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Automatic location of services
- In Proceedings of the 2nd European Semantic Web Conference (ESWC 2005
, 2005
"... Abstract. The automatic location of services that fulfill a given need is seen as a key step towards dynamic and scalable integration. In this paper we present a model for the automatic location of services that considers the static and dynamic aspects of service descriptions and identifies what not ..."
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Cited by 36 (9 self)
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Abstract. The automatic location of services that fulfill a given need is seen as a key step towards dynamic and scalable integration. In this paper we present a model for the automatic location of services that considers the static and dynamic aspects of service descriptions and identifies what notions of match and techniques are useful for the matching of both. Our model presents three important features: ease of use for the requester, efficient pre-filtering of relevant services, and accurate contracting of services that fulfill a given requester goal. We further elaborate previous work and results on Web service discovery by analyzing what steps and what kind of descriptions are necessary for an efficient and usable automatic service location. Furthermore, we analyze the intuitive and formal notions of match that are of interest for locating services that fulfill a given goal. Although having a formal underpinning, the proposed model does not impose any restrictions on how to implement it for specific applications, but proposes some useful formalisms for providing such implementation. 1
NORA/HAMMR: Making Deduction-Based Software Component Retrieval Practical
, 1997
"... Deduction-based software component retrieval uses preand postconditions as indexes and search keys and an automated theorem prover (ATP) to check whether a component matches. This idea is very simple but the vast number of arising proof tasks makes a practical implementation very hard. We thus pass ..."
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Cited by 36 (4 self)
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Deduction-based software component retrieval uses preand postconditions as indexes and search keys and an automated theorem prover (ATP) to check whether a component matches. This idea is very simple but the vast number of arising proof tasks makes a practical implementation very hard. We thus pass the components through a chain of filters of increasing deductive power. In this chain, rejection filters based on signature matching and model checking techniques are used to rule out non-matches as early as possible and to prevent the subsequent ATP from "drowning." Hence, intermediate results of reasonable precision are available at (almost) any time of the retrieval process. The final ATP step then works as a confirmation filter to lift the precision of the answer set. We implemented a chain which runs fully automatically and uses MACE for model checking and the automated prover SETHEO as confirmation filter. We evaluated the system over a medium-sized collection of components. The resul...

