IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

QoS-Based Collaborative Filtering for Web Service Mining

QoS-Based Collaborative Filtering for Web Service Mining
View Sample PDF
Author(s): Ilhem Feddaoui (ISG, University of Tunis, Tunisia), Faîçal Felhi (ISG, University of Tunis, Tunisia), Fahad Algarni (University of Bisha, Saudi Arabia)and Jalel Akaichi (University of Bisha, Saudi Arabia)
Copyright: 2021
Volume: 13
Issue: 1
Pages: 22
Source title: International Journal of Web Portals (IJWP)
DOI: 10.4018/IJWP.2021010103

Purchase

View QoS-Based Collaborative Filtering for Web Service Mining on the publisher's website for pricing and purchasing information.

Abstract

Stored information in the databases is heterogeneous, from various sources, and of large volumes. The web service selection becomes nontrivial, as the users are easily overloaded by vast amount candidates. Using the keyword-based search method, users are struggling to choose the best web services among those having similar features. In the traditional methods, the users set different constraints and QoS parameters of a web service from what's claimed by the provider. Moreover, different researches challenge this problem, introducing semantic discovery process to enable relevant and desired search results. These approaches don't give importance to users' opinions and the selection history. The classical development of the ontology is typically entirely based on high human participation. In this paper, the authors use ontology-based querying, user profile to know the history, new collaborative filtering to calculate user, and query similarity and QoS as the final step for web service selection. The approach combines the syntactic and semantic methods to increase the selection precision.

Related Content

Lata Jaywant Sankpal, Suhas H. Patil. © 2022. 23 pages.
Amna Alsalem, Emad Ahmed Abu-Shanab. © 2022. 20 pages.
Bimal aklesh Kumar. © 2022. 12 pages.
Nikola Vlahovic, Andrija Brljak, Mirjana Pejic-Bach. © 2021. 19 pages.
Ahmed Aloui, Okba Kazar. © 2021. 20 pages.
Ilhem Feddaoui, Faîçal Felhi, Fahad Algarni, Jalel Akaichi. © 2021. 22 pages.
Fernando Almeida, José Augusto Monteiro. © 2021. 12 pages.
Body Bottom