A Hypergraph Coloring Based Modelling of Service Oriented System Service Oriented Architecture (SOA) provides a high level structure and related facets for developing service oriented system (SOS). In recent days, various SOA modelling approaches, such as set theoretic and graph, have been proposed. These approaches hinder the scalability in the design of large scale SOS. In such system, the computational complexity is increasing due to the multiple numbers of service consumers, providers, and associated groups. In this context, this paper proposes a hypergraph coloring approach to represent such a system in more effective way. Two algorithms, service discovery and publishing of services are proposed for such system. Further, a case study is illustrated for the practical implementation of the proposed approach. The novelty of the proposed research work is to map the idea of large scale SOA along with its different modelling elements into a graphical concept, so that it will be more useful for a large scale SOS.

Diversified Keyword Search based Web Service Composition Abstract   To assist system engineers in efficiently building servicebased software systems, the keyword search based service composition approach on service connection graphs (scgraphs) has been proposed recently. However, due to the ambiguity of keywords, a keyword query may represent a bunch of different user requirements. Thus the current approach that only returns the composition with the optimal Quality of Service (QoS) cannot guarantee to hit the spot. In this paper, in order to satisfy the various possible requirements underlying a given keyword query, we formally introduce the topk diverse service composition problem, and present a novel diversified keyword search approach on scgraphs to address it. Specifically, we firstly propose an AllThenDiversify (ATD) algorithm that enumerates all potential compositions by searching a scgraph and then derives the topk diverse subsets by deriving the maximal independent sets of a similarity graph. Then, due to the possibly large number of potential compositions, we present a PopAndDiversify (PAD) algorithm that only maintains a similarity graph of the top compositions that have been found so far during the search and computes its maximal independent sets incrementally until convergence, thereby reducing unnecessary computation overheads. Moreover, we propose two composition similarity measurements w.r.t. the categories or descriptions of services respectively. Lastly, the experimental results on ProgrammableWeb.com demonstrate that, our approach outperforms another stateoftheart composition diversification approach on both metrics of density and redundancy, and meanwhile, improves the efficiency of diversification significantly.