Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Towards personalised and adaptive QoS assessments via context awareness

Tools
- Tools
+ Tools

Barakat, L., Taylor, Phillip M., Griffiths, Nathan, Taweel, A., Lucas, M. and Miles, S. (2018) Towards personalised and adaptive QoS assessments via context awareness. Computational Intelligence, 34 (2). pp. 468-494. doi:10.1111/coin.12129

[img]
Preview
PDF
WRAP-towards-personalised-adaptive-QoS-assessments-Griffiths-2017.pdf - Accepted Version - Requires a PDF viewer.

Download (2216Kb) | Preview
Official URL: http://doi.org/10.1111/coin.12129

Request Changes to record.

Abstract

QoS (quality of service) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Quality of service (Computer networks) , Computational intelligence, Context-aware computing
Journal or Publication Title: Computational Intelligence
Publisher: Wiley-Blackwell Publishing, Inc.
ISSN: 0824-7935
Official Date: May 2018
Dates:
DateEvent
May 2018Published
1 August 2017Available
12 April 2017Accepted
Date of first compliant deposit: 18 April 2017
Volume: 34
Number: 2
Page Range: pp. 468-494
DOI: 10.1111/coin.12129
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Related URLs:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us