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In general, a SaaS application consists of a set of atomic or composed services, their underlying business processes and business rules to support the services operations and data sources (Mandal et al., 2013 and Papazoglou et al., 2008). A cloud can host numerous services of different types. Thus, the supporting business processes and data sources are also varied to a great extent. Again, a service can be created by combining multiple existing services with different business logic and data sources (Figure 1). Thus, in a large and distributed environment like cloud, locating the compatible service is a crucial task. To address this issue numerous researchers proposed various service discovery, service selection and service composition mechanism. However, almost all of these mechanisms require accessing the meta-information of the services and their underlying business logics to determine the compatibility of the constituent services in the composition. Moreover, in this service-oriented environment, if a service is updated or changed then it may requires changing the other services, which are composed of it. Thus, efficient management of meta-information is taking on much more important role in the service ecosystem (Xue et al., 2015; Kong et al., 2011). This demands an efficient categorization and cataloguing of meta-information for SaaS (Bin et al., 2010) to facilitate the discovery, selection, composition and change management by enabling tracing. This helps in determining how services, business processes or data sources are used across different applications. It also provides the capability to authenticate whether the properties and metaphors accurately reflecting the business goals (Smit et al., 2012). Without it, the data intensive cloud applications cannot be utilized fully the value of automation due to the lack of essential characteristics like, transparency and auditability intrinsic to a non-metadata driven approach (Kong et al., 2011).
Figure 1.
A generic SaaS architecture