Functional service discovery –capability discovery

The OASIS research on service discovery [5] is based on technologies of pattern matching and searching techniques that have been applied in the field of Very Large Databases (VLDB). The proposed process searches for the required object in a huge amount of data by the use of classified catalogues. For example, it compares the requested name, address, type of service or region information to the data stored in the registry, and returns the found bindingTemplate to the requestor if there is any match. The bindingTemplate indicates the URL of the found service. Using the URL, clients can download the WSDL description and then starts to interact with the service as shown in Figure 2-15. However, UDDI provides a keyword search of Web services but not of capability [46]. It is hard to find a specific airline booking service through this approach because the service is advertized by its function.

Figure 2-15 Seeking flows for a specific service via UDDI Registry

Functional service discovery means searching by functionalities which are provided by Service Providers. It is not only a simple pattern matching but also semantic searching.

For example, ServiceRequestorsmay referto ‘airlinebooking services’to book aflight.The discovery service should return booking services for airlines and not include other booking services, for example, for football or concert tickets. It is known as capability discovery.

The work by a team at the Carnegie Mellon University (CMU), M. Paolucci et al.

[15],[35],[46],[50], integrates the OWL-S matching engine into UDDI which enables capability search in a semantic context. ItcomparestheServiceRequestors’requirements with ServiceProviders’capabilitiesbased on semantic descriptions. The approach proposed by this dissertation will also leverage their work to enable service discovery using semantic descriptions. This study proposes a consensus-based service discovery approach which attempts to use the underlying data and information about services as a search criterion (quality rating). With this feature, Service Requestors can discovery services by using linguistic terms (vague queries) such as Cheap or Comfortable during their search for a flight booking service for example.

In [54], the authors argue that search by the information in the ServiceProfile is not enough to find a service properly. The limitations arise due to the logical relationships among the inputs and outputs of a process. Assume that a simple process produces two outputs, o1 and o2. If a request requires both of these outputs, it will result in a positive match when the search is simply based on the ServiceProfile. The authors of [54] develop algorithms which do not search using the ServiceProfile but use the ServiceModel to examine the detailed execution process of a Web service. They declared that analyzing the logical nature within the process would increase accuracy. However, the authors of [11] criticize this idea and claim that the ServiceModel is not primarily provided to express properties to be used for finding matches. Moreover, the use of underlying data referring to services as a search criterion still has not been addressed in [54].

Another important project in the field of service discovery is the Language for Advertisement and Request for Knowledge Sharing (LARKS) [16]. It emphasizes that matching should be based on other elements, NOT ONLY on keyword retrieval. The semantics of requests and advertisements should be taken into consideration. The discovery

process of LARKS contains both syntactic and semantic concerns. Table 2-5 shows the frame structure of a LARKS specification used for service discovery.

Table 2-5 The frame structure of a LARKS specification [16]

Context Types Input Output InConstraints OutConstraints ConcDescriptions TextDescription

Context of specification

Declaration of used variable types Declaration of input variables Declaration of output variables Constraints on input variables Constraints on output variables

Ontological descriptions of used words Textual description of specification

An overview for matchmaking using LARKS is shown in Figure 2-16. The LARKS approach offers the option to use application domain knowledge in any advertisement or request using a local ontology. Briefly, LARKS provides discovery based on capability or functionality. This point of view is important to the researches thereafter. As mentioned before, however, the approach proposed by this dissertation is not only interested in the semantic issues but also the vague queries based on the quality rating of the underlying data about Web services.

Figure 2-16 An overview for matchmaking using LARKS [16]

In [11] and in earlier work [48], an algorithm to rank the corresponding Web services according to OWL-S descriptions during the discovery process is proposed. In this algorithm, all possible Web services will be analyzed in four stages: (1) the matching of inputs, (2) the matching of outputs, (3) the matching of service category, and (4) the matching of user-defined criteria. Each Web service will be rated during these four stages and the results will be aggregated to become the final assessment as shown in Figure 2-17. According to the assessments, a rank for Web services can be made for further selection. The ranking idea has contributed to service discovery and is somewhat similar to what this dissertation proposes. The concept of quality rating is also adopted by this dissertation. In addition to the concept of quality rating, this dissertation also details a rating procedure, describing how to evaluate a service using consensus based opinion, which has not been fully explored in [11],[48].

Figure 2-17 The rating procedures and ranking result for algorithm [11],[48]

Other work in functional discovery, such as [55], contribute their efforts in designing the detailed discovery algorithm for functional context reasoning within semantic environments.

However, all studies mentioned in this section are based on functional or capability search, but they have not paid sufficient attention to the use of underlying data and

information on services as a search criterion. This study proposed a consensus-based service discovery approach which attempts to use the underlying data on services as a search criterion (quality rating) to refine the search space and to increase the precision rate of discovery.