Web Service Ranking Research Articles

Knowledge distillation representation and DCNMIX quality prediction‐based Web service recommendation

SummaryWeb service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC.

AN APPROACH WEB SERVICE SELECTION BY QUALITY CRITERIA BASED ON SENSITIVITY ANALYSIS OF MCDM METHODS

Context. The problem of QoS based Web service from the list of Web services with equal or similar functionality was considered. This task is an essential part of the processes of finding, discover, matching and using Web services on the Internet due to the numerous offerings of Web services with equal or similar functionality. The reasonable selection of a suitable Web service takes into account a lot of user’s quality requirements, such as response time, throughput, reliability, cost, etc. Such a task is usually formulated as an MCDM problem, in which the parameters are the Web service quality factors and the importance degree of these factors. The object of this research is a process of selection Web services using MCDM methods, taking into accounts the user’s preferences and requirements to the Web service quality characteristics. The subject of the research is the LSP method, which, in addition to the degree of importance of the criteria used in all MCDM methods, simulates the user’s reasoning about quality, taking into account, in particular, such characteristics of the criteria as mandatory, sufficiency, desirability, simultaneity and substitutability. Objective. The objective of the work is to develop an approach for comparing the result of using the LSP method with the results of using other MCDM methods. Method. A method for calculating the weights of input criteria that are not always explicitly specified in the LSP method was proposed. For this, the conjunctive coefficients of impact are used, which are calculated as a result of the sensitivity analysis of the Web service generalized quality criterion to changes the partial quality criteria. This method underlies the proposed approach to comparing the efficiency of the LSP method with other MCDM methods, which consists of using the obtained weights as the weights of the input criteria for the MCDM methods. Results. The developed method and approach was verified experimentally. The Web service ranking produced by the LSP method was compared with the ones produced by SAW, AHP, TOPSIS and VIKOR methods. This comparison confirmed the efficiency of the proposed method and approach. Conclusions. From the obtained results of comparing the LSP method and the MCDM methods considered in this study, it follows that the proposed method and approach provide the equivalent input conditions for these methods as for the LSP method, which is a necessary condition for the correct comparison of MCDM methods. The use of the proposed approach made it possible to study the sensitivities of the considered MCDM methods. In practical applications, this approach can be used to select a suitable MCDM method. The proposed method can be useful for creating professional evaluation systems in which it is necessary to assess the importance (weights) of tens and hundreds of quality criteria.

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

Web services have emerged as an accessible technology with the standard ’Extensible Mark Up’ (XML) language, which is known as ’Web Services Description Language’ WSDL. Web services have become a promising technology to promote the interrelationship between service providers and users. Web services users’ trust is measured by quality metrics. Web service quality metrics vary in many benchmark datasets used in the existing studies. The selection of a benchmark dataset is problematic to classify and retest web services. This paper proposes a method to rank web services quality metrics for the selection of benchmark web services datasets. To measure the diversity in quality metrics, factor analysis with Varimax rotation and scree plot is a well-established method. We use factor analysis to determine percentage variance among principal factors of four benchmark datasets. Our results showed that the two-factor solution explained 94.501, 76.524, and 45.009% variances in datasets A, B, and D, respectively. A three-factor solution explained 85.085% variance in dataset C. Reliability, and response time quality metrics were predicted as the most dominating quality metrics that contributed to explain the percentage variance in four datasets. Our proposed web metric ranking (WMR) method resulted in reliability as the top-most web metric with (57.62%) score and latency web metric at the bottom-most with (3.60%) score. The proposed WMR method showed a high (96.17%) ranking precision. Obtained results verified that factor solutions after reducing the dimensions could be generalized and used in the quality improvement of web services. In future works, the authors plan to focus on a dataset with dominating quality metrics to perform regression testing of web services.

Classification of web services using data mining algorithms and improved learning model

As per the global digital report, 52.9% of the world population is using the internet, and 42% of the world population is actively using e-commerce, banking, and other online applications. Web services are software components accessed using networked communications and provide services to end users. Software developers provide a high quality of web service. To meet the demands of user requirements, it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset and found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conducted experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are compared with various techniques random forest, artificial neural network, J48 decision tree, extreme gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of 98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes.

Subset Matching based Selection and Ranking (SMSR) of Web Services

Subset Matching based Selection and Ranking (SMSR) of Web Services

QoS Based Discovery and Service Provider System

We propose a two-step, context-based semantic approach to the problem of matching and ranking Web services for possible service composition. Semantic understanding of Web services may provide added value by identifying new possibilities for compositions of services and context-based semantic. The semantic matching ranking approach is unique since it provides the Web service designer with an explicit numeric estimation of the extent to which a possible composition. This process consists of multiple services that can be executed in sequence or in a parallel process. Given a service request, a set of candidates (available services and service patterns) is dynamically generated layer by layer from inputs to outputs of this request. For each layer, the algorithm traverses a priori search space which is a set of service patterns from historical solutions, then it searches available services from the repositories. That means each layer contains all services and service patterns that can be executed with a set of outputs provided by previous layers. The search process terminates until all the outputs of a request are obtained.

Service Profile based Ontological System for Selection and Ranking of Business Process Web Services

Service Profile based Ontological System for Selection and Ranking of Business Process Web Services

A Hybrid Algorithm for Efficient Ranking of Web Services, Emphasizing on Solution to the Coldstart Problem

A Hybrid Algorithm for Efficient Ranking of Web Services, Emphasizing on Solution to the Coldstart Problem

Diversifying web service recommendation results via exploring service usage history

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Weighted Quality of Service based Ranking of Web Services

Background/Objectives: The primary objective of the study is to evaluate the performance of Composite Web Service based on the parameters relating to Quality of Service by using statistical methodologies. Methods/Statistical Analysis: By using Web Service Crawler Engine we have collected data on eleven parameters of interest from a more number of Web Services. Among the eleven parameters we have selected six parameters of importance using the sampling of thirty Web Services. We have constructed the weight matrix for the parameters. By using Web Services Relevancy Function ranking has been done for three different scenarios. Using Spearman's Rank Correlation Coefficient we have compared the scenarios. Findings: The graphical representation of the three scenarios shows varying pattern. The Spearman's Rank Correlation coefficient of the Web Services Relevancy Function varied widely among the sampled Web Services. F-Test revealed that there are no significant differences in respect of weights of Quality of Web Services for three different scenarios. We concluded that weight assigned for Quality of Service parameters in Scenario I is preferred than the scenarios II and III through the evaluations. Application/Improvements: The technique developed in this research paper can be applied for the comparison of Web Services during discovery to enhance the performance of the Composite Web Service.

A New Improved Clustering Algorithm based Diversified Web Page Recommendation

tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user's potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user's QoS preferences as well as diversification of the web services list. First, the active user's usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity- aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph. Keywordsservice recommendation, diversity, user interest,

An Effective Web Service Ranking Method via Exploring User Behavior

Service-oriented computing and Web services are becoming more and more popular, enabling organizations to use the Web as a market for selling their own Web services and consuming existing Web services from others. Nevertheless, with the increasing adoption and presence of Web services, it becomes more difficult to find the most appropriate Web service that satisfies both users’ functional and nonfunctional requirements. In this paper, we propose an effective Web service ranking approach based on collaborative filtering (CF) by exploring the user behavior, in which the invocation and query history are used to infer the potential user behavior. CF-based user similarity is calculated through similar invocations and similar queries (including functional query and QoS query) between users. Three aspects of Web services—functional relevance, CF based score, and QoS utility, are all considered for the final Web service ranking. To avoid the impact of different units, range, and distribution of variables, three ranks are calculated for the three factors respectively. The final Web service ranking is obtained by using a rank aggregation method based on rank positions. We also propose effective evaluation metrics to evaluate our approach. Large-scale experiments are conducted based on a real world Web service dataset. Experimental results show that the proposed approach outperforms the existing approach on the rank performance.

A survey on Web Service Selection and Ranking Methods

day's web service is getting more and more popularity because of its characteristic like loosely coupled, composable, reusable, platform independent etc... Due to this popularity web services are developed with similar functionality. When user search web service in directory, Directory retrieve many web service with similar functionality. Web service recommendation only base on functionality matching is not good approach. In this situation web services are recommend base on QoS. QoS is considering as secondary approach for service selection. QoS consider different Non function properties of web service like response time, reliability, availability etc... This paper conduct a survey on different web service Selection and Ranking process. KeywordsWeb Service, Web Service Selection, Web Service Ranking.

Efficient Discovery and Ranking of Web Services Using Non-functional QoS Requirements for Smart Grid Applications

Abstract Web service discovery is the important process and traditional methods use keyword-based search to locate the appropriate web service. This method may not yield good results. Non-functional QoS parameters of a web service play a vital role in ranking a web service. Moreover, standards such as UDDI, WSDL and SOAP have potential of providing QoS based web service discovery. In this paper, a new method is proposed to rank the web services. The proposed method computes relevancy function value of the web services and normalization is performed. This normalized result is used to rerank the web service. The selection of appropriate web service provide user to access smart grid over internet. The experimental results show that the proposed method is effective in selecting appropriate web services than traditional methods

Enhancing Fuzzy Topsis for web service selection

With proliferation of web services, quality-of-service QoS capability is becoming a significant factor for distinguishing services. QoS includes various non-functional issues such as performance, reliability and availability, etc. The functional requirements are assured through Semantic Matchmaking algorithm whereas the non functional requirements are guaranteed though QoS factors. Selection of a web service has become very crucial due to the availability of multiple web service with similar functionality. To solve this issue there is a need to distinguish the services based on their non-functional criteria QoS. This paper proposes an enhanced QoS-based semantic web service selection and ranking solution with the application of Fuzzy Topsis method. Further, this paper proposes a new dynamic replication algorithm to increase the availability of the service and decrease network delays. This is demonstrated using a diabetes monitoring system.

An Automatic Web Service Composition Framework Using QoS-Based Web Service Ranking Algorithm.

Web service has become the technology of choice for service oriented computing to meet the interoperability demands in web applications. In the Internet era, the exponential addition of web services nominates the “quality of service” as essential parameter in discriminating the web services. In this paper, a user preference based web service ranking (UPWSR) algorithm is proposed to rank web services based on user preferences and QoS aspect of the web service. When the user's request cannot be fulfilled by a single atomic service, several existing services should be composed and delivered as a composition. The proposed framework allows the user to specify the local and global constraints for composite web services which improves flexibility. UPWSR algorithm identifies best fit services for each task in the user request and, by choosing the number of candidate services for each task, reduces the time to generate the composition plans. To tackle the problem of web service composition, QoS aware automatic web service composition (QAWSC) algorithm proposed in this paper is based on the QoS aspects of the web services and user preferences. The proposed framework allows user to provide feedback about the composite service which improves the reputation of the services.

Improving semantic Web services discovery and ranking: A lightweight, integrated approach

Los Servicios Web Semanticos (SWSs) se han convertido en un area de investigacion muy activa, en la que diversos frameworks, como WSMO u OWL-S, definen ont ologias de la Web Semantica para describir servicios Web, de forma que puedan descubrirse, ordenarse, componerse y ejecutarse automaticamente, de acuerdo a los requisitos y preferencias del usuario. En concreto, diversas tecnicas de descubrimiento y ranking han sido propuestas, incluyendo herramientas relacionadas. Sin embargo, las propuestas existentes ofrecen una expresividad limitada para definir preferencias, siendo muy dependientes de los formalismos subyacentes. Ademas, las tecnicas semanticas actuales sufren de problemas de rendimiento, interoperabilidad e integracion, impidiendo su explotacion efectiva. Para solucionar estos problemas, la investigacion se centra actualmente en el desarrollo de descripciones de SWSs ligeras, las cuales faciliten la interoperabilidad de las propuestas actuales, asi como en soluciones para el descubrimiento y el ranking que proporcionen mejor rendimiento con una disminucion acotada en su precision. En esta memoria de tesis, resolvemos esos desafios mediante la propuesta de SOUP, un completo modelo ontologico de preferencias sobre el que hemos desarrollado unas herramientas ligeras, llamadas EMMA y PURI, que mejoran el rendimiento del descubrimiento e integran las propuestas actuales de ranking, respectivamente. Nuestras contribuciones han sido evaluadas aplicandolas a escenarios sinteticos y reales. En primer lugar, la expresividad e independencia del modelo de preferencias SOUP se ha validado mediante la descripcion completa de escenarios complejos del SWS Challenge. Ademas, hemos llevado a cabo un estudio experimental de EMMA que muestra una mejora de rendimiento significativa, obteniendo una penalizacion en la precision insignificante. Por ultimo, hemos aplicado PURI dentro del proyecto SOA4All, integrando sus tres mecanismos de ranking (objetivo, basado en NFP, y en logicas difusas) en una solucion de descubrimiento y ranking interoperable.

QOS Aware Formalized Model for Semantic Web Service Selection

Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS) are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS) based Ranking of semantic web services. This paper focuses on various concepts of Quality of Service associated with web services. Various QoS parameters like performance, availability, reliability and stability etc. are formalized in order to enhance the pertinence of web service selection. A QoS mediator agent based Web Service Selection Model is proposed where QoS Consultant acts as a Mediator Agent between clients and service providers. Model suggests user’s preferences on QoS parameter selection. The proposed model helps to select pertinent Web Service as per user’s requirement and reduce the human effort.. Further process of adding ontology with semantic web services is also illustrated here.

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer's requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.