Large-Scale Longitudinal Analysis of SOAP-Based and RESTful Web Services

Users of Web and grid services often must temporarily delegate some or all of their rights to a software entity to perform actions on their behalf. The problem with the typical grid services approach (X. 509 proxy certificates) is that commercial Web services tooling fails to recognize these certificates or process them properly. The security assertion markup language (SAML) is a standardized XML-based framework for exchanging authentication, authorization and attribute information. SAML has broadening commercial support but lacks delegation capabilities. To address this shortcoming, we exploit SAML's inherent extensibility to create a delegation framework for Web and grid services that supports both direct and indirect delegation. We develop a set of verification rules for delegation tokens that rely on WS-Security X.509 signatures, but do not force any trust relationship between the delegatee and the target service. We have implemented the framework on two common Web service hosting environments: Java/Tomcat and .NET. By leveraging existing Web services standards, we make it easier for Grid practitioners to build and consume Web and grid services without resorting to grid-specific protocols.

Web services are interoperable and reusable software components that can be dynamically discovered and integrated over the Internet. Developed on open standards, Web services have become a promising solution to inter- and intra-organization application integration. The supply chain of Web services exhibits two distinct features that are not considered in previous literature on information and physical-good supply chain: the integration of multiple Web services and the cross-network externality effect between Web service vendors and users. In a quest to fill in the research gap, this paper studies the optimal pricing strategies of a monopolistic intermediary in the supply chain of complementary Web services. The Web service intermediary (WSI) provides both technical and aggregation services, and seeks to charge optimal subscription and listing fees. Analytical results show that in a supply chain of complementary Web services exhibiting cross-network effects, the optimal strategy for the WSI is to set the listing fee such that all service providers list on it. On the other hand, the optimal subscription fee depends on the intensity of the cross-network effect, consumers' valuation of value-added services, and the characteristics of the Web services under consideration.

Several researchers argue that age related cognitive impairments have detrimental affect on use of web services by older adults. However little and systematic applied research has been conducted on how age related cognitive impairments might affect the usage of web services by older adults. Undoubtedly, understanding the relationship between the cognitive changes that accompany aging and their impact on older adults' usage of web services will be beneficial for designing web services for this group. The paper demonstrates how such understanding has been employed to develop an assistive technology in order to improve older adults' interaction with online forms (e.g. state benefit application form). However the paper acknowledges that this new assistive technology does not guarantee that people with age related cognitive impairments accept it, as diffusion of innovation research shows that getting a new technology adopted even when it has noticeable advantage is often very difficult. Consequently the paper identifies critical factors that need to be considered when adopting this new assistive technology, drawing on Rogers (2003) theory of Diffusion of Innovations.

This paper addresses the issue of selecting Web services residing in a community. Since these Web services have similar functionalities, this selection depends on their Quality of Service (QoS). Existing approaches only consider the satisfaction of users' requirements and neglect the satisfaction of Web services' requirements and the community to which they belong. This paper proposes an approach of selecting Web services based on the satisfaction of all three parties - user, Web service, and community. The approach consists of first, formalizing the selection process and then, using integer programming to define a score function, which can be maximized to find the best selection based on three satisfaction factors. Experiments using real Web services and measurements are conducted to demonstrate the influences of the approach.

With the development of Service-Oriented technologies, the amount of Web services grows rapidly. QoS-Aware Web service recommendation can help service users to design more efficient service-oriented systems. However, existing methods assume the QoS information for service users are all known and accurate, but in real case, there are always many missing QoS values in history records, which increase the difficulty of the missing QoS value prediction. By considering the user-service-time three dimension context information, we study a Temporal QoS-Aware Web Service Recommendation Framework which aims to recommend best candidates to service user's requirements and meanwhile improve the QoS prediction accuracy. We propose to envision such QoS value data as a tensor which is known as multi-way array, and formalize this problem as a tensor factorization model and propose a Tucker decomposition (TD)algorithm which is able to deal with the triadic relations of user-service-time model. However, one major challenge is that how to deal with the dynamic incoming service QoS value data streams. Thus, we introduce the incremental tensor factorization (ITF)method which is (a) scalable, (b) space efficient (it does not need to store the past data). Extensive experiments are conductedbasedc on our real-world QoS dataset collected on Planet-Lab, comprised of service invocation response-time values from 408 users on 5,473 Web services at 240 time periods. Comprehensive empirical studies demonstrate that our approach is faster and more accuracy than other approaches.

Web Services technologies have over a period of time replaced expensive EDI links and have come to stay as the backbone for collaboration between various enterprises. While currently majority of Web Services are available free, over a period of time increased business dependencies of customers on Web Services are resulting in demands for better Quality of Service. Investments in newer technologies to improve Quality of Service results in increasing Cost of Service, which needs to be offset by revenues. While service providers seek predictability in revenues, customers and users of the Web Services seek flexibility in pricing by not being charged for services not used and service features that are not provided. QoS thus becomes a key determinant of pricing in Web Services. This paper attempts to define a framework for pricing Web Services based on the QoS features provided, the underlying Cost of Service and the level of predictability of revenues that accrue to the service provider based on the transaction volumes of various services. The proposed framework suggests a Subscription-based Pricing for Commoditized Web Services, Transaction-based Pricing for Channelized Web Services and Risk-Based Pricing for Customized Web Services.

Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. To tackle this challenge, in recent literature, a number of QoS prediction methods are proposed, but they still demonstrate disadvantages on prediction accuracy. In this paper, we design a location-based hierarchical matrix factorization (HMF) method to perform personalized QoS prediction, whereby effective service recommendation can be made. We cluster users and services into several user-service groups based on their location information, each of which contains a small set of users and services. To better characterize the QoS data, our HMF model is trained in a hierarchical way by using the global QoS matrix as well as several location-based local QoS matrices generated from user-service clusters. Then the missing QoS values can be predicted by compactly combining the results from local matrix factorization and global matrix factorization. Comprehensive experiments are conducted on a real-world Web service QoS dataset with 1,974,675 real Web service invocation records. The experimental results show that our HMF method achieves higher prediction accuracy than the state-of-the-art methods.

Now a days, Web services are becoming the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single web service, so QoS information is an important indicator for service selection. But in reality, QoSs of some Web Services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This study first proposes a novel method for clustering similar web services using semantic approach in order to overcome the limitations of pattern-matching approach and finally proposes a cluster based approach using Slope One Collaborative filtering method to predict the QoS values for similar web services for similar users. Proposed approaches are applied on a dataset consisting of WSDL files and QoS values for various QoS parameters which are collected from the Internet and the proposed approaches shows better qualities with respect to clusters formed and the QoS values predicted.

Web service is an emerging technology in this decade for its openness and loosely coupled interaction among systems. Due to the proliferation of web services, more number of web services is available for similar functionality. QoS deals about the non-functional information of a Web services. Furthermore, QoS plays a vital role for differentiating web services which has similar functionality. Predicting the QoS value of a web service is an important process for web service discovery and selection. Due to the different network environment includes bandwidth, capacity and delay, the QoS value of a web service is differs from user to user. Therefore, QoS prediction is imperative for a recommendation system to provide best services based on QoS values. To achieve this, an enhanced collaborative filtering approach is introduced. In this approach, the PCC (Pearson Correlation Coefficient) is calculated for identifying the similar user and items. To increase the prediction accuracy, the user’s personalized degree of similarity is calculated and the Top-k users and items are identified. The confidence value computation helps to predict the missing values in the user-item matrix. By systematic integration of user-based and item-based methods, the final QoS values of a web services is predicted. The system has been tested with the invocation of real world web services, and the efficiency of the proposed approach is compared with the existing approaches. The MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) between the proposed and existing approaches shows the accurate prediction of the proposed approach.

With the increasing use of web and Service Oriented Systems, web-services have become a widely adopted technology. Web services repositories are growing fast, creating the need for advanced tools for organizing and indexing them. Clustering web services, usually represented by Web Service Description Language (WSDL) documents, enables the web service search engines and users to organize and process large web service repositories in groups with similar functionality and characteristics. In this paper, we propose a novel technique of clustering WSDL documents. The proposed method considers web services as categorical data and each service is described by a set of values extracted from the content and structure of its description file and as quality measure of clustering is defined the mutual information of the clusters and their values. We describe the way to represent web services as categorical data and how to cluster them by using LIMBO algorithm, minimizing at the same time the information loss in features values. In experimental evaluation, our approach outperforms in terms of F-Measure the approaches which use alternative similarity measures and methods for clustering WSDL documents.

Web Services are considered as the predominant aspect of the world wide web which renders online services to its clients based on the user requested functionality. Due to its popularity for more than two decades, the count of functionally similar web services are growing exponentially which makes the process of selecting the web services more complex. The research community have proposed multiple solutions for solving this issue using varied techniques like Bio Inspired Algorithms, Multi Criteria Decision Making, Optimization techniques and others. This work focuses on Multi Criteria Decision Making methods to solve the real world Web Service Selection issue. The current research trend in the area of applying Multi Criteria Decision Making for Web Service Selection is found to be in discordance with the real world data. Hence, this work aims analyzing the prevalent real world issues related to Web Service Selection and proposes a feasible solution to solve it. An original framework that incorporates the power of QoS and QoE values with GPU acceleration for selection of web services is proposed and it is found that the proposed framework is highly time efficient when compared with the literature works. Also, the correlation of the ranking scheme given by the proposed framework with the original ranking is higher which is a clear indication that the proposed framework is more suitable for real time Web Service Selection process.

Web services are one of the most important resources on the Internet. Faced with the increasing numbers of Web services and service users, researchers in the services computing field have attempted to address a challenging issue, i.e., how to quickly find the suitable ones according to user queries. Many previous studies have been reported towards this direction. In this work we proposing a new web service discovery approach which is consist of semantics based mining. This approach helps to mine underlying semantics and user to employ web services. First we creating web database which contains dataset which is extracted from well-known registry sites. Clustering is performed to cluster the data which contains similarity i.e. synonyms, abbreviations and disordered fragments using k-means clustering. using clusters, similarity index libraries are created for semantic mining. Semantic mining is performed before receiving request from the user. Finally web service discovery approach is designed which accepts the request from the users and produces related services based on semantic mining. In this we performing data redundancy to avoid time complexity and it lead to faster response time. The experimental results will show better precision and recall rate than existing approaches.

The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS prediction framework, called WSPred, to provide time-aware personalized QoS value prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.

ABSTRACTThere is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user’s mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.

International audience