHe QChen FDeng SYang Y(2019)Diversified Quality Centric Service Recommendation2019 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2019.00031(126-133)Online publication date: Jul-2019
https://doi.org/10.1109/ICWS.2019.00031
Hasan MJaafar JWatada JHassan MAziz I(2019)An interval type-2 fuzzy model of compliance monitoring for quality of web serviceAnnals of Operations Research10.1007/s10479-019-03328-6Online publication date: 3-Aug-2019
https://doi.org/10.1007/s10479-019-03328-6
Vakili ANavimipour N(2017)Comprehensive and systematic review of the service composition mechanisms in the cloud environmentsJournal of Network and Computer Applications10.1016/j.jnca.2017.01.00581:C(24-36)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.jnca.2017.01.005
Mohammed MAmine CFethallah H(2016)Leveraging fuzzy dominance relationship and machine learning for hybrid web service discoveryInternational Journal of Web Engineering and Technology10.1504/IJWET.2016.07733611:2(107-132)Online publication date: 1-Jan-2016
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Ko IKo HMolina AKwon J(2016)SoIoTACM Transactions on Internet Technology10.1145/283549216:2(1-21)Online publication date: 15-Apr-2016
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