An Approach to Computation of Similarity, Inter-Cluster Distance and Selection of Threshold for Service Discovery Using Clusters

Abstract

Meeting out similarity demands of clients, selection of threshold and computation of inter-cluster distance (ICD) are difficult while clustering. Hierarchical agglomerative clustering based approach is proposed for service discovery including two similarity models viz., Output Similarity Model (OSM) and Total Similarity Model (TSM) with additional levels for Degree of Match (DoM). The OSM which computes similarity between services using solely the outputs of services is proposed while clustering services to eliminate irrelevancy completely. The TSM which computes similarity between services using both inputs and outputs of services is proposed while discovering matched services of a given query. The work justifies the 'complete linkage' as suitable method for computing ICD. It selects threshold-ICD in terms of DoM without altering the similarity demands of clients and ensures rightness of clusters. The computation time of discovery using clusters is found to be faster (7.32 against 170.59 seconds) than that of sequential method.Index Terms-similarity models, clustering of services, service discovery using clusters, optimization of discovery, semantic service discovery.

bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

“…Rupasingha et al [28] propose a CF-based recommendation approach for ontology generation. Surianarayanan et al [33] propose a hierarchical agglomerative clustering-based approach for service discovery. Rodriguez-Mier et al [27] propose a composition framework that enables the generation of a graph-based composition.…”

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“…Similarly, considering the intercluster distance, hierarchical agglomerative clustering was performed by Surianarayanan et al, depending on both the inputs and the outputs of services with prioritization. The similarities were then identified as different degrees of match for meeting disparate queries from clients [24]. Yisong et al [25] utilized nonfunctional descriptions to group services and provided the service-to-service matches in terms of semantic similarity.…”