Web Service Search Engine Research Articles

Unveiling the Hidden Dynamics: exploring causative factors and impact of employee turnover on organisational performance.

Employee turnover is a widely recognised subject in an organisational setting. Although academics and organisational human resource departments have accepted the fact that employee turnover can have a disastrous impact on the organisation, there are limited studies conducted on the causative factors and effects of employee turnover in organisations. Therefore, this study helped to highlight some of the causative factors triggering employee turnover and their impact on an organisation. In addition, the study also suggests vital strategies for mitigating and reducing employee turnover. In line with the preceding assertion, the cross-sectional qualitative study taking into consideration conference papers, government periodicals, chronological documents, and mainly secondary and primary sources were used. Online search engines such as Scopus, EBSCO, Emerald, science direct, and unrestricted web service search engines such as google scholar and Mendeley demonstrated to be valuable database resources. The finding indicated that performance appraisal feedback, job insecurity, stress and work overload, death, and compensation are significant factors responsible for employee turnover was also deduced that employee turnover can affect organisational performance through ineffectuality in the delivery of service, increase in workload, errors in work processes and reduction in sense of loyalty. However, findings also postulated that training and development, competitive benefits and compensation, effective leadership, and job satisfaction are key procedures that help in reducing and mitigating employee turnover.

Reduction of Search Space in Restful Service Discovery

Web Services has been enabled IT services and computing technology to perform business services more efficiently and effectively. REpresentational State Transfer (REST) is to be used for creating Web APIs/services. In the existing system, web service search engines for RESTful Web Services/Api’s provide Keyword, Tag and Semantic based search functions. One of the RESTful service discovery, referred as Test-oriented RESTful service discovery with Semantic Interface Compatibility (TASSIC) have been developed by the search of RESTful Service’s/Api’s. TASSIC approach will search the semantic characteristics of search and match interface terms in the service document. An inability to consider the classification and in finding the suitable Api’s or services are a key issue of the search space in Tassic. A new approach has proposed for reduction of the search space in restful service discovery to develop a k-Nearest Neighbor classification algorithm. it provide candidate services with ranking based on semantic similarity, and classifying of similar candidate services and service unit testing will be considered. This approach is meant for increasing search precision in the retrieval and quick search for classifying their RESTful services or Api according to user-defined criteria.

A Web service search engine for large-scale Web service discovery based on the probabilistic topic modeling and clustering

With the ever increasing number of Web services, discovering an appropriate Web service requested by users has become a vital yet challenging task. We need a scalable and efficient search engine to deal with the large volume of Web services. The aim of this approach is to provide an efficient search engine that can retrieve the most relevant Web services in a short time. The proposed Web service search engine (WSSE) is based on the probabilistic topic modeling and clustering techniques that are integrated to support each other by discovering the semantic meaning of Web services and reducing the search space. The latent Dirichlet allocation (LDA) is used to extract topics from Web service descriptions. These topics are used to group similar Web services together. Each Web service description is represented as a topic vector, so the topic model is an efficient technique to reduce the dimensionality of word vectors and to discover the semantic meaning that is hidden in Web service descriptions. Also, the Web service description is represented as a word vector to address the drawbacks of the keyword-based search system. The accuracy of the proposed WSSE is compared with the keyword-based search system. Also, the precision and recall metrics are used to evaluate the performance of the proposed approach and the keyword-based search system. The results show that the proposed WSSE based on LDA and clustering outperforms the keyword-based search system.

Enhancing service discovery using cat swarm optimisation based web service clustering

Summary Web service discovery is a critical task in service oriented application development. Due to extensive proliferation in the number of available services, it is challenging to obtain all the relevant services available for a given task. For the retrieval of most relevant Web services, a user would have to use those service-specific terms that best describe and match the natural language documentation contained within a service description. This process can be time intensive, due to functional diversity of available services in a repository. Domain specific clustering of Web Services based on the similarities of their functionalities would greatly boost the ability of a Web service search engine to retrieve the most relevant service. In this paper, we propose a novel technique to cluster service documents into functionally similar service groups using the Cat Swarm Optimisation Algorithm. We present experimental results that show that the proposed technique was effective and enhanced the process of service discovery.

An automatic approach for tagging Web services using machine learning techniques1

Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, an increasing number of Web services have been developed. However, this huge collection of Web services makes the task of locating a suit able one more challenging as well as more difficult. Automatic clustering of Web services groups services with similar functions together. Clustering could greatly boost the power of Web service search engines and generate tags to improve the search accuracy of tag-based service recommendation. In this paper, we propose a Web service clustering technique based on Carrot search clustering and K-means to group similar services together. These clustered groups are then tagged. We also develop a tag-based service recommendation for WSDL documents using naive bayes algorithm to classify Web services into different tags. We demonstrate that the proposed clustering approach is effective for Web service discovery through two sets of real data.

Unified Collaborative and Content-Based Web Service Recommendation

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 increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collaborative filtering</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> content-based recommendation</i> . Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

A Trust-Aware Search Engine for Complex Service Computing

Service computing has become a key-enabling technology to support collaboration and interaction among business partners and customers. With the development of new emerging service-related computing paradigms such as Cloud Computing and Mobile Internet, more and more services are provided by different providers. These services are becoming increasingly complex. Aiming at recommending high-quality and trustful services in the complex service computing environment, this paper presents a trust-aware search engine by integrating service functionalities, QoS (quality of service) and service trust. The proposed search engine primarily contains four components: keyword-based service matching, service QoS evaluation, service reputation evaluation and a hybrid ranking method which combines the results yielded by the previous three components to produce final service recommendations. To evaluate the performance of the authors' service search engine, comprehensive experiments are conducted using a real Web service dataset. The experimental results show that our approach outperforms conventional QoS-based service selection methods. Finally, a prototype is also presented to validate the authors' trust-aware Web service search engine.

Multilabel Learning for Automatic Web Services Tagging

Recently, some web services portals and search engines as Biocatalogue and Seekda!, have allowed users to manually annotate Web services using tags. User Tags provide meaningful descriptions of services and allow users to index and organize their contents. Tagging technique is widely used to annotate objects in Web 2.0 applications. In this paper we propose a novel probabilistic topic model (which extends the CorrLDA model - Correspondence Latent Dirichlet Allocation-) to automatically tag web services according to existing manual tags. Our probabilistic topic model is a latent variable model that exploits local correlation labels. Indeed, exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Moreover, several existing systems can recommend tags for web services based on existing manual tags. In most cases, the manual tags have better quality. We also develop three strategies to automatically recommend the best tags for web services. We also propose, in this paper, WS-Portal; An Enriched Web Services Search Engine which contains 7063 providers, 115 sub-classes of category and 22236 web services crawled from the Internet. In WS-Portal, severals technologies are employed to improve the effectiveness of web service discovery (i.e. web services clustering, tags recommendation, services rating and monitoring). Our experiments are performed out based on real-world web services. The comparisons of Precision@n, Normalised Discounted Cumulative Gain (NDCGn) values for our approach indicate that the method presented in this paper outperforms the method based on the CorrLDA in terms of ranking and quality of generated tags.

Research of Distributed Search Engine Based on Hadoop (DSEH)

The study of distributed search engine based on Hadoop (referred to as DSEH) has put forward system structure of distributed web service search engine based on Map/Reduce, and made introduction to related modules. Built the whole system on Hadoop framework by Map/Reduce and analyzed the key technologies of distributed search engine.

Clustering Web services to facilitate service discovery

Clustering Web services would greatly boost the ability of Web service search engine to retrieve relevant services. The performance of traditional Web service description language (WSDL)-based Web service clustering is not satisfied, due to the singleness of data source. Recently, Web service search engines such as Seekda! allow users to manually annotate Web services using tags, which describe functions of Web services or provide additional contextual and semantical information. In this paper, we cluster Web services by utilizing both WSDL documents and tags. To handle the clustering performance limitation caused by uneven tag distribution and noisy tags, we propose a hybrid Web service tag recommendation strategy, named WSTRec, which employs tag co-occurrence, tag mining, and semantic relevance measurement for tag recommendation. Extensive experiments are conducted based on our real-world dataset, which consists of 15,968 Web services. The experimental results demonstrate the effectiveness of our proposed service clustering and tag recommendation strategies. Specifically, compared with traditional WSDL-based Web service clustering approaches, the proposed approach produces gains in both precision and recall for up to 14 % in most cases.

Humans, Semantic Services and Similarity: A User Study of Semantic Web Services Matching and Composition

Inferring similarity between Web services is a fundamental construct for service matching and composition. However, there is little evidence of how humans perceive similarity between services, a crucial knowledge for designing usable and practical service matching and composition algorithms. In this study we have experimented with 127 users to define and evaluate a model for service similarity in the context of semantic Web services. Our findings show that humans take a complex and sophisticated approach towards service similarity, which is more fine-grained than suggested by theoretical models of service similarity, such as logic-based approaches. We define a similarity model, based on our empirical findings and prove that the similarity model, expressed by a distance metric, is complete and that it closely predicts humans' perceptions of service similarity. Finally, we describe an application of a Web service search engine that implements our model.

Humans, semantic services and similarity: A user study of semantic Web services matching and composition

Abstract Inferring similarity between Web services is a fundamental construct for service matching and composition. However, there is little evidence of how humans perceive similarity between services, a crucial knowledge for designing usable and practical service matching and composition algorithms. In this study we have experimented with 127 users to define and evaluate a model for service similarity in the context of semantic Web services. Our findings show that humans take a complex and sophisticated approach towards service similarity, which is more fine-grained than suggested by theoretical models of service similarity, such as logic-based approaches. We define a similarity model, based on our empirical findings and prove that the similarity model, expressed by a distance metric, is complete and that it closely predicts humans’ perceptions of service similarity. Finally, we describe an application of a Web service search engine that implements our model.