Domain-aware web service clustering based on ontology generation by text mining

Due to the emergence of Internet technologies and service-oriented computing, there is a rapid growth in the quantity and variety of services on the web. Discovering the web services as per the request is not an easy task because of the advancement of service-oriented computing which includes web services, cloud services, mobile services, etc. These services are dynamic and published according to the emerging standards in repositories. Web service clustering plays a crucial role in web service discovery. When services are grouped according to the similarity, then it reduces the search space and time, so that services can be discovered efficiently. Many eminent researchers have proposed approaches for efficient web service discovery by incorporating web service clustering. In this paper, we review different approaches that are proposed for web service clustering to enhance the discovery process. A comparison among existing approaches is carried out based on the vector representation approach, dimensionality reduction technique, method to capture semantic relationship among features, clustering technique, type of input dataset, number of web services in dataset and service repository. This review will help the researchers to understand the existing techniques to group services in similar clusters to improvise service discovery and the scope for improvement by future directions.KeywordsService-oriented architectureWeb services/Web APIWeb service clusteringWeb service discovery

Web services (WS) is called composite or compound when its execution involves interactions with other WS to utilize their features. The service providers published the web services through the internet as independent software components that are fulfilling the requirements of customer request. Clustering is more necessary for efficient web service discovery and web service composition processes. Clustering process groups the similar type of web services. In this paper, efficient clustering methods such as k-means clustering, Hierarchical agglomerative clustering and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering are introduced for web service clustering. The k-means clustering is a kind of partitional clustering where the web pages are divided into subsets with no hierarchy defined over them and the hierarchical agglomerative clustering is a type of hierarchical clustering where the web pages are arranged in tree structure in which leaves represents the data points and nodes denotes the clusters. BIRCH is an integrated hierarchical clustering algorithm uses the clustering features and cluster feature tree for general cluster description. Based on these clustering methods, web pages are clustered which are used for web service discovery and web service composition. The experiments are conducted on number of web services and the efficiency is evaluated in terms of accuracy, precision, recall and run time.

The number of web services available on the Internet has grown rapidly. Service consumers face a hard decision over which service to choose among the available ones. Security holds a key after various vulnerabilities have been exploited by attackers on number of notable web services. This paper carries out a survey on how security has been expressed and promised for web services, through both the Web Service Description Language and Service Level Agreements. It reviews existing technologies used for comparing individual web services, as well as for service compositions. Taking security into account further complicates the already difficult process of choosing the right service. The paper reveals that despite existing efforts, a quantitative solution needs to be established urgently in order to help service consumers to choose the most secure service for them to use.

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.

Efficient and fast service discovery becomes an extremely challenging task due to the proliferation and availability of functionally-similar web services. Service classification or service grouping is a popular and widely applied technique to classify services into several groups according to similarity, in order to ease up and expedite the discovery process. Existing research on web service classification uses several techniques, approaches and frameworks for web service classification. This study focused on a hybrid service classification approach based on a combination of ontology generation and machine learning algorithm, in order to gain more speed and accuracy during the classification process. Ontology generation is applied to capture the similarity between complicated words. Then, two machine learning classification algorithms, namely, Support Vector Machines (SVMs) and Naive Bayes (NB), were applied for classifying services according to their functionality. The experimental results showed significant improvement in terms of accuracy, precision and recall. The hybrid approach of ontology generation and NB algorithm achieved an accuracy of 94.50%, a precision of 93.00% and a recall of 95.00%. Therefore, a hybrid approach of ontology generation and NB has the potential to pave the way for efficient and accurate service classification and discovery.

Nowadays, with the advancement in mobile technologies, the use of web services has seen an explosion in interest for mobile computing environment. However, the heavyweight nature of conventional web services to be deployed on mobile devices brings new challenges in mobile computing in the coming future. Web services technology and mobile computing domains are converging at their intersection which leads to Mobile Web Services (MWS) which enables the service access in users’ mobile device. As the numbers of web services are increasing dramatically, the web service discovery becomes more important to discover the usable web services in effective and efficient manner. This paper discusses an overview of mobile challenges in MWS recent research, and a summary of the issues. Moreover, current approaches of Mobile Web Services Discovery are classified and these approaches are compared to some criteria. The results of this study will help researchers to deliver more applicable solutions with the most appropriate approaches for Mobile Web Services Discovery.KeywordsWeb ServicesMobile Web Serviceweb service discoverymobile computingmobile web service discovery

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

Today, web service plays a huge part where multiple applications used by people in their daily lives such as weather information or healthcare for accessing data. Therefore, our study takes an important study on why the web service discovery algorithm is a better option over available web services on the internet. Web service and various web platforms in tracking and updating information; for instance, in recent research, a high-efficiency interface for web service was discovered to make use of semantics, abbreviation, and fragment combination of user input which is OPD (Operation Discovery Algorithm), rather than the traditional algorithm. However, a problem identification where the end-user still needed a refined solution of using high trusted value. On the other hand, a re-ranking approach among the output result in between the recommended web services discovery need to perform by the organization. Further, OPD can be enhanced using efficient web service discovery, along with the highest public recommendation.

Semantic Web Services are the key technology providing services for the users' convenience in the semantic web environment. Many companies in various fields are researching and developing languages for constructing Semantic Web Services such as DAML-S, WSDL, X-LANG and BPEL4WS. DAML-S is a method that accesses the existing Web Service method from a semantic web environment. However, the current Semantic Web Services Discovery System does not provide sufficient processing of quality factors (performance, accessibility, availability, reliability and transaction) nor reliable and accurate service results desired by the user through a UDDi search method. The present study analyzes the disadvantages of the current web services and suggests a Semantic Web Services Discovery System based on QoS applying semantic web technologies as a solution to the problems. The suggested system provides a UDDi and DAML-S based discovery engine to allow efficient web service discovery and composition.

Clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

In modern heterogeneous environments, such as mobile, pervasive and ad-hoc networks, architectures based on web services offer an attractive solution for effective communication and inter-operation. In such dynamic and rapidly evolving environments, efficient web service discovery is an important task. Usually this task is based on the input/output parameters or other functional attributes, however this does not guarantee the validity or successful utilization of retrieved web services. Instead, non-functional attributes, such as device power features, computational resources and connectivity status, that characterize the context of both service providers and consumers play an important role to the quality and usability of discovery results. In this paper we introduce context-awareness in web service discovery, enabling the provision of the most appropriate services at the right location and time. We focus on context-based caching and routing for improving web service discovery in a mobile peer-to-peer environment. We conducted a thorough experimental study, using our prototype implementation based on the JXTA framework, while simulations are employed for testing the scalability of the approach. We illustrate the advantages that this approach offers, both by evaluating the context-based cache performance and by comparing the efficiency of location-based routing to broadcast-based approaches.

Service-oriented computing is gaining momentum. Now-a-days web services are geared up to occupy center stage as part of this adoption. In this new paradigm, existing business logic would be wrapped as web services to be accessible on the web. As the number of web services is expected to significantly increase, this would have the effect of introducing competition among web services that offer similar functionalities. Selection of best service based on their expected quality such as price, response time has more importance. Sound framework is essential to organize web services. The identified key challenges are publishing of service QoS information, the matching of QoS requirements. A model for web services discovery is proposed as a solution. The discovery agent finds services that best meet a consumer's QoS and rating requirements that are specified in a discovery request.

ABSTRACTService substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

Tagging technique is widely used to annotate objects in Web 2.0 applications. Tags can support web service understanding, categorizing and discovering, which are important tasks in a service-oriented software system. However, most of existing web services' tags are annotated manually. Manual tagging is time-consuming. In this paper, we propose a novel approach to tag web services automatically. Our approach consists of two tagging strategies, tag enriching and tag extraction. In the first strategy, we cluster web services using WSDL documents, and then we enrich tags for a service with the tags of other services in the same cluster. Considering our approach may not generate enough tags by tag enriching, we also extract tags from WSDL documents and related descriptions in the second step. To validate the effectiveness of our approach, a series of experiments are carried out based on web-scale web services. The experimental results show that our tagging method is effective, ensuring the number and quality of generated tags. We also show how to use tagging results to improve the performance of a web service search engine, which can prove that our work in this paper is useful and meaningful.

Web service tags, terms annotated by users to describe the functionality or other aspects of Web services, are being treated as collective user knowledge for Web service mining. However, the tags associated with a Web service generally are listed in a random order or chronological order without considering the relevance information, which limits the effectiveness of tagging data. In this paper, we propose a novel tag ranking approach to automatically rank tags according to their relevance to the target Web service. In particular, service-tag network information is utilized to compute the relevance scores of tags by employing HITS model. Furthermore, we apply tag ranking approach in Web service clustering. Comprehensive experiments based on 15,968 real Web services demonstrate the effectiveness of the proposed tag ranking approach.