Number Of Web Services Research Articles

Semantic Web Services Discovery

This article describes how Web services play an important role in several fields such as e-commerce and e-health. As the number of Web services is increasing rapidly, finding the best Web service according to users' requirements becomes more challenging. The traditional method of Web service discovery is based on keyword match. Due to this, many Web services which are most relevant to the user request are left undiscoverable. Some other emergent approaches are based on semantics to improve the quality of the discovered Web services in terms of relevance and satisfaction of user's need. In this paper, the authors present a survey of existing semantic Web services discovery approaches giving priority to relevant ones. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to be addressed to substantially enhance the semantic Web service discovery.

Ranking Egyptian Universities Using Fuzzy Logic

During the last quarter of a century, university ranking systems have been developed as an outcome of new academic fields and metrics. Although they have attracted wide attention from policy makers, students, academicians, authorities and the media, they have been subjected to criticism. Any university that does not change as with the environment around them is doomed to fail. The results of universities in international rankings are a disturbing and foreboding danger. Universities are seeking to improve its services, students, citizens and visitors, to achieve better results in the international rankings. The objective of this study is spread public awareness of the importance of ranking of universities and higher education institutions, and push these institutions towards excellence and competition in the quality of scientific research and academic performance, and spreading the culture of the orientation about the universities rankings and higher education institutions in Egypt. By using web service we have collected data on Egyptian Universities of interest from a more number of web Services for fifteen Indicator each Egyptian University, We have constructed the weight matrix for the Indicator, Where an algorithm was created to Ranking the Egyptian universities using the membership function (trapezoidal ) in fuzzy logic Has been applied the fuzzy logic algorithm on the that data, we have compared the results with some International rankings for example (QS,U.S Nwes, Webmatix).This study showed positive results about comparing the results we get with some of the global rankings, the study confirmed that the Egyptian universities have high capabilities in the field of scientific research.

QoS-Aware Rule-Based Traffic-Efficient Multiobjective Service Selection in Big Data Space

The number of Web services has increased dramatically during the last few years. This has resulted in an increase in the volume of candidate services for tasks in composition systems. This has led to growth in the variety of nonfunctional properties in service selection, resulting in uncertainty (veracity issues) among such properties, which has severely affected the NP-hard aspects of service selection. Despite this, consumers in many areas would like access to a variety of selection methods such as linear programming and dynamic programming techniques. An additional problem is that the composition length (the number of tasks) of the workflow has increased, with the incorporation of research domains such as data science. These trending composition issues are challenging the computational power of existing methods. Such concerns have opened the door to research involving Big Data space. We propose a flexible, distributed selection algorithm that facilitates heterogeneous-selection methods to satisfy multiobjective composition requirements rather than rigid, specific composition requirements. However, service-selection processes in a Big Data space will inevitably increase traffic congestion caused by the increased volume of internal communication, particularly external traffic, such as Zipf and Pareto phenomena, and internal traffic during shuffling. To address these concerns, we propose solutions for each case. Our experiments demonstrate that the proposed traffic-efficient multiobjective method is well behaved when selecting services in Big Data space.

Refined cloud services discovery based on user model including privacy-preserving access control

Cloud applications are increasingly available, and the cloud must provide a multitude of services to cover the requirements of diverse users. Likewise, the number of web services has increased, and users may find themselves lost in the array of complex services. Consequently, it has become difficult for users to find the best services to meet their needs. In this context, cloud service discovery is an important challenge that has received increasing attention. In the cloud environment, services are openly accessible, which can lead to potential risks. Security in cloud services thus is necessary and can be identified through many attributes. In this study, we examine privacy attributes, which at present are the most bargain property in the cloud environment, and present a few mechanisms, including access control.With a specific goal to encourage cloud investigation by the user, we propose a refined cloud services discovery tool based on a user model that includes privacy-preserving access control. This user model is a combination of user context and security context and is designed with the aim of disentangling the demonstrating task.

A compressed graph representation for services composition

Service-oriented computing enhances business scalability and flexibility. Providers who expect to benefit from it may bring the explosive growth of web services. As a result, the huge storage required in searching a composition solution may be too much to afford. In this paper, we apply a compressed data structure to tackle this challenging issue. We propose a system model to solves web service composition (WSC) problem. In this model, we use a compressed tree to represent the search graph. This work innovatively solve the composition problem with compressed graph representation. The proposed model is verified through theoretical analysis and experimental study. The obtained experimental results show that this system may handle a large number of web services with small space requirement.

Extracting relevant terms from mashup descriptions for service recommendation

Due to the exploding growth in the number of web services, mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort. Service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates. However, the majority of existing methods utilize service profiles for content matching, not mashup descriptions. This makes them suffer from vocabulary gap and cold-start problem when recommending components for new mashups. In this paper, we propose a two-step approach to generate high-quality service representation from mashup descriptions. The first step employs a linear discriminant function to assign each term with a component service such that a coarse-grained service representation can be derived. In the second step, a novel probabilistic topic model is proposed to extract relevant terms from coarse-grained service representation. Finally, a score function is designed based on the final high-quality representation to determine recommendations. Experiments on a data set from ProgrammableWeb.com show that the proposed model significantly outperforms state-of-the-art methods.

Engineering Polynomial-Time Solutions for Automatic Web Service Composition

Web Service Composition (WSC) is the task of creating some new functionality over a repository of independent resources, Web Services in particular. Services are described by their input and output parameters that are matched if they have the same textual name in service definition for the classic, simplified version of WSC. The problem requires finding an ordered list of services such that all input parameters are available starting from the initially user known parameters and revealing all user required parameters. In this paper we propose a proven efficient polynomial-time solution to Automatic WSC combined with a heuristic for shortening the solution length: the number of Web Services in the composition. The algorithm is tested against several benchmarks of tests and is compared with previous solutions that use AI Planning, revealing tremendous improvements. Two benchmarks are well-known in WSC literature but due to lack of high run time variations over their tests, a new benchmark is created with a special designed generator described in the paper. The new tests reveal more meaningful information.

Skyline service selection approach based on QoS prediction

The internet currently hosts a large number of web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.

Ontology-based web services classification for registration and discovery of web services

Web services become increasingly important in the world of internet. Thus, users need to access and effectively share web services. The centralised UDDI is a less robust structure and does not support a large number of web services. If the distributed environment is introduced into the UDDI, there will be a rapid increase in services with minimisation of resource consumption. Accordingly, mechanisms are needed for efficient selection of the appropriate instance of the web service in terms of factors like quality and performance. Distribution mechanisms, clustering and classification are necessary for efficient selection of the appropriate web service to the customer needs. In this paper, we propose a shared repository (ontology) which is produced by clustering of web services. This ontology can be exploited by the UDDI for publishing and discovering web services. A matching algorithm is also proposed to show how to perform this operation. A prototype tool has been developed to support experiments that have been carried out to evaluate and show the efficiency of our matching algorithm. We also introduce a comparative analysis to show the effectiveness of our approach compared to existing works.

A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

In order to develop a Supercomputing Cloud Platform (SCP) prototype system using Service-Oriented Architecture (SOA) and Petri nets, we researched some technologies for Web service composition. Specifically, in this paper, we propose a reliability calculation method for Web service compositions, which uses Fuzzy Reasoning Colored Petri Net (FRCPN) to verify the Web service compositions. We put forward a definition of semantic threshold similarity for Web services and a formal definition of FRCPN. We analyzed five kinds of production rules in FRCPN, and applied our method to the SCP prototype. We obtained the reliability value of the end Web service as an indicator of the overall reliability of the FRCPN. The method can test the activity of FRCPN. Experimental results show that the reliability of the Web service composition has a correlation with the number of Web services and the range of reliability transition values.

Weighted Quality of Service based Ranking of Web Services

Background/Objectives: The primary objective of the study is to evaluate the performance of Composite Web Service based on the parameters relating to Quality of Service by using statistical methodologies. Methods/Statistical Analysis: By using Web Service Crawler Engine we have collected data on eleven parameters of interest from a more number of Web Services. Among the eleven parameters we have selected six parameters of importance using the sampling of thirty Web Services. We have constructed the weight matrix for the parameters. By using Web Services Relevancy Function ranking has been done for three different scenarios. Using Spearman's Rank Correlation Coefficient we have compared the scenarios. Findings: The graphical representation of the three scenarios shows varying pattern. The Spearman's Rank Correlation coefficient of the Web Services Relevancy Function varied widely among the sampled Web Services. F-Test revealed that there are no significant differences in respect of weights of Quality of Web Services for three different scenarios. We concluded that weight assigned for Quality of Service parameters in Scenario I is preferred than the scenarios II and III through the evaluations. Application/Improvements: The technique developed in this research paper can be applied for the comparison of Web Services during discovery to enhance the performance of the Composite Web Service.

A Multilevel Index Model to Expedite Web Service Discovery and Composition in Large-Scale Service Repositories

The number of web services has grown drastically. Then how to manage them efficiently in a service repository is an important issue to address. Given a special field, there often exists an efficient data structure for a class of objects, e.g., the Google’ Bigtable is very suitable for webpages’ storage and management. Based on the theory of the equivalence relations and quotient sets, this work proposes a multilevel index model for large-scale service repositories, which can be used to reduce the execution time of service discovery and composition. Its novel use of keys as inspired by the key in relational database can effectively remove the redundancy of the commonly-used inverted index. Its four function-based operations are for the first time proposed to manage and maintain services in a repository. The experiments validate that the proposed model is more efficient than the existing structures, i.e., sequential and inverted index ones.

SLUM: Service Layered Utility Maximization Model to Guide Dynamic Composite Webservice Selection in Virtual Organizations

Dynamic webservice composition is a promising ICT support service for virtual organizations. However, dynamic webservice composition remains a nondeterministic polynomial (NP) hard problem despite more than 10 years of extensive research, making the applicability of the technique to problems of industrial relevance limited. In [48], we proposed a layered method, SLUM, to combat the problem. Analytically, SLUM overcomes the relative weaknesses of two widely used approaches in the literature – the local planning (hereafter L-MIP) strategy and the Mixed Integer Programming (S-MIP) method. Despite the promising benefits of SLUM, it's unknown to what extent and under what circumstances SLUM is better or worse than L-MIP algorithms and S-MIP. The research objective of the study was to investigate the relative performance of SLUM w.r.t S-MIP and L-MIP using two performance criteria: - solution quality and CPU running time. Several randomly generated two task workflows of monotonically increasing hardness in the number of webservices per task were used to benchmark SLUM against the other two algorithms. A set of numerical and statistical techniques were used to experimentally compare the solution quality and the running time growth of SLUM against L-MIP and S-MIP. We determined that SLUM generates solutions with an average quality of 93% w.r.t the global optimum. Further, we show that SLUM yields solutions that are 5% more quality than L-MIP. On the other hand, we established that L-MIP outperforms both S-MIP and SLUM by multiple factors in terms of computational efficiency. However, we find that for problem instances with less than 22 webservices per task, S-MIP is about 1.3 times faster than SLUM. Beyond n=22, the running time of SLUM teB, expressed in terms of the running time of S-MIP teA, is given by teB= teA0.78. We also establish that SLUM is asymptotically 3.6 times faster than S-MIP on average. We conclude that in order for a virtual enterprise broker to obtain maximum benefit from dynamic service composition, the broker should combine the three techniques in the following manner- (1) for service request without global constraints requirements, L-MIP is the most suitable method to use, (2) Where there is need for global constraints and the number of service providers per task is less than 22, S-MIP is most preferred and (3) in scenarios the number of service providers per task is more than 22 and there is a need to satisfy global constraints, SLUM is superior to both S-MIP and L-MIP.

A Robust Service Selection Method Based on Uncertain QoS

Nowadays, the number of Web services on the Internet is quickly increasing. Meanwhile, different service providers offer numerous services with the similar functions. Quality of Service (QoS) has become an important factor used to select the most appropriate service for users. The most prominent QoS-based service selection models only take the certain attributes into account, which is an ideal assumption. In the real world, there are a large number of uncertain factors. In particular, at the runtime, QoS may become very poor or unacceptable. In order to solve the problem, a global service selection model based on uncertain QoS was proposed, including the corresponding normalization and aggregation functions, and then a robust optimization model adopted to transform the model. Experiment results show that the proposed method can effectively select services with high robustness and optimality.

Service Composition Optimization Using Differential Evolution and Opposition-based Learning

The numbers of web services are increasing rapidly over the last decades. One of the most interesting challenges in using web services is the usage of service composition that allows users to select and invoke composite services. In addition, the characteristic of each service is distinguished based on the quality of service (QoS). QoS is utilized in optimizing decisive factors such as cost or response time that is required by the user in the runtime system. Thus, QoS and service composition problem can be modeled as an optimization problem. In this study, differential evolution and opposition-based learning optimization methods have been proposed to obtain the optimal solution from candidate services. The results show that the proposed method converges faster than others. Therefore, the method is capable to select better composite services in short time.

Improving Response Time of Web Service Composition based on QoS Properties

Background: Nowadays, web services are one of the most widely used groups of SOA and service computing. The problem of QoS based selecting a web service dynamically and composing a set of web services to conduct a business task has been investigated in this paper. One of the main objectives of this paper is selecting web services based on non-functional properties and QoS score. Methods: In this paper is assumed that there are web services with similar functionality for each task and these web services have different non-functional properties and QoS parameters. To select a web service for eachtask SAW method is used, but this method don’t apply SAW method on all of web services. It use user requirements for ranking set of candidate web services and finally apply SAW method on set of candidate web services. Result: This method will help to select web services based on QoS score and user requirements. Select web services among many number of web service with this method can use to composition web service and finally will help to optimize response time of web service composition. Application: .NET Application with Visual Studio Environment and C# Programming Language.

A Hierarchical Multilayer Service Composition Model for Global Virtual Organizations

A major benefit of service composition is the ability to support agile global collaborative virtual organizations. However, being global in nature, collaborative virtual organizations can have several virtual industry clusters (VIC), where each VIC has hundreds to thousands of virtual enterprises that provide functionally similar services exposed as web services. These web services can be differentiated on a high dimensionality of quality of service attributes. The dilemma the virtual enterprise broker is faced with is how to dynamically select the best combination of component services to fulfill a complex consumer need within the shortest time possible. This composite service selection problem remains a Multi-Criteria Decision Making (MCDM) NP hard problem. Although existing MCDM methods based on local planning are linearly scalable for large problems, they lack capabilities to express critical intertask constraints that are practically relevant to service consumers. MCDM global planning methods on the other hand suffer exponential state space explosion making them severely limited for large problems of industrial relevance. This paper proposes HMSCM: Hierarchical Multi-Layer Service Composition Model. HMSCM is based on the theory of Layering as Optimization Decomposition (28-31). We view the service selection process as a layer network where each layer is a subproblem to be solved. The objective of one of the layers is to maximize a local utility function over a subset of web service QoS attributes from a service consumer perspective. The objective of the other layer is to maximize a local utility function over another subset of web service QoS attributes from the perspective of the Virtual enterprise broker. We develop the algorithm: Service Layered Utility Maximization (SLUM) that extends the Mixed Integer programming model in (9). We then formulate the problem at each layer in form of SLUM. Together, the two layers attempt to achieve the global optimization objective of the network. We show analytically how HMSCM overcomes the shortcomings of existing local planning and global planning service selection methods while retaining the strengths from each. i.e HMSCM is able to scale linearly with increasing number of QoS variables and number of web services while being able to enforce global intertask constraints.

A Practical Approach Towards Automatic Testing of Web Services Interoperability

Web Services are a technology designed to support the invocation of remote elements by client applications, with the goal of providing interoperable application-to-application interaction while assuring vendor and platform independence. In business-critical environments, the occurrence of interoperability issues can have disastrous consequences, including direct financial costs, reputation, and client fidelity losses. Despite this, experience shows that services interoperability is still quite difficult to achieve. The goal of this paper is to propose a practical testing process to understand the real level of interoperability provided by web services platforms. An extensible tool, that implements the proposed approach, has been used to run a large campaign during which we have tested the interoperability of a large number of web services, comprising both home-implemented and publicly available services, deployed on top of several web service platforms and against 11 client-side web service platforms. Numerous issues have been disclosed, showing the effectiveness of the proposed approach and the importance of having an automatic tool for testing web services for interoperability.

Semi-automatic Web Service Classification Using Machine Learning

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.

QoS-Based and Network-Aware Web Service Composition across Cloud Datacenters

With the development of cloud computing, more and more Web services are deployed on geo-distributed datacenters and are offered to cloud users all over the world. Through service composition technologies, these independent fine-grain services can be integrated to value-added coarse-grain services. During the composition, a number of Web services may provide the same function but differ in performance. In addition, the distribution of cloud datacenters presents a geographically dispersive manner, which elevates the impact of the network on the QoS of composite services. So it is important to select an optimal composition path in terms of QoS when many functionally equivalent services are available. To achieve this objective, we first present a graph model that takes both QoS of Web services and QoS of network into consideration. Then, a novel approach aiming at selecting the optimal composition path that fulfills the user’s end-to-end QoS requirements is provided. We evaluate our approach through simulation and compare our method with existing solutions. Results show that our approach significantly outperforms existing solutions in terms of optimality and scalability.