Flexible semantic based service matchmaking and discovery. Flexible Semantic

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A Semantic

flexible semantic based service matchmaking and discovery

SeeVa is a storage system that includes a Datalog engine to enable language-independent reasoning capabilities. For a property used in one service advertisement, it may not be used by another service advertisement within the same service category. Another aspect that leads to the improvement of service discovery mechanisms is the use of contextual information. Minimizing false positives and false negatives is achieved with three selection stages in combination with the well-defined ontology. The Job Submission service is responsible for the actual Job management operations such as submission, cancellation and monitoring.


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DaaS: Cloud

flexible semantic based service matchmaking and discovery

This trade-off needs to be considered carefully for every application process. Each decisive property used for identifying advertised services has a maximum match degree when matching all the properties used in a service request. Depending on the parameters, the user makes the choice with the user selection module and regarding these parameters the service request is matched with the services provided in the registry database. Let ƒ ƒ ƒ Ω be a domain ontology. It is envisioned that computing resources in a future Grid environment will be exposed as services. The selection stages restrict the false positives and the ontology restricts the false negatives together with the customized input parameters. However, the crucial drawbacks of the deductive approach are both the difficulty to model service requests and service descriptions using formal logic and, the high computational complexity due to the proof process and consequently the considerable slowness of the search process.

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flexible semantic matchmaking engine

flexible semantic based service matchmaking and discovery

This provides a short description of the agent, a sample query, input and output parameter declarations, and other constraints. In the following section, we discuss how to use the similarity degrees to produce a set of advertised services that may maximally satisfy a user request. The method proposed has been validated by performing a set of experiments for evaluating the recall and precision of the method. These irrelevant properties used by advertised services should be removed before the service matchmaking process is carried out. As can be seen from Figure 1, service discovery plays a crucial role in service-oriented Grid systems.

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Flexible Semantic

flexible semantic based service matchmaking and discovery

As can be seen from Table 1, the property P1 used in service S1 may not exist in S2. Figure 5: Overhead evaluation in accessing service records. For the simplicity of expression, input and output properties used in a service request are generally referred to as service request properties. This is a significant addition to existing work, where discovery of services needs to be encoded in a precise way, making it difficult to find services which have an approximate match to a query. Information is provided to the service requester by sending a contact address and related capability descriptions of the relevant service provider. The challenges facing Web service discovery are further magnified by the stringent constraints of mobile devices and the inherit complexity of wireless heterogeneous networks.

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Service Matching and Discovery in P2P Semantic Community

flexible semantic based service matchmaking and discovery

Klein and Bernstein also provide an analysis of the recall and precision of these four service discovery approaches and they resolve that keyword-based methods offer the lowest precision and recall rates, while the deductive approach offers the best precision and recall rates. Section 2 gives an introduction to the requirements of the matchmaking mechanism in general. An additional problem related with performing flexible matches is that the matchmaking engine is open to exploitation from advertisements and requests that are too generic in the attempt to maximize the likelihood of matchmaking. If they allow little flexibility, they reduce the likelihood of finding services that match their requirements, which means, they minimize the false positives, while increasing the false negatives. Algorithm 2 uses the advertised services with the maximum number of nonempty property values as targets to find indecisive properties.

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Service Matching and Discovery in P2P Semantic Community

flexible semantic based service matchmaking and discovery

Following the work proposed by Paolucci et al. It characterizes the service for advertisement, discovery and matchmaking. Advertised services are organised as service records in a database. This facilitates system interoperability through the use of a common methodology and a corresponding representation. Such non-functional properties may also be encoded as part of a service description. This ontology identifies the job submission process of Grid applications.

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Service Matchmaking with Rough Sets

flexible semantic based service matchmaking and discovery

With a defined set of rules, the following inference engine reasons about the value parameters parsed from the ontology. The use of service properties need to be related to a particular applicationspecific ontology. Defining the ontology and the selection stages precisely allows the matchmaking process to be efficient. In the mean time, the following service discovery process can be speeded up because of the reduction of properties in matching advertised services. This approach retrieves items whose property values match the query values. The matchmaking engine can reduce the efficiency of these exploitations by ranking advertisements based on the degree of a match supplied with the request.


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DaaS: Cloud

flexible semantic based service matchmaking and discovery

The targeted services can still be uniquely identified without using these indecisive properties. Using semantic information for the matchmaking process achieves better results than syntactic type matchmaking. The approach adopted here can be applied to other domains — where a specific ontology can be specified. All possible combinations of individual indecisive properties will be checked with an aim to remove the maximum indecisive properties which may include uncertain properties whose values are empty. Extraction of semantic metadata includes identification of relevant entities and also relationships within the context of relevant ontologies. Whenever the ontology changes, the reasoner provides the matching parameters for the user selection process accordingly. Each Pizza service had two input properties i.

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