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Top1. Introduction
Web Service (WS) has been considered as a major software paradigm with the abilities of self-description, self-management, self-adaptation and self-organization (Pastrana, Pimentel & Katrib, 2011). WSs are advertised, located, distributed and applied across the Internet through service oriented architecture (SOA). They enable to monitor and adapt themselves when the unexpected changes are encountered in their working environments.
Since very recent years, a large body of works has been done in the area of service management such as service discovery, selection and composition. Some applied category or syntactic approaches (Keller & Ludwig, 2002; Martin-Diaz et al, 2003; Rajendran & Balasubramanie, 2010; Lin, Sheu, Chang & Yuan, 2011; Li, Song & Zheng, 2014) while some utilized semantic approaches (Shu, Rana, Avis & Dingfang, 2007; Liu, Shen, Hao & Yan, 2009; Brandic et al., 2008; Ke & Haung, 2012; Zhao, Sun & Jin, 2015; Wang, Cao & Xiang, 2015) to partially address the SOA vision. Even though their mechanisms led to the semantic technology, they could not exploit well all semantic aspects of WSs. The reason is because they wholly depended on heuristic algorithms such as genetic algorithm (Su, Zhang & Chen, 2007), analytical hierarchy process (Tran, Tsuji & Masuda, 2009), artificial neural network (Cai, Hu, Lu & Cao, 2009), rough sets theory (Yahyaoui, Alumulla & Own, 2014) and collaborative filtering (Lin et al., 2014) based on some intuitive rules or guidelines to generate their expected solutions.
With the Web’s growth, there have been many different QoS languages (Tosic, Pagurek & Patel, 2003; Keller & Ludwig, 2002) and models (Choi, Her & Kim, 2007; Toma, Foxvog & Jaeger, 2006) to express the meanings of QoS information offered by service providers and requested by users. However, remaining lack of semantic exploitation and self-adaptabilities in discovering the services creates the following two problems.
Firstly, the current approaches (Kritikos & Plexousakis, 2006; Cai, Hu, Lu & Cao, 2009; Tran, Tsuji & Masuda, 2009; Lin, Sheu, Chang & Yuan, 2011; Redl, Breskovic, Brandic & Dustdar, 2012; Yahyaoui, Alumulla & Own, 2014) consider only QoS constraint as a key factor of QoS metrics matchmaking. The sole consideration of QoS constraint without taking into account the other aspects of QoS metrics makes them inefficient to understand and reason the subtle QoS specification. In addition, the traditional exact match is no longer useful on today’s sophisticated QoS descriptions because two syntactically different services might be similar by means of semantic (Redl, Breskovic, Brandic & Dustdar, 2012). Hence, it becomes insufficient to match the services without practically usage of semantic technology.
Secondly, the previous QoS-based service discovery approaches did not explore self-adaptable feature in discovery while some functional discovery ones (Ke & Haung, 2012; Wang, Cao & Xiang, 2015) presented adaptive methods. As lack of adaptive feature in QoS-based approaches, they remain difficult to solve the problem of non-feasible solution occurrences when unexpected QoS specifications are encountered. Besides, when incomplete or inconsistent user requests are obtained, they could not adjust the searching conditions to relax some rigid user QoS demands or to replace irrelevant QoS attributes that are unconsciously chosen by non-expert users with relevant ones. For example, the study (Redl, Breskovic, Brandic & Dustdar, 2012) faced no feasible solution occurrences under incomplete conditions like user’s insufficient QoS demands, user’s over-constrained QoS needs or unavailable status of suitable service offers which can exactly satisfy user QoS requests.