Service Discovery Method Research Articles

Web Service Discovery for Cloud-Based Mobile Application Using Multi-Level Clustering and QoS-Based Ranking

Since many service providers are providing similar web services, finding an accurate web service based on user preferences is becoming a challenging task. Therefore, enhancing web service discovery (WSD) method will improve the searching performance. In this paper, we firstly discuss and review some existing web service discovery approaches and identify their limitations. Subsequently, we propose a web service discovery method for cloud-based mobile application by using multi-level clustering technique to improve performance by reducing the searching scope. Our web service discovery architecture is able to increase the discoverability of more accurate web services based on user's preferences. Meanwhile, user preference Quality of Services (QoS) attributes are also used for ranking procedure to allow user to decide the quality of the mobile application. The experimental results show that our approach is able to increase the searching performance and provide a reliable list of selection for users.

A Novel Rubric and Feature-based Appraisaland Comparison Framework for the Evaluationof Semantic Web Services Composition Approaches

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.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

Agent-based manufacturing service discovery method for cloud manufacturing

The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.

Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S

According to the shortage of semantic model in the present service discovery, the method of service discovery about cloud manufacturing is proposed based on Ontology Web Language for Service (OWL-S), as well as service description. The semantic elements are introduced into service discovery problem, and the algorithm for semantic similarity measurement in ontology is improved. Then the service filtrate method from latent semantic analysis (LSA) is put forward. The basic attributes and quality attributes of Web services are also expressed in the tree-like structure. The effectiveness and feasibility of the proposed method is verified by an application case of cloud manufacturing.

Quantitative trust management with QoS-aware service selection

As the number of services available on the web increases, discovering which services are trustworthy becomes vital. Given the black-box nature of services, selecting a trustworthy service that best fits users' requirements is critical. Once a set of services fulfilling users' functional requirements has been found, which one a user chooses depends mostly on the Quality of Services QoS, particularly security, trust and reputation. This paper proposes a trust management model to support service discovery and selection based on QoS. We define a quantitative trust-evaluation method for dynamic service discovery and selection and show experimental results. The proposed model allows service consumers to get the most trustworthy services possible. Our mechanism uses consumers' feedback to describe how trustworthy they have found services and service providers to be. Choosing services using a quantitative measurement rather than their intuition will allow consumers to select a highly reliable service that meets their quality and technical requirements.

Web Service Discovery with Quality of Service (QoS) Parameters using Bayesian Belief Network

The aim of this study is to present a novel approach which uses the belief net to intelligently infer the best and most relevant web service based on the available Quality of service parameters. Service Oriented Architecture (SOA) is the discovery of relevant Web Services for a specified task is a one of the key challenges. As Web services proliferate, there is a possibility of existing more than one service with same functionalities consequently; most of service discovery method will return several services which meet user given input and output. Finding the mainly relevant and best service is very crucial for the service consumers. In this situation, the non-functional Quality of Service (QoS) parameters present in the service description provided by the service provider play a major responsibility.

AN IMPROVED LIGHT-WEIGHT MATCHMAKING MECHANISM FOR DISCOVERING OWL-S SERVICES BASED ON SPARQL, BIPARTITE AND NLP APPROACH

Semantic Web services integrate the meaningful content of the Semantic Web with the business logic of Web services and thus enable industries and individuals to access these services. But as the number of available Web services increase, there is a growing demand for a mechanism for effective retrieval of required services. We propose an improved Semantic Web service discovery method for finding OWL-S (Web Ontology Language for Services) services by combining functional similarity matching (using bipartite graph) and textual similarity matching. However, discovering relevant Semantic Web service is a heavyweight task. Performance of service discovery is significantly reduced when the number of services increases. To overcome this issue, a lightweight filtering stage is also introduced before the discovery mechanism. Filtering is performed by semantic-based SPARQL (Simple Protocol and RDF Query Language) query. It will significantly reduce the input for the discovery process. Thus the search space and the time required to find the relevant services will be reduced. The proposed techniques are applied to a sample test collection and experimental results are presented, which demonstrate the effectiveness of the idea. ( Keywords: discovery, filtering, OWL-S, Semantic Web service, SPARQL )

Multi-Strategy Web Service Discovery for Smart Government

Semantic Web Service technology is the solution to system integration and business collaboration for smart government which is cross-border and heterogeneous on a large scale. However the tremendous Web services search space caused by the wide range, large scale and complex e-government business systems is one of the great challenges for smart government. The paper focuses on researches about service discovery in e-government business integration for smart government. In accordance with the application environment and the current technical status of e-government, the author proposes a multi-strategy Web service discovery method on the basis of the proposed semantic model. The discovery process comprises three stages: keyword query with semantic enhancement, IO semantic matching and PE semantic matching. Finally similarity calculating method is proposed to evaluate the matching degree of each candidate service for service selection as well as the conclusions.

Principles of Dynamic Configuration of Flexible Supply Networks Based on Semantic Service Composition

In a complex business network finding a supplier can be a very time consuming task. In advanced supply networks like Build-to-Order supply chains, this task should be carried out under time constraints and under uncertainties both in suppliers and in the orders. The technology of semantic service oriented architectures is aimed to support such kind of tasks, enabling construction of self-organizing flexible supply networks. A novel approach to dynamic network members’ discovery and selection based on competence profiles included in provided service descriptions is proposed. The approach is grounded in a method for service discovery with incomplete information using query expansion techniques. The approach is illustrated by example from automotive industry.

Service Discovery Based on Self-Evolution of Domain Ontology and Cloud Migration of Web Service

To meet the demand of the higher precision ratio and recall ratio of service search, a method of Web service discovery is proposed based on ontology evolution and Web service migration. According to the developed cloud service platform and the existed service repository and domain ontology list, a service migration process is proposed which migrate to the cloud platform and implement unified management for Web service. This process effectively achieves that Web services migrate in and out of the cloud platform. In order to realize the self-evolution of domain ontology, an ontology alignment mechanism is designed to automatic extend and replace of ontology concept. This mechanism achieves the integration of different keywords with the same meaning and ensures the consistency of ontology concept. In this paper, the method not only enables efficient service discovery and dynamic service management, but also can achieve high concurrency support of services and supply or billing on demand. The experiments show that the proposed method solves the search problem of low efficiency based on keyword, and it effectively improves the precision ratio and recall ratio of service discovery.

A Service Discovery Algorithm Based on Network Topology Management

In view of the current development trend of Web service discovery technology and based on the service discovery method of formal concept analysis, the paper puts forward a service discovery algorithm based on QuickPeer, which exploits the idea of P2P network topology management to manage the formal concept of Web service in order to optimize the existing service discovery algorithm.

A Novel Approach: A Hybrid Semantic Matchmaker for Service Discovery in Service Oriented Architecture

Since service-oriented architectures make the commercial systems more reliable and reusable, they have gained more popularity in industry and scientific community in recent decades. Service-oriented architectures bring flexibility and reusability to software design. Due to the increasing number of services on the Web, finding a service which is suited to user requirements is crucial. The process of finding suitable services to user request is one of the main purposes of service-oriented architectures. Many methods have been proposed for service discovery in service-oriented architectures that try to fulfil user requirements and offer suitable services to user request; however the proposed methods do not have enough precision for discovering suitable services. In this paper, we propose a method for service discovery which offers more accurate services according to user request. The proposed method is a hybrid semantic matchmaker for service discovery in service oriented architecture. By providing accurate services suitable to user requests, we have greatly increased the reusability rate and reduced the time and cost of software development .

A Web Service Discovery and Composition Method Based on Service Classes

A Web Service Discovery and Composition Method Based on Service Classes

A QoS‐aware service discovery method for elastic cloud computing in an unstructured peer‐to‐peer network

SUMMARYTraditionally, service discovery is often promoted by the centralized approach that typically suffers from single point of failure, poor reliability, poor scalability, to name a few. In view of this challenge, a QoS‐aware service discovery method is investigated for elastic cloud computing in an unstructured peer‐to‐peer network in this paper. Concretely speaking, the method is deployed by two phases, that is, service registering phase and service discovery phase. More specifically, for a peer node engaged in the unstructured peer‐to‐peer network, it firstly registers its functional and nonfunctional information to its neighbors in a flooding way. With the multiple registered information, the QoS‐aware service discovery is promoted in a probabilistic flooding way according to the network traffic. At last, extensive simulations are conducted to evaluate the feasibility of our method. Copyright © 2013 John Wiley & Sons, Ltd.

Special issue: second international workshop on workflow management in service and cloud computing (WMSC2010)

Special issue: second international workshop on workflow management in service and cloud computing (WMSC2010)

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the “Things”. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.

An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises

With the development of virtual enterprise (VE) paradigm, the usage of serviceoriented architecture (SOA) is increasingly being considered for facilitating the integration and utilisation of distributed manufacturing resources. However, due to the heterogeneous nature among VEs, the dynamic nature of a VE and the autonomous nature of each VE member, the lack of both sophisticated coordination mechanism in the popular centralised infrastructure and semantic expressivity in the existing SOA standards make the current centralised, syntactic service discovery method undesirable. This motivates the proposed agent-based peer-to-peer (P2P) architecture for semantic discovery of manufacturing services across VEs. Multi-agent technology provides autonomous and flexible problemsolving capabilities in dynamic and adaptive VE environments. Peer-to-peer overlay provides highly scalable coupling across decentralised VEs, each of which exhibiting as a peer composed of multiple agents dealing with manufacturing services. The proposed architecture utilises a novel, efficient, two-stage search strategy – semantic peer discovery and semantic service discovery – to handle the complex searches of manufacturing services across VEs through fast peer filtering. The operation and experimental evaluation of the prototype system are presented to validate the implementation of the proposed approach.

A Flexible tool for Discovery and Selection of Sensor Web Registry Services with Extended SOA Framework

Sensor Web Services are recently emerged as promising technology and have potential usage in a wide range of application domain. However due to advancement of OGC SWE standard, the discovery and selection of Sensor Web Registry services throughout heterogeneous environment become a challenging task. Many approaches and frameworks have been proposed to discover the sensor web registry services and some of the approaches assume that the requests are placed in SOAP compatible formats while others focus on GUI based parametric query processing. In this paper, a flexible tool has been proposed that uses the Natural Language Query Processing which is a convenient and easy method of sensor data access in comparison to SQL or XML based Query Language like XQuery and XPath. An architecture based on extended SOA Framework that organizes the method of sensor web registry service discovery and selection in an efficient and structured manner has been suggested by adding some new layers like Request Parser & Query Generator (RPQ), Service Verifier and Certifier (SVC) and Service Rank Calculator (SRC). A typical weather sensor web service scenario is considered in this paper for finding the most relevant service according to requester‟s functional and QoS requirements.

An Efficient Method for QoS-aware Service Discovery and Composition

Recently, more and more enterprises are embracing SOA paradigm to integrate and implement interoperable, robust and platform-independent distributed applications. Therefore, service discovery and composition become two main tasks which have gained great momentum. In order to improve the efficiency of service discovery and composition, a method is proposed in this paper. Firstly, some concepts and operations are defined. Then a composition algorithm is introduced in detail after definition. In addition, a QoS-aware evaluation method based on maximizing deviation calculation is proposed to resolve the problem of service selection. An instance and some simulation experiments are illustrated at last. The result shows it is an efficient and effective method for service discovery and composition.