A heuristic-based runtime ranking for service composition

The automatic composition of Web Services has been explored in the literature from different standpoints. It aims to create an execution plan for the flow of Web Services based on requests made and sent by the client, by following the stages necessary for the generation of composite services and then carrying out the execution of the workflow that has been designed. However, no research studies have been found that undertake the whole process of an automatic composition and execution, from the user's request to the execution of the services chosen as a solution. Therefore, the goal of this paper is to evaluate the performance of an automatic Web Service composition, since the request made by the client, to the delivery of the results of the executed composition. This article examines the integration between two tools, the automatic web service composition system and the extensible platform to evaluate semantic web services with the aim of conducting a performance evaluation of an entire process of automatic composition of Web services.

Background/Objectives: This article proposes a novel methodology based on rubrics and feature-based schemes for the appraisal and comparison of approaches to semantic Web services composition and discovery. Methods/Statistical analysis: In order to evaluate the Semantic Web services composition and discovery approaches we created a new framework called RFSWS. This framework is the combination of traditional feature-based evaluation schemes and newly developed analytic rubrics tables. Five recently introduced prominent Semantic Web services composition approaches were identified, explained, and then evaluated/compared using RFSWS. Findings: In this work we determined aspects of Semantic Web services composition and discovery processes that can be evaluated as performance criteria in rubric tables. This is a novel application of rubrics, which have traditionally been used for grading student performance by teachers. We created a novel framework called RFSWS consisting of rubric tables and a feature-based evaluation scheme for the evaluation and comparison of Semantic Web service discovery and composition approaches, and applied it in the evaluation of five recently introduced prominent Semantic Web services composition approaches. Considering the shortcomings of existing Semantic Web services composition approaches that were discovered through this evaluation, we proposed an idealized dynamic Semantic Web service discovery and composition method, a yardstick by which all future Semantic Web services composition approaches can be evaluated. Application/ Improvements: Our novel RFSWS framework can be applied in the comprehensive and systematic evaluation/comparison of Semantic Web services discovery and composition approaches.

Through providing an execution environment for semantic web services, SWSF (Semantic Web Service Framework) can facilitate automatic web service delivery, discovery, selection, invoking, composition and interoperation. However, each of those ongoing approaches does not satisfy all basic requirements of SWSF, e.g. OWL-S (Ontology Language of Web Service) does not offer the mechanism for automatic web service tasks' execution and WSMO can not describe the composition of web services in a structural way yet. So, in order to provide a fully- fledged SWSF, we present the ISCF (Integrated Service Composition Framework) which is based on the OAA (Open Agent Architecture ). We describe the technical characters and design objectives of ISCF from global and detailed view. Comparing with other SWSFs, ISCF has more extensible and autonomous distributed architecture, and has more complete and effective web service automatization. In addition, ISCF provide some useful tools to help users design semantic web services and manage ISCF system.

Automatic web service composition is defined as the const ruction of virtual enterprises as complex services on demand. In the current research scenario choosing business service partners for composition on the fly is usually determined with user-centric metrics and deficient of provider’s business specific metrics. Automatic web service composition in today’s real world business circumstances is more of a toy model due to the lack of service provider collaboration metrics [1]. SLAKY System is a new model for selection of business service partners by adding servic e provider metrics to user centric metrics enabling strategic and realistic selection of services for composition. SLAKY System selects services automatically considering the vision, time planning, environmental context, user adoption, usage policies, trus t management, risk management, market scenario, native intelligence, and competitive profit management as service provider collaboration metrics in addition to functionality satisfaction metrics for client’s requirements. The time planning metric was designed using opus deviser algorithm [2] and profit management using SLAKY BWG algorithm [3]. In this paper we focus on strategic selection of semantic services based on the metric of native intelligence. Howard Gardner, psychology professor, Harvard Universit y, defines intelligence as an ability to solve problems, or to create products, that are valued within one or more cultural settings. Richard Heck, Professor of Natural Theology, Brown University, in his work states that native intelligence is primarily influenced by racial and cultural differences. User preferences in any sector intend to have native influence. For example the affinity towards entertainment services for Indian Premier League or low cost car booking service for TATA Nano is more in India th an America. Moreover decision made by a decision maker is motivated by native intelligence in addition to Intelligence Quotient and is valued highly in native grounds. For example the selection of a matrimonial service totally relies on native aspects. Native Intelligence is also a significant metric to improve time -to-market and quality in the launch of any product. Considering the significance of native intelligence in strategic selection we layer native intelligence descriptions for realistic selection o f semantic web services on the fly. OWL -S upper ontology that describes semantics of services for automation of service discovery, selection, composition, orchestration and invocation lacks entities for semantic modeling of native intelligence. In this pap er we propose an extended OWL -S ontology which augments where, why and who native status descriptions of a service apart from the existing what (ServiceProfile sub -ontology) and how (ServiceModel, ServiceGrounding sub-ontology) semantics. The why, whereand who semantics are newly augmented as ServiceParameters of Profile sub -ontology using sParameter property. We also propose Naavi algorithm that reasons with common sense knowledge for selection of the web service that is highly valuable in a native context using native description in extended OWL -S.

This paper presents a new approach for automatic Web service composition based on the formalism of Fluent Calculus using semantic Web service descriptions. In our approach, the Web service composition process is viewed as an AI planning problem in the Fluent Calculus formalism. To semantically describe Web services, we have used a Web service domain ontology which is then translated into a Fluent Calculus knowledge base, necessary for the composition planning phase. For verifying the composed services, the Label Transition System Analyzer (LTSA) formalism is used. The paper also presents an experimental prototype for the Fluent Calculus based Web service composition and demonstrates its effectiveness with the help of an application scenario from the social event planning domain.

The potential benefits of Web services composition heavily rely on semantic interoperability, i.e., the ability to exchange data meaningfully amongst Web services. Context heterogeneity, which refers to different implicit assumptions about interpreting the exchanged data, hampers the automatic composition of Web services. However, existing initiatives of Semantic Web Services (SWSs) often ignore context heterogeneity. In this paper, we introduce an approach to address this issue. The contexts of the involved Web services are defined in a lightweight ontology and their WSDL descriptions are annotated by an extension of a W3C standard, i.e., Semantic Annotation for WSDL and XML Schema (SAWSDL). The composition of Web services is described using BPEL specification. Given a BPEL file that ignores context heterogeneity, the approach automatically detects all context differences among the involved services, and reconciles them by producing a mediated BPEL file that incorporates necessary conversions using XPath functions and/or Web services.

With the proliferation of Web services and the evolution towards the Semantic Web comes the opportunity to automate various Web services tasks. A number of approaches have been proposed to tackle the problem of automatic web service composition. Most of them are inspired by the researches in AI planning. As heuristics plays a key role in the promotion of planning efficiency, the selection of heuristic mechanism is very important. In this paper, we propose the conception of composition distance, and propose a heuristic bidirectional search algorithm based on composition distance. The most contribution of this paper is : (1)propose the conception of composition distance; (2)a heuristic bidirectional search algorithm. The algorithm we proposed is effective and efficiency when applied in web service composition engine.

Automatic web services composition approaches aim at automatically composing services to satisfy users' request according to the information of users' input. As the number of web services is increasing, the performance of automatic service composition is faced with challenges. In this paper, we propose an approach based on backward chaining method to compose web services with high performance and good Qos, called Clustering Based Backward Chaining Method (CBBCM). We use k-means method to cluster services as the preliminary work. Services are clustered according to the semantic similarities of their output parameters. Skyline is used as an optimization to set services with good Qos on the top of each cluster. Then the adapted backward chaining method is applied to compose services. We evaluate our approach and the backward chaining method through experiment with different datasets. It is proved that our algorithm has a high performance and gets the composition results with better Qos.

The automatic composition of Web services requires the existing services to be encoded in a semantic form, such as ontology. If Web services are represented with ontology, we can automatic compose Web services by means of their semantics. Ontology modeling is divided into two steps: input/output parameters modeling and Web services modeling. First, we collect Web services' input/output parameters, represent them with classes in ontology, and transform Web services to properties in ontology, define relations between classes and properties. Then we judge Web services' relations based on relations between properties transformed from these Web services and estimate whether these Web services can be composed.

The automatic composition of semantic Web services is one of challenging areas of semantic Web services research. Mathematical semantic Web services are very useful in practice, however only a small number of related research results are reported. In this paper, we will show a dynamic, automated composition method for mathematical semantic Web services, using intelligent agents. The paper is focused on solving a kind of mathematical puzzle, called the countdown problem. In addition, we will show a static, manual composition method for Web services.

In a domain SOA environment, multiple services exist to help in executing the users requests but manually handling each request is time wasting and very tiring thus the idea of automatic composition of web services emerged. This paper describes the ACUAI framework which is an AI automatic composer for web services with learning capabilities, since games have the most developed dynamic system adaptation to user action this framework uses two AI techniques which are used in games development they are decision trees and the finite state machines, the user request will be handled in the same way games react to user input then a decision is taken based on current state of the orchestration and then the algorithm changes the state of the orchestration according to the last decision taken to be able to guide the orchestration towards the goal and get a successful composition then each successful composition and its template request are saved to be used later on in enhancing the execution time.

With the growth of web service in quantity, it has become an urgent issue how to compose web service with effective method so as to capacitate web service providers to work cooperatively and exert greater function. The paper introduces web service composition method based on rules and meta-services. Meta services are the abstraction of web services with similar functions, while rules are used to describe logic relation and data dependence relation among meta-services. An algorithm that generates the abstract service composition process based on the rules and meta-services is designed. The abstract service composition process is converted into executable process by employing web service discovery. The method proposed in this paper is proved to be feasible by the application in travel platform.

Web services play an important role in the development of distributed systems. Particularly, the possibility of composing already implemented web services in order to provide new functionalities is an interesting approach for building distributed applications and business processes. In this paper we focus on mismatches occurring during dynamic composition of web services. We propose an algorithm for dynamic and automatic composition and adaptation of web services. Our approach only uses the information that is already available in service interface descriptions. The algorithm allows programmers to define dynamic web service compositions without changing the source code.

Web services facilitate reusability that allows cost-effective development of business applications using web services composition. Due to the proliferation of web services, different service providers are providing similar functionality web services. But these web services can have different values for QoS attributes and transactional properties. Thus, it is difficult to build a transactional and QoS optimal composite web service. Most of the existing works used the scalarization-based method for selecting optimal composite web service. In the scalarization-based method, the service user should have a priori knowledge of its preferences about the nonfunctional requirements of desired solutions. This paper proposes a Multiobjective Discrete Grey Wolf Optimization (MDGWO)-based approach for Transactional and QoS-driven Web Services Composition. The Pareto dominance concept is used to select optimal composite web service. Generational Distance (GD), Inverse Generational Distance (IGD), and Spread measures are used to evaluate the performance of the proposed approach. Experimental results indicate that the proposed approach performs well.

This paper presents a novel approach for semantic web service composition based on the formalism of fluent calculus. We show how the planning capabilities of the fluent calculus can be used to automatically generate an abstract composition model. For describing web service capabilities we have used an OWL-S ontology. Based on the OWL-S ontology semantics, we encode the web service description in the fluent calculus formalism and provide a planning strategy for service composition. For testing our composition method, we have implemented an experimental framework that automatically composes and executes web services.