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Toward personalized and adaptive QoS assessments via context awareness
Author(s) -
Barakat Lina,
Taylor Phillip,
Griffiths Nathan,
Taweel Adel,
Luck Michael,
Miles Simon
Publication year - 2018
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12129
Subject(s) - computer science , quality of service , context (archaeology) , process (computing) , quality (philosophy) , service (business) , service provider , domain (mathematical analysis) , machine learning , human–computer interaction , artificial intelligence , computer network , paleontology , philosophy , economy , epistemology , economics , biology , operating system , mathematical analysis , mathematics
Quality of Service (QoS) 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, using 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 used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, 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.