Service Selection Model Research Articles

User-preferences based service selection model in large-scale services with various QoS properties

With the rapid growth of service scale, there are many services with the same functional properties but different non-functional properties on the Internet. There have been some global optimizing service selection algorithms for service selection. However, most of those approaches cannot fully reflect users’ preferences or are not fully suitable for large-scale services selection. In this paper, an ant colony optimization (ACO) algorithm for the model of global optimizing service selection with various quality of srevice (QoS) properties is employed, and a user-preference based large-scale service selection algorithm is proposed. This algorithm aims at optimizing user-preferred QoS properties and selecting services that meet all user-defined QoS thresholds. Experiment results prove that this algorithm is very efficient in this regard.

A Method for Context-aware Web Services Selection

Service-oriented computing (SOC) is emerging as a new, promising computing paradigm that centers on the notion of service as the fundamental element for developing software applications. Traditional service selection deals with applications which only consider functional matchmaking of services and do not put them into a context when selecting services. When recognizing the importance of contextual information, in this paper we mainly focus on the precise and effective context-aware service selection. At first we proposed a General Context Model (GCM) for service selection. After the construction of GCM, we gave a framework for context-aware service selection and discussed the method of service selection based on context and function. The experimental result shows that our method performs better than the methods without consideration of context.

Reputation-aware Service Selection based on QoS Similarity

For the up-and-coming computing models like as cloud computing, service is the standard package for meeting all kinds of consumers' requirements. Web Services are the concrete implement of the service. When users request and consume Web Services, services' reputations will play a vital role in users' selection. A gradually adjusting reputation evaluation method of Web Services is proposed based on eliminating the collusive behaviors of consumers step by step, and a reputation-aware model for service selection is designed. In order to adjust reputations, QoS similarity is computed firstly according to the differences between advertised QoS from service providers and delivered QoS from service consumers' evaluation, next, current reputation is attained; then the consumers are sorted based on reputation using clustering algorithm and the potential collusive consumers are mined using association rules algorithm; finally, the updated reputation is recalculated and saved in the reputation center included in the model. The experimental results show that the model can identify the malicious consumers and improve the exact rate of reputation evaluation and success rate of service selection.

An evidence-based scheme for web service selection

Existing studies on the web service selection problem focus mainly on the functional QoS properties of the service rather than the consumer satisfaction and trust aspects. While a good QoS enhances the reputation of a service, different consumers invariably hold differing views of the service contents. Some service reputation approaches primarily consider the consumer's prior experience of the service via opinion feedback system, may neglect the effect of social trust transition in the recommendations of others. As a result, the problem of reaching consensus on the level of consumer trust regarding service becomes one of key issues in service selection. This study proposes a trust-based service selection model to estimate the degree of consumer trust in a particular service based on the consumers' direct experience and indirect recommendation of the service. In the proposed approach, the degree of consumer trust is correctly estimated by extending Dempster---Shafer evidence reasoning theory to the reputation computation using consumers' direct experience and incorporating Josang's belief model for solving the trust transition problem in the indirect recommendation of the service. The proposed model effectively enables deception detection by means of existing bodies of evidence, and therefore excludes the fraudulent evidence of malicious evaluators from the selection process. In addition, a quality index is proposed to help third party (TTP) examine the body of evidence and make the outranking result more reliable. Importantly, the quality index is based not only on the confidence degree of the evidence, but also on the support degree, and therefore discovers the effects of intentional negative assessments. The validity of the proposed approach is demonstrated numerically by means of two service selection examples.

A Service Selection Model Using Mixed Intelligent Optimization

Based on the thought of separating non-functional properties from functional properties of Web Service,a composition Web Service selection model based on non-functional properties is put forward,in which executing sequence of the composition service has been given.On account of both incommensurability and confliction among non-functional properties,a new mixed intelligent optimization algorithm making use of both the advantage of Maximum Entropy Method and Social Cognitive Optimization is introduced to achieve the optimal result for the service selection model.Finally,numerical experiments are given,which demonstrate that non-functional requirements can be comprehensive considered for achieving the global automatic service selection.

A Multi-QoS Based Local Optimal Model of Service Selection

Global optimal and local optimal are two strategies of service selection.The current global optimal algorithms of service selection provide single optimal solution instead of multi-acceptable solutions under end-to-end constraints,which cannot fully reflect users' preference and service personality,and is not conducive to encourage service provider to optimize the service quality.In this paper,an ordinary utility function is used as a numerical scale of ordering local services,and meanwhile a Multi-QoS based Local Optimal Model of Service Selection(MLOMSS)is proposed firstly to provide important grounds to choose the best service.Then,subjective weight mode,objective weight mode,and subject-objective weight mode are constructed to determine the weight of each QoS property,which not only shows users' preference and the objectivity of service quality but also helps to generate service composition chains.At last,experimental results indicate the flexibility and effectiveness of this model based on SEWSIP(Semantic Enable Web Service Integration Platform).

A Neuro-Fuzzy Model for QoS Based Selection of Web Service

The automatic selection and composition of Web services rely strongly on the manner to deal with ambiguity inherent to the description of functionalities of these services and the client’s requests. Quality of Service (QoS) criteria become crucial in Web services selection and the problem of checking that a web service satisfies a given level of QOS is considered in recent research works. This paper presents a QoS based automatic classification method of web services. These services give generally similar functionalities and are offered by different providers. The main feature of our Web service selection model is to take advantage of the neuro-fuzzy logic for coping with the imprecision of QoS constraints values.

POYRAZ: CONTEXT‐AWARE SERVICE SELECTION UNDER DECEPTION

The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer‐oriented and context‐dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating‐based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.

Service-Correlation Aware Service Selection for Composite Service

In service oriented application, value-added service is composed of some component services which are selected from the candidate services. However, in current service selection model, the QoS of a candidate service is independent from other candidate services. Therefore, current service selection approaches can′t deal with the situation in which the QoS of a candidate service has a correlation with other candidate services. This paper presents a systematic approach to construct the service-correlation aware QoS description to characterize the QoS dependence of an individual service on the other related services. Based on this model, it also proposes service correlation aware selection approaches for above situation, including integer programming based selection approach for optimal solution and heuristic based selection approach for sub-optimal solution. A case study is conducted and demonstrates the applicability of the proposed approach. Experiment results indicate that, based on the service correlation aware QoS description model, the QoS of composite service through integer programming based selection approach reach an obviously high level, and a sub-optimal QoS of composite service can be gained efficiently through the heuristic based selection approach.

A Multi-dimension Qos based Local Service Selection Model for Service Composition

There are two service selection strategies in dynamic service composition, i.e., global strategy and local strategy. The existed global service selection algorithms, due to insufficiently show users’ partiality and feature of the service, is unfavorable to encourage the service provider to optimize the service quality to some extent. In this paper, an ordinary utility function is used as a numerical scale of ordering local services, and then a multi-dimension Qos based local service selection model is proposed to provide important grounds to choose superior service and sift inferior service. Secondly, subjective weight mode, objective weight mode, and subject-objective weight mode are constructed to not only determine the weight coefficient of each Qos criterion, but also show users’ partiality and the objectivity of service quality. At last, this model is proved to be flexibility and effective based on our SEWSCP (Semantic Enable Web Service Composition Platform).

On optimal decision for QoS-aware composite service selection

The increasing popularity of employing web services for distributed systems contributes to the significance of service discovery. However, duplicated and similar functional features existing among services require service consumers to include additional aspects to evaluate the services. Generally, the service consumers would have different view on the quality of service (QoS) of service attributes. How to select the best composite service in theory among available service (WS) candidates for consumers is an interesting practical issue. This work proposes a QoS-aware service selection model based on fuzzy linear programming (FLP) technologies, in order to identify their dissimilarity on service alternatives, assist service consumers in selecting most suitable services with consideration of their expectations and preferences. This approach can obtain the optimal solution of consensual weight of QoS attribute and fuzzy positive ideal solution (FPIS) by extending LINMAP method, developed by Srinivasan and Shocker. Finally, two numerical examples are given to demonstrate the process of QoS-aware web service selection. The experimental results demonstrated that it is a feasible and supplementary manner in selecting the of web services.

A semantic web technology based framework for educational-offer selection in higher education

Semantic web technology can be applied to a wide range of application domains. Educational-offer selection is an area of the education domain, which can benefit greatly from the application of semantic web technology. The paper explores the educational-offer selection problem as an application of semantic web technology. It specifically deals with the problem of higher education admission. A semantic web-based model for educational-offer selection is presented. Further, Semantic Web Service (SWS) selection and composition models are shown to be applicable to the problem of educational-offer selection. A prototype system, implementing semantic web-based educational-offer selection, is presented.

A Hybrid Model for Service Selection in Semantic Web Service Composition

One of the most important aspects of semantic web service composition process is the selection of most appropriate semantic web service. The Quality of Service (QoS) and cognitive parameters can be a good basis for this selection process. In this paper, we have presented a hybrid selection model for the selection of semantic web services based on their QoS and cognitive parameters. The presented model provides a new approach of measuring the QoS parameters in an accurate way and provides a completely novel and formalized measurement of different cognitive parameters.

A novel ANN-based service selection model for ubiquitous computing environments

How to choose an appropriate service from all the usable services regardless of user's location and heterogeneous architecture of underlying software and hardware infrastructure is the most important study content in ubiquitous computing domain. In order to overcome the shortcomings of blindness and randomicity in traditional and improved trust-mechanism-based service selection models, we propose a novel ANN-based (Artificial Neural Network) service selection model (called the ANNSS model). We adopt a novel method which according to the earlier information of the cooperation between the devices and the context information, an ANN-based evaluation standard for the service quality of service provider is given out so that user can acquire an effective guidance and choose the most appropriate service. At the same time, we improved the traditional BP algorithm based on three-term method (called the TTMBP algorithm) consisting of a learning rate (LR), a momentum factor (MF) and a proportional factor (PF) in order to satisfy the requirements of time issue in real-time system. The convergence speed and stability were enhanced by adding the proportional factor. The self-adjusting architecture method is adopted so that a moderate scale of neural network can be obtained. We have implemented the ANNSS algorithm in an actual power supply system for communication devices and fulfilled various simulations. The results of simulation show that the proposed service selection scheme is not only scalable but also efficient, and that the novel BP algorithm based on three-term has high convergence speed and good convergence stability. The novel service selection scheme superior to the traditional and improved trust-mechanism-based service selection scheme. The novel scheme can exactly choose a most appropriate service from many service providers and provide the most perfect service performance to users.

A framework for QoS computation in web service and technology selection

Abstract There is a global trend of convergence of voice, video and data. Industry is facing a major shift from circuit-based voice networks to packet-based converged networks. In the last few years, data networks have been growing at much faster rate than voice networks, mainly due to the growth of Internet. As a result of this trend, more and more data are being sent over Data Networks (Voice over IP, Voice over Frame Relay, and Voice over ATM). The standards used are SOAD, WSDL, and UDDI. The emerging Service-Oriented Computing ( SOC ) paradigm promises to enable businesses and organizations to collaborate in an unprecedented way by means of standard web services. To support rapid and dynamic composition of services in this paradigm, web services that meet requesters' functional requirements must be able to be located from a large and constantly changing number of service providers based on their Quality of Service ( QoS ). In order to enable quality-driven web service selection, we need an open, secure and fair framework to evaluate the QoS of a vast number of web services. The fair computation and enforcing of web services should have minimal overhead but yet able to achieve sufficient trust by both service requesters and providers. In this paper I present our open and fair QoS computation model for web service selection.

A mathematical programming model for optimal service selection in the airline industry

Airlines, offering tour plans, have to select a few gateways (airports) to which the air tickets for tour plans are issued. A number of alternate gateways are available for different tour plans, and the selection of gateways is influenced by the perceived preferences of tourists for these and competing plans. In this paper, using the ordinal preferences of tourists, we formulate the problem of gateway selection as an integer program. The solution procedure presented for this integer program is computationally attractive as we exploit the special structure of our formulation. The model proposed is general, and can be applied to the simultaneous positioning of several new products or services.