Classification of Web Services Using Tensor Space Model and Rough Ensemble Classifier

In software technology, over the diversified environment, services can be rendered using an innovative mechanism of a novel paradigm called web services. In a business environment, rapid changes and requirements from various customers can be adapted using this service. For service management and discovery, the classification of Web services having the same functions is an efficient technique. However, there will be short lengthened Web services functional description documents, having less information, and sparse features. This makes difficulties in modelling short text in various topic models and leads to make an effect in the classification of Web services. A Mixed Wide and PSO-Bi-LSTM-CNN model (MW-PSO-Bi-LSTM-CNN) is proposed in this work for solving this issue. In this technique, the Web service category‟s breadth prediction is performed by combining Web services description document‟s discrete features, which exploits the wide learning model. In the next stage, the PSO-Bi-LSTM-CNN model is used for mining Web services description document word‟s context information and word order, for performing the Web service category‟s depth prediction. Here, particle swarm optimization (PSO) is integrated with the Bi-LSTM-CNN network for computing various hyper-parameters in an automatic manner. In third stage, Web service categories, results of depth, and breadth prediction are integrated using a linear regression model as final service classification result. At last, MW-PSO-Bi-LSTM-CNN, Wide&Bi-LSTM, and Wide&Deep web service classification techniques are compared and a better result with respect to web service classification accuracy is obtained using the proposed technique as shown in experimental results.

Co-attentive representation learning for web services classification

Service oriented architecture is a glue that allows web applications to work in collaboration. It has become a driving force for the service-oriented computing (SOC) paradigm. In heterogeneous environments the SOC paradigm uses web services as the basic building block to support low costs as well as easy and rapid composition of distributed applications. A web service exposes its interfaces using the Web Service Description Language (WSDL). A central repository called universal description, discovery and integration (UDDI) is used by service providers to publish and register their web services. UDDI registries are used by web service consumers to locate the web services they require and metadata associated with them. Manually analyzing WSDL documents is the best approach, but also most expensive. Work has been done on employing various approaches to automate the classification of web services. However, previous research has focused on using a single technique for classification. This research paper focuses on the classification of web services using a majority vote based classifier ensemble technique. The ensemble model overcomes the limitations of conventional techniques by employing the ensemble of three heterogeneous classifiers: Naive Bayes, decision tree (J48), and Support Vector Machines. We applied tenfold cross-validation to test the efficiency of the model on a publicly available dataset consisting of 3738 real world web services categorized into 5 fields, which yielded an average accuracy of 92 %. The high accuracy is owed to two main factors, i.e., enhanced pre-processing with focused feature selection, and majority based ensemble classification.

The number of web services on the Internet is increasing continuously. Determining a web service’s domain is very helpful for many issues like web service discovery, composition and semantic annotation. Based on predefined domains, we implemented semi-automatic web service classification with supervised machine learning technique in the paper. First we exploited the process of web service classification with choosing element from web service description document, preprocessing data, mapping to a digital vector and applying classifier. Then service classification accuracy of single element and multi-element based on four machine learning algorithms were tested. At last, experimental results showed that C4.5 classifier had a higher accuracy and efficiency in web service classification. In addition, multi-element classification did not perform better than single element.

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.

Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data sparsity. However, beside functional description documents, the Web API ecosystem has accumulated a wealth of information that can be used to improve the accuracy of Web service (API) classification. At the moment, there is an absence of a unified way to combine functional description documents with other sources of information (e.g., attributes, interactions and external knowledge) accumulated in the Web API ecosystem for API classification. To address this issue, we present a dual-GCN framework that can effectively suppress the noise propagation of textual contents by distinguishing functional description documents and other sources of information (specifically Mashup-API co-invocation patterns by default in this paper) for API classification. This framework is extensible with the ability to include different sources of information accumulated in the Web API ecosystem. Comprehensive experiments on a real-world public dataset demonstrate that our proposed method can outperform various representative methods for API classification.

We propose in this paper a rough set based classification technique for real world Web services. The set of Web service quality attributes is reduced to a core set, which represents the most releva...

This paper puts forward a general middleware framework of the Internet of Things (IOT). This framework has distributed, loosely coupled, scalable features. To solve the problems of machine processing of messages of IOT, the concept of ontology is introduced. The proposed algorithm can quickly classification and annotation of user demand, web services and resources data. In addition, the algorithm has good scalability. It can be applied to different fields only what people need to replace the domain ontology.

As web grows at an increasing speed, hypertext classification is becoming a necessity. While the literature on text categorization is quite mature, the issue of utilizing hypertext structure and hyperlinks has been relatively unexplored. In this paper, we introduce a novel split and merge technique for classification of hypertext documents. The splitting process is performed at the feature level by representing the hypertext features in a tensor space model. We exploit the local-structure and neighborhood recommendation encapsulated in the this representation model. The merging process is performed on multiple classifications obtained from split representation. A meta level decision system is formed by obtaining predictions of base level classifiers trained on different components of the tensor and actual category of the hypertext document. These individual predictions for each component of the tensor are subsequently combined to a final prediction using rough set based ensemble classifiers. Experimental results of classification obtained by using our method is marginally better than other existing hypertext classification techniques.

Service-oriented computing (SOC) enables organizations and individual users to discover openly-accessible capabilities realized as services over the Internet. An important issue is the management of the messages that flow into and out of these services to ultimately compose higher-level functions. A significant problem occurs when service providers loosely define these messages resulting into many services that in effect cannot be easily integrated. State of the art research explores semantic methods for dealing with this notion of data integration. The assumption is that service providers will define messages in an unpredictable manner. In our work, we investigate the nature of message definitions by analyzing real, fully-operational web services currently available on the Internet (i.e. from the wild). As a result, we have discovered insights into how real web services messages are defined as affected by the tendencies of the web services developers. Using these insights we propose an enhanced syntactical method that can facilitate semantic processing by classifying web services by their message names as a first step.

A web service is a software system that supports interoperable machine to machine interaction over the network. It can be any kind of service provided on the web that can exchange data between applications. In simple terms, it is any program that is callable by another program across the web using standard protocols. Web services are special because they are independent of the platform, programming language, and model design. In current web applications, more businesses are gradually publishing their business as services over the web. This growing number of web services available within an organization and on the Web raises a new and challenging search problem: locating desired web services. Searching for web services with conventional web search engines is insufficient in this context. In this paper, a latent semantic web service discovery and composition framework, addressing both discovery and composition of web services, is introduced. The framework provides a graphical visualization interface that can facilitate users in finding the desired web service beyond the general keyword search and provide composition of complex services if the service does not exist. A novel approach based on an information retrieval technique known as latent semantic analysis is applied to a large web service descriptions data set collection. By using co-occurrence patterns across the entire data collection, and then using those patterns to infer semantic relationships between documents, relevant results in a ranked order can be retrieved. Moreover, a back to front algorithm is applied to the composition search interface in order to compose complex web services in full or partial terms from pre-existing web services when the requested service does not exist. An experimental study conducted on a collection of 2525 publicity available web service shows improved performance to the technique applied in obtaining much better search results.

Technology of Web Service Classification can be widely used in the area of service applications. With the rapid growth of services, service classification turn to work in a poor performance. In this paper, we propose an efficient algorithm based on Locality Sensitive Hashing (LSH) to solve the service classification problem. This method is capable of high dimensional service classification as well. It is introduced that how to convert WSDL to vectors based on VSM. Then services are classified using LSH. We evaluate our approach and KNN method through experiment on different datasets. Two standards precision and recall are adopted to check the quality of service classification. The experiments have shown that our algorithm has a high performance and works stably.

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

In this article we present best practices in implementing a geographic information system that may be used as a foundation for commercial geographic web services. On one hand we explain important rules to be followed during the design of the information system itself, e.g. the data supply process. On the other hand we introduce different categories for potential geographic web services and analyze these categories according to several properties. From some of those properties rules can be extracted on how well every kind of service is suited for commercial use and which technical context (e.g. security) has to be observed to be able to offer it. The whole article is based on a case study and prototype where we implemented a geographic database and geographic web services from each of the different categories in the domain of hydrology.