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 servicesare 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 datasetand found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conductedexperiments 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, extremegradient 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 of98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes

A new framework for matching semantic web service descriptions based on OWL-S services

Nowadays, semantic web services are published and updated with growing demand for cloud computing. Since a single service is not capable of processing the increase of data and user's demand the improvement is necessary to match and rank semantic web service to achieve the user's goal. In the semantic web service framework, users' request is the input to the system and output is ranking of semantic web service. It has become a limitation to match between requests with the semantic web service description. This paper proposes a new framework for matching and ranking semantic web service based on OWL-S. The proposed new framework can match the keyword in each task and ranking service. This framework is done by using performance ontology-based indexing. The result is obtained and the performance of the services for multiple requests has been measured

INVESTIGATION OF THE ROLE OF SERVICE LEVEL AGREEMENTS IN WEB SERVICE QUALITY

Context/Background: Use of Service Level Agreements (SLAs) is crucial to provide the value added services to consumers to achieve their requirements successfully. SLAs also ensure the expected Quality of Service to consumers. Aim: This study investigates how efficient structural representation and management of SLAs can help to ensure the Quality of Service (QoS) in Web services during Web service composition. Method: Existing specifications and structures for SLAs for Web services do not fully formalize and provide support for different automatic and dynamic behavioral aspects needed for QoS calculation. This study addresses the issues on how to formalize and document the structures of SLAs for better service utilization and improved QoS results. The Service Oriented Architecture (SOA) is extended in this study with addition of an SLAAgent, which helps to automate the QoS calculation using Fuzzy Inference Systems, service discovery, service selection, SLA monitoring and management during service composition with the help of structured SLA documents. Results: The proposed framework improves the ways of how to structure, manage and monitor SLAs during Web service composition to achieve the better Quality of Service effectively and efficiently. Conclusions: To deal with different types of computational requirements the automation of SLAs is a challenge during Web service composition. This study shows the significance of the SLAs for better QoS during composition of services in SOA

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Un environnement de spécification et de découverte pour la réutilisation des composants logiciels dans le développement des logiciels distribués

Notre travail vise à élaborer une solution efficace pour la découverte et la réutilisation des composants logiciels dans les environnements de développement existants et couramment utilisés. Nous proposons une ontologie pour décrire et découvrir des composants logiciels élémentaires. La description couvre à la fois les propriétés fonctionnelles et les propriétés non fonctionnelles des composants logiciels exprimées comme des paramètres de QoS. Notre processus de recherche est basé sur la fonction qui calcule la distance sémantique entre la signature d'un composant et la signature d'une requête donnée, réalisant ainsi une comparaison judicieuse. Nous employons également la notion de " subsumption " pour comparer l'entrée-sortie de la requête et des composants. Après sélection des composants adéquats, les propriétés non fonctionnelles sont employées comme un facteur distinctif pour raffiner le résultat de publication des composants résultats. Nous proposons une approche de découverte des composants composite si aucun composant élémentaire n'est trouvé, cette approche basée sur l'ontologie commune. Pour intégrer le composant résultat dans le projet en cours de développement, nous avons développé l'ontologie d'intégration et les deux services " input/output convertor " et " output Matching ".Our work aims to develop an effective solution for the discovery and the reuse of software components in existing and commonly used development environments. We propose an ontology for describing and discovering atomic software components. The description covers both the functional and non functional properties which are expressed as QoS parameters. Our search process is based on the function that calculates the semantic distance between the component interface signature and the signature of a given query, thus achieving an appropriate comparison. We also use the notion of "subsumption" to compare the input/output of the query and the components input/output. After selecting the appropriate components, the non-functional properties are used to refine the search result. We propose an approach for discovering composite components if any atomic component is found, this approach based on the shared ontology. To integrate the component results in the project under development, we developed the ontology integration and two services " input/output convertor " and " output Matching "

Detection of Spammer Based On the User Recommendation Report in Web Mining

ABSTRACT: Online video sharing systems, out of that YouTube is that the most well-liked, offer options that permit users to post a video as a response to a discussion topic. These options open opportunities for users to introduce impure content, or just pollution, into the system. Therefore we discover for example, spammers could post associate unrelated video as response to a well-liked one, aiming at increasing the chance of the response being viewed by a bigger range of users. We have a tendency to propose the users Video Recommendation (UVR) system in cloud computing atmosphere. Video attributes capture specific properties of the videos uploaded by the supplier We employing a novel rule to as ALAC (active lazy associative classifier).Content pollution could jeopardize the trust of users on the system we offer a characterization of content, individual, and social attributes that facilitate distinguish every user category. Classification approach succeeds at separating spammers and promoters video search systems is fooled by malicious attacks that depends on a good selective sampling strategy to traumatize the foremost favorite Videos. This work provides a high flexibility, high reliability, low-level transparency, security features. Proposed tag cloud recommendation approaches

AI-driven Service Broker for Simple and Composite Cloud SaaS Selection

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud Software as a Service (SaaS) is one of the three types of services offered in cloud computing. Cloud SaaS is a software application that runs on top of Platform as a Service (PaaS), which in turn works on top of Infrastructure as a Service (IaaS). Due to the numerous advantages offered by cloud SaaS to service consumers, such as reducing the cost of IT expenditures, security capabilities and disaster recovery offered by cloud SaaS service providers, Cloud SaaS is becoming a leading and growing type of cloud service among other cloud services (i.e., IaaS and PaaS). Therefore, Cloud SaaS service consumers may face a difficult task when searching for the most suitable service based on their preferences. Service selection is based on matching the service requirements of functional and non-functional quality attributes. However, selecting a Cloud SaaS service provider with a high number of non-functional quality attributes that fulfils consumer requirements within a large number of similar functional services is a key factor for a Cloud SaaS service selection. In addition, considering that a cloud SaaS service can involve a long-term contract, Cloud SaaS providers frequently offer a free trial period to test and evaluate services before the consumers make the decision of whether they will use that service. Furthermore, selecting multiple Cloud SaaS service providers in order to create a new business value, known as a service composition in the service-oriented architecture (SOA) model, is very important, since Cloud SaaS services are the first option for deploying IT services for many new enterprises. Therefore, this research aims to propose intelligent methods for a simple and composite service selection framework based on consumer preferences. By simple, we mean a singular service whereas by composite, we mean an aggregated service. This work seeks to find the services with a high number of non-functional quality attributes that meet the consumer requirements. To achieve the objectives of this research, a design science research methodology will be adopted. Fuzzy logic will be proposed to address the uncertainty of consumer preferences. A ranking service system, evaluation system and composite decision maker system are proposed in this thesis to help a Cloud SaaS service consumer select the optimal service required. Multiple approaches of decision-makers will be developed in order to achieve our research objectives. It is expected that this research work will enhance the selection mechanism of Cloud SaaS, either simple or composite based on service consumer’s preferences