Scalable preconditionaware service composition with SPARQL Functional service composition builds a plan that fulfills some userprovided goal using available services. The literature describes several approaches for description of services and goals, with different levels of expressiveness, ranging from the ones based only on inputs and outputs to the more expressive logicsbased solutions. The former provide better scalability and performance, while the latter allow for increased expressivity in preconditions and effects of both services and goals. The approach proposed in this paper aims to achieve a balance between expressivity and performance of functional service composition. To this end, a graphbased composition algorithm is extended to support preconditions and effects described with a small subset of SPARQL. Experiments compare the performance of this extended algorithm with two stateoftheart algorithms that support preconditions and effects and demonstrate better scalability. In one case, a maximum speedup of 29 times was achieved for the problems that could be expressed with the SPARQL subset. In another, problems that a stateoftheart algorithm could not solve after 5 minutes where solved in seconds.

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.