Web Service Discovery Approach Research Articles

Web service discovery approach: application in e-healing domain

The main objective of today's companies is to cope with rapid changes in the environment. For this, they must ensure the integration and interoperability of their applications. To manage and automate the life cycle of these applications, companies are adopting web services technology. The current problem is that the content of these web services cannot be processed automatically. Only humans can interpret its contents. The semantic web is a new vision of the web that promises to overcome this difficulty. The goal of this technology is to automate the retrieval, assembly, and selection of web services. In this post, we are interested in semantic detection of web services. The main problem is automatically discovering web services on request from clients. Against this background, we first describe the principle of the proposed detection mechanism and then present the designed matchmaking algorithm. Finally, we implement our proposed method. To verify our work, we run tests against various user requests and web service panels. As part of a case study, we consider an online hospital problem. This problem is a typical web service discovery scenario to which the concepts of our method are applied.

A Web Service Discovery Approach Based on Operation Discovery with Ranking Algorithm

Abstract: Today’s all people aware with Grid Computing. It is resource sharing and coordinated problem solving in dynamic multi–institutional virtual organization. Trust is a characteristics and quality of a Grid Computing. It is enabling of confidence that something will or will not occur in a predictable manner. It is supported on identification, authentication, accountability, authorization and availability. Trust Model identifies the specific mechanisms that are necessary to respond to a specific threat profile. Threat Profile identifies the specific threats that are most likely to put environment at risk. Web Service Prediction Framework is used for the service discovery and web content, it explaining an algorithm for the induction’s rule for prediction. This framework description model contains the path type of interface parameter. This method shows overall matching of interface by assuming abbreviation of synonyms and combined form of disordered fragments into outcomes in form of high precision, recall and F-measure. The current approach is dealing with query of word related or it may be semantic in discovery dataset. Further web service mining of synaptic from dataset in knowledge of domain can be work in future. It will help to investigate the better approach to calculate relationship of trust and drawback evaluation

Proposition of Web Services Discovery and Composition Approach: Application in E-learning Platform

The rapid penetration of Web technologies and strong economic pressure have led to an irresistible proliferation of Web services. In this paper, we are interested in the discovery and composition of Web services. In this sense, we propose a conceptual architecture and technical tools to carry out our approach. The originality of the proposed solution lies in the use of mixed tools ranging from multi-agents systems to Business Process Management (BPM) techniques, including Matchmaking algorithms and semantic models.

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

Web Service Discovery Approaches for Dynamic Mobile Environment

Mobile web service (MWS) discovery is taking a new direction due to the explosion of users accessing mobile services using diverse mobile devices, coupled with the persistent changes in a dynamic mobile environment (DME). This leads to renewed adoption of lightweight solutions for the identification of the most suitable web services that correspond with the service requests. Contemporary mobile web service discovery approaches are plagued with performance and accuracy problems and are rarely compatible with the DME. The objective of this systematic literature review is to develop a more rigorous understanding and identify recent research trends in mobile web service discovery techniques in a dynamic mobile environment. This review followed the systematic literature review (SLR) guidelines. Essential information was extracted from the 76 relevant articles in line with the formulated questions and finally reported after in-depth analysis. The results of this study discuss the critical contributions and limitations of the proposed approaches.

A novel and efficient index based web service discovery approach

Nowadays, with the expansion of the number of web services, the discovery and the selection of the latter are becoming complex tasks. To address this issue, different methods (indexing, clustering, database etc.) have been proposed in the literature so far. In this paper, we deal with index based methods to manage web service repositories. Indeed, using indexes perspective in web services discovery can help to promote the web service composition. Within this context, we propose a novel index method named Multi Level Sorted Index (MLSI) to improve the run time of the discovery process. The implementation and the simulation of our method have shown the efficiency of our approach in comparison with the inverted index and Multilevel Index Model (MLIM) in term of retrieval and addition times.

Self-adaptive mobile web service discovery approach based on modified negative selection algorithm

Self-adaptive mobile web service discovery approach based on modified negative selection algorithm

QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS

Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.

Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external semantics of the Web service description. Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model. Extensive experiments on several datasets demonstrated that the proposed approach achieves improved performance in terms of F-measure. The results also suggest that leveraging external information is useful for semantic sparse Web service discovery.

Web service discovery with incorporation of web services clustering

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach.

A user requirement oriented web service discovery approach based on logic and threshold petri net

In recent years, the number of Web services has increased significantly. Web service discovery has drawn much attention with the development of Web service applications and big data analysis. Under this circumstance, traditional Web service discovery strategies cannot adequately meet high user requirements due to the efficiency and precision of service discovery is low. In order to improve the accuracy and efficiency of service discovery, a user requirement oriented Web service discovery approach based on Petri nets is proposed in this study. A data preprocessing strategy of Web service is first designed. Then, a service clustering method is proposed based on Petri nets, which can conduct service cluster head generation, service cluster composition, and service discovery. The proposed method utilizes a superior data preprocessing method. Using simulation experiments, the efficiency and precision of Web service discovery are illustrated. Finally, the application value of the approach on real Web service is discussed.

Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA

Nowadays, a growing number of web services are offered in API marketplaces browsed by service developers or third-party registries. Under this situation, API marketplaces' users greatly rely on a search engine to find suitable web services. However, due to the fact that functional attributes of web services are usually described in short texts, the search engine-based discovery approach suffers from the semantic sparsity problem, which hinders the effect of service discovery. To address this issue, we propose a novel web service discovery approach using word embedding and Gaussian latent Dirichlet allocation (Gaussian LDA). Unlike most existing service discovery approaches, our approach first uses context information generated by word embedding to enrich the semantics of service descriptions and users' queries. Then, the enriched service description is loaded into the Gaussian LDA model to acquire service description representation. Finally, the services are ranked by considering the relevance between the extended user's query and service description representation. The experiments conducted on a real-world web service dataset and the results demonstrate that the proposed approach achieves superior effectiveness on web service discovery.

Flexible service discovery based on multiple matching algorithms

Traditional web service discovery approaches rely on logic or non-logic matching techniques. In general, logic approaches can achieve satisfactory precision levels, but they result in modest recall scores. In contrast, non-logic approaches may ensure more balanced scores in terms of recall and precision, but they need additional aggregation schemes or optimisation methods. To improve the discovery performance, we need to combine multiple matching algorithms and fuse their results into a single ranked list of services. This combination must avoid the well-known side effects of fusion, such as overfitting or noise sensitivity. To tackle the service-discovery issue, we propose a solution based on two key ideas: first, we propose a majority voting model based on the 'Condorcet' paradigm to fuse a set of individual ranked lists (provided by the matching functions). Second, we leverage a probabilistic extension of the dominance relationship to ensure comparison between the services. The experimental evaluations indicate the proposed solution, 'probabilistic Condorcet', outperforms all individual matching functions, as well as many concurrent fusion algorithms.

Semantic Web Services Discovery

This article describes how Web services play an important role in several fields such as e-commerce and e-health. As the number of Web services is increasing rapidly, finding the best Web service according to users' requirements becomes more challenging. The traditional method of Web service discovery is based on keyword match. Due to this, many Web services which are most relevant to the user request are left undiscoverable. Some other emergent approaches are based on semantics to improve the quality of the discovered Web services in terms of relevance and satisfaction of user's need. In this paper, the authors present a survey of existing semantic Web services discovery approaches giving priority to relevant ones. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to be addressed to substantially enhance the semantic Web service discovery.

Tagging augmented neural topic model for semantic sparse Web service discovery

SummarySearch engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real‐world Web service dataset demonstrate the effectiveness of the proposed approach.

Exploiting Semantic Web Services in the Development of Context-Aware Systems

Nowadays, web services have become increasingly used and deployed in view of the benefits that they offer. Thus, the increasing of providers and service offerings as well as the features of mobile devices has made the selection of relevant services a difficult operation. In addition, the information related to the user, called context / situation, became very useful for the implementation of the eloquent choice of the sought-after functionalities. In this paper, we present a comparative study of semantic web services and service discovery approaches. Then we propose the model of a context-aware semantic service with their process of implementation. We also highlight the discovery algorithm based on the concept of context / situation.

Collaborative Web Service Discovery and Recommendation Based on Social Link

With the increasing application of web services in varying fields, the demand of effective Web service discovery approaches is becoming unprecedentedly strong. To improve the performance of service discovery, this paper proposes a collaborative Web service discovery and recommendation mechanism based on social link by extracting the latent relationships behind users and services. The presented approach can generate a set of candidate services through a complementary manner, in which service discovery and service recommendation could collaborate according to the formalized social link. The experimental results reveal that the proposed mechanism can effectively improve the efficiency and precision of Web service discovery.

A SEMANTIC MODEL FOR WEB SEARCH AND DATA MATCHING USING LDA

An abstract architecture for semantic web services is requisite to present a conceptual model to assist in the principled design and implementation of semantic web service applications. The architectural structure we portray stands on the shoulders of two promising technical conceptions: Web Services and the Semantic Web. Automatic discovery is acknowledged as a key task and semantic description is the foundation for automatic service discovery. We portray a novel approach for automatic discovery of semantic Web services which utilize LDA to match a user request, articulated in service discovery language, with a semantic Web service description. Our approach to semantic based web service discovery entails semantic-based service categorization and semantic enhancement of the service request. We intend a solution for accomplishing functional level service categorization based on an ontology framework. Furthermore, we exploit clustering for precisely categorizing the web services based on service functionality.

A Web Services Discovery Approach Based on Mining Underlying Interface Semantics

In recent years, Web service discovery has been a hot research topic. In this paper, we propose a novel Web services discovery approach, which can mine the underlying semantic structures of interaction interface parameters to help users find and employ Web services, and can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Our approach is based on mining the underlying semantics. First, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. This index library can help provide a high-efficiency interface that can match not only synonyms but also abbreviations and fragment combinations. Finally, we propose a Web service Operations Discovery algorithm (OpD). The OpD discovery results include two types of Web services: services with “Single” operations and services with “Composite” operations. The experimental evaluation shows that our approach performs better than other Web service discovery methods in terms of both discovery time and precision/recall rate.

A semantic web services discovery approach based on a mobile agent using metadata

PurposeThe success of web services involved the adoption of this technology by different service providers through the web, which increased the number of web services, as a result making their discovery a tedious task. The UDDI standard has been proposed for web service publication and discovery. However, it lacks sufficient semantic description in the content of web services, which makes it difficult to find and compose suitable web services during the analysis, search, and matching processes. In addition, few works on semantic web services discovery take into account the user’s profile. The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services.Design/methodology/approachThe authors propose a new approach for the semantic web services discovery based on the mobile agent, user profile and metadata catalog. In the approach, each user can be described by a profile which is represented in two dimensions: personal dimension and preferences dimension. The description of web service is based on two levels: metadata catalog and WSDL.FindingsFirst, the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “semantic match”. The result of this first matching restricts the search space at the level of UDDI registry, which allows the users to have good results for the “functional match”. Second, the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information. Finally, the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service.Originality/valueTo the best knowledge of the authors, this is the first attempt at implementing the mobile agent technology with the semantic web service technology.