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An interval type-2 fuzzy model of compliance monitoring for quality of web service

  • Mohd Hilmi HasanEmail author
  • Jafreezal Jaafar
  • Junzo Watada
  • Mohd Fadzil Hassan
  • Izzatdin Abdul Aziz
S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018
  • 85 Downloads

Abstract

Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.

Keywords

Web services monitoring Quality of web service QoWS monitoring Interval type-2 fuzzy Uncertainties 

Notes

Acknowledgements

This research is an ongoing research supported by Fundamental Research Grant Scheme (FRGS/1/2018/ICT02/UTP/02/1); a Grant funded by the Ministry of Education, Malaysia.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre for Research in Data Science, Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASPerakMalaysia

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