Invocation Of Web Services Research Articles

Proposal for an e-learning system model based on the invocation and semantic discovery of web services

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).

A Location-Based Factorization Machine Model for Web Service QoS Prediction

With the prevalence of web services, a large number of similar web services are provided by different providers. To select the optimal service among these service candidates, Quality of Service (QoS), representing the non-functional characteristics, plays an important role. To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. Collaborative web service QoS prediction is one of the most popular approaches. Based on the historical QoS data, collaborative QoS prediction methods employ memory-based collaborative filtering (CF), model-based CF, or their hybrids to predict QoS values. However, these methods usually only consider the QoS information of similar users and services, neglecting the correlation between them. To enhance the prediction accuracy, we propose a novel method to predict QoS values based on factorization machine, which leverages not only QoS information of users and services but also the user and service neighbor’s information. To evaluate our approach, we conduct experiments on a large-scale real-world dataset with 1,974,675 web service invocations. The experiment results show that our approach achieves higher prediction accuracy than other QoS prediction methods.

Towards Dynamic Reconfiguration of a Composite Web Service: An Approach Based on QoS Prediction

Service-oriented architecture provides the ability to combine several web services in order to fulfil a user-specific requirement. In dynamic environments, the appearance of several unforeseen events can destabilize the composite web service (CWS) and affect its quality. To deal with these issues, the composite web service must be dynamically reconfigured. Dynamic reconfiguration may be enhanced by avoiding the invocation of degraded web services by predicting QoS for the candidate web service. In this paper, we propose a dynamic reconfiguration method based on HMM (Hidden Markov Model) states to predict the imminent degradation in QoS and prevent the invocation of partner web services with degraded QoS values. PSO (Particle Swarm Optimization) and SFLA (Shuffled Frog Leaping Algorithm) are used to improve the prediction efficiency of HMM. Through extensive experiments on a real-world dataset, WS-Dream, the results demonstrate that the proposed approach can achieve better prediction accuracy. Moreover, we carried out a case study where we revealed that the proposed approach outperforms several state-of-the-art methods in terms of execution time.

A workflow language for research e-infrastructures

Research e-infrastructures are “systems of systems,” patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of “reproducibility of science” and “transparent assessment of science.” This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of “manually executable actions,” i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and “machine-executable actions,” i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to (i) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and (ii) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms.

CBPCS

With the development of cloud computing and the advent of the Web 2.0 era, composing a set of Web services as a service process is becoming a common practice to provide more functional services. However, a service process involves multiple service invocations over the network, which incurs a huge time cost and could become a bottleneck to performance. To accelerate its execution, we propose an engine-side cache-block-based service process caching strategy (CBPCS). It is based on, and derives its advantages from, three key ideas. First, the invocation of Web services embodies semantics, which enables the application of semantic-based caching. Second, cache blocks are identified from a service process, and each block is equipped with a separate cache so that the time overhead of service invocation and caching can be minimized. Third, a replacement strategy is introduced taking into account time and space factors to manage the space allocation for a process with multiple caches. The algorithms and methods used in CBPCS are introduced in detail. Moreover, how CBPCS can be applied to multiple service process models is also investigated. Finally, CBPCS is validated via comparison experiments, which shows the considerable improvements of CBPCS over other strategies.

Geographic-aware collaborative filtering for web service recommendation

The explosion of reusable Web services (e.g., open APIs, open data sources, and cloud/IoT services), has become a new opportunity for modern service-composition based applications development. However, this enormous growth of Web services increases the difficulty of selecting the best suitable Web services for a particular application. Hence, the design of an effective and efficient Web service recommendation, primarily based on user feedback, has become a challenge. In the mashup-API recommendation scenario, the most available feedback is the implicit invocation data, i.e., the binary data indicating whether or not a mashup has invoked an API. Various efforts are exploiting potential impact factors, such as the invocation context, to augment the implicit invocation data with the aim to improve service recommendation performance. One significant factor affecting the context of Web service invocations is geographical location, but it has been given less attention in the implicit-based service recommendation. In this paper, we propose a probabilistic matrix factorization based recommendation approach, which considers geographic location information in the derivation of the preference degree underlying a mashup-API interaction. The geographic information, which is integrated with functional descriptions, complements the mashup-API invocation data input for our matrix factorization model. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb. The evaluation results show that augmenting the implicit data with geographical location information increases the precision of API recommendation for mashup services.

A FORMAL APPROACH TO VALIDATED SERVICE DISCOVERY AND EXECUTION IN CLOUD COMPUTING

Ponte Academic JournalOct 2020, Volume 76, Issue 10 A FORMAL APPROACH TO VALIDATED SERVICE DISCOVERY AND EXECUTION IN CLOUD COMPUTINGAuthor(s): Souraya Hamida ,Okba KazarJ. Ponte - Oct 2020 - Volume 76 - Issue 10 doi: 10.21506/j.ponte.2020.10.4 Abstract:With cloud computing technology, we can move the local application resources into the cloud computing resource pool in the form of Web services. Mobile computing will continue to grow in the next few years in power and pervasiveness and is poised to become the dominant computing technology. Due to power and bandwidth constraints inherent in mobile computing, it is imperative to communicate with web services as efficiently as possible. This need for efficiency motivates our research. In this paper, we propose the use of the mobile agent for efficient discovery and invocation of M-services in cloud computing. The idea is to explore the properties of mobile agents to adapt to the characteristics of services, to eliminate the problems associated with mobile environments and mobile devices. Moreover, we exploit the advantages of the Semantic Web to automate the discovery and invocation of web services. Finally, we use the user context to better satisfy the demand of the user. On the other hand, high-level Petri nets are exploited to verify and simulate the proposed system. The verification test result demonstrates the viability and usefulness of the system. Download full text:Check if you have access through your login credentials or your institution Username Password

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

Reflective approach to improve self-adaptation of Web service compositions with autonomic middleware

Purpose This paper aims to propose an approach for the self-adaptation of the Web composition called SAMIWA. The SAMIWA framework helps users during the search, invocation and composition of the appropriate Web service. Design/methodology/approach The authors’ approach allows expressing requirements by taking into account potential users’ context in addition to the functional one. Findings In this paper, the authors introduce a new context-aware approach that provides a dynamic adaptation of service compositions. Originality/value The author has implemented a Web application that enables selection and composition of the most appropriate composite service.

Technical analysis on security realization in web services for e-business management

The web service is proved to be one of significant milestone in the evolution of distributed computing. Applications interoperate with programs providing simple services to deliver sophisticated value-added services. Web service proves to be a loosely coupled way of achieving complex operations with less ownership of the resources in a standard way. Variety of platforms and frameworks communicate with the aim of transferring the business intelligence, domain specific functionalities and so on. The communication between the server providing the service and the client revolves around two main web technologies such as World Wide Web, and Hyper Text Transfer Protocol. As specified earlier web service invocation is achieved due to collaboration of multiple entities on the web. The quality of service factors such as performance, reliability, security, response time, availability etc., are very important to enable this web service invocation. Among which security proves to be a challenging factor due to vulnerabilities in the web that is imposed on the usage of numerous methods, tools and technologies. In the same pace, numerous standards and mechanisms has been introduced to handle the security threats. It is found to be difficult to arrive at a complete solution or standard to address the security issues of web services. As an initiative to provide a broader perspective on security of web services the review presented could provide glimpses of security vulnerabilities and solutions available.

An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization

Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS-estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its practical parallelization.

A Jumpstart Framework for Semantically Enhanced OPC-UA

Decentralization is the norm of future smart production as it assists in contextual dynamic decision-making and thereby increases the flexibility required to produce highly customized products. When manufacturing business software is operated as a cloud based solution, it experiences network latency and connectivity issues. To overcome these problems, the production control should be delegated to the manufacturing edge layer and hence, the argument of decentralization is even more applicable to this narrative. In order to accomplish the assigned manufacturing task effectively, the edge layer is required to possess contextual awareness to make run-time decisions in production. Semantic technologies, on the other hand, assist in discerning the meaning, reasoning and drawing inferences from the data. There are several specifications and frameworks to automate the discovery, orchestration and invocation of web services; the prominent are OWL-S, SAWSDL and WSMO. This paper derives a hybrid approach that integrates OWL-S and SAWSDL specifications to overcome the downsides, yet retain the benefits of both approaches to the OPC-UA application methods. Consequently, the proposed semantically enriched OPC-UA concept enables the edge layer to create flexible production orchestration plans in a manufacturing scenario controlled by cloud MES. Furthermore, the derived hybrid approach is applied to a real use case to demonstrate its feasibility in industrial environments.

Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> overing-based quality prediction method for Web services via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</b> eighborhood-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> atrix <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</b> actorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization

With the rapid development of cloud manufacturing (CMfg), quality-of-service (QoS) prediction becomes increasingly important in CMfg service platform because it turns out to be impractical to acquire all service QoS values. In this paper, we present a neighbourhood enhanced matrix factorization approach to predict missing QoS values. We first systematically consider geographical information, sample set diversity computation and platform context to extend basic Pearson Correlation Coefficient (PCC) similarity and extract neighbourhood information. Then, we integrate neighbourhood information into matrix factorization (MF) and make prediction of missing values. Compared with existing methods, the proposed method has the following new features: (1) entropy information is adopted to derive personal weights for different users or services when computing PCC similarity; (2) location information and sample set similarity are considered to enhance PCC similarity; (3) topology information is introduced to address data sparsity issue; (4) neighbourhood information is incorporated with MF to improve prediction accuracy. We conduct an experiment on a real-world dataset which includes web service invocations from 339 service users on 5825 services to verify the feasibility and efficiency of our method.

Multi-agent System for Cloud Manufacturing Process Planning

The fundamental concern of building a distributed Cloud manufacturing (CM) platform is how wide variety of manufacturing services (SaaS, PaaS, HaaS, IaaS) can be integrated under a common cloud platform? More technically, this concern is related to the classical data integration problem, i.e. how to universally map information from heterogeneous data sources. Adoption of CM may become possible if collaborative agents can automatically discover a set of suitable CM services, and invoke them in a particular order to achieve a certain manufacturing task. Cloud computing community suggested semantic web services in order to enable automatic discovery, invocation and orchestration of web services. In this paper, we describe a novel multi-agent framework, where agents are configured based on Belief, Desire, and Intention (BDI) formalism. In this proposed framework, agents are capable of autonomously discovering and orchestrating manufacturing services based on their intrinsic and extrinsic awareness of the manufacturing knowledge at its most contemporary state. The internal structure of agents are constructed by Horn-DL rules, arguing such DL-safe rules will be able to extract and analyze manufacturing knowledge bases, built with OWL-DL. This chapter also introduces a novel method of selecting suitable services, from the intrinsic semantic in Horn-DL rule. This integration of Horn-Dl and semantic cloud services is designed based on a logical extension of Horn-DL.

A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side

Quality of service (QoS) has been playing an increasingly important role in today’s Web service environment. Many techniques have been proposed to recommend personalized Web services to customers. However, existing methods only utilize the QoS information at the client-side and neglect the contextual characteristics of the service. Based on the fact that the quality of Web service is affected by its context feature, this paper proposes a new QoS-aware Web service recommendation system, which considers the contextual feature similarities of different services. The proposed system first extracts the contextual properties from WSDL files to cluster Web services based on their feature similarities, and then utilizes an improved matrix factorization method to recommend services to users. The proposed framework is validated on a real-world dataset consisting of over 1.5 million Web service invocation records from 5825 Web services and 339 users. The experimental results prove the efficiency and accuracy of the proposed method.

Discovery and Invocation of Web Services using Multi-Dimensional Data Model with WSDL

Objectives: The aim of this research is to discover and invoke the Web services dynamically according to the users’ requirement. This system resolves the unknown input parameters’ names and invokes the required services. Methods/ Statistical Analysis: The methodology used is retrieving the Web Service Description Language (WSDL) of the Web services from the online-search-engine according to the user request, extracting and storing only the required elements in Multidimensional Online Analytical Processing (MOLAP) Cubes, mining MOLAP records to fill the unknown WSDL parameters’ name and dynamically invoking the Web services. Findings: The WSDL is big in size due to its multi elemental structure and requires huge space and time for storing and processing. Hence MOLAP cubes are used to store only the required elements of the retrieved WSDL. Existing Web Service discovery methods are classified into several categories: Ontology-based approach, UDDI based approach, Semantic-Based approach, QoS-based approach, Information Retrieval Based approach, data mining based approach and link-based approach. The drawbacks of the existing systems motivated the proposed system to use the WSDL-based method and to use an online search engine for the discovery of Web services. The proposed system is designed and implemented by considering the dynamic nature of the Web services. The results of experiments justify that the proposed system satisfies the user requests with reduced consumption of bandwidth, CPU and storage space. The performance of proposed system is high compared to the existing non-MOLAP systems. Application/ Improvements: Developing bigger applications with fewer resources and lesser time. Reusability of existing procedures through Remote Procedure Call (RPC). Satisfaction of the user requests at high speed.

Programming Language Design for Event-Driven Service Composition

To adapt to rapidly changing market conditions and increase the return of investment, today’s IT solutions usually combine service-oriented architecture (SOA) and event-driven architecture (EDA) that support reusability, flexibility, and responsiveness of business processes. Programming languages for development of event-driven service compositions face several main challenges. First, a language should be based on standard service composition languages to be compatible with SOA-enabling technologies. Second, a language should enable seamless integration of services into event-driven workflows. Third, to overcome a knowledge divide, language should enable seamless cooperation between application developers with different skills and knowledge. Since WS-BPEL is widely accepted as standard executable language in SOA, we extended WS-BPEL with support for event-driven workflow coordination. We designed event-handling mechanisms as special-purpose Coopetition services and augmented WS-BPEL with primitives for their invocation. Coopetition services augment SOA with fundamental EDA characteristics: decoupled interactions, many-to-many communication, publish/subscribe messaging, event triggering, and asynchronous operations. To make the application development familiar to wide community of developers, we designed an application-level end-user language on top of WS-BPEL whose primitives for invocation of regular Web services and Coopetition services resemble the constructs of typical scripting and coordination language.

An Invocation Cost Optimization Method for Web Services in Cloud Environment

The advent of cloud computing technology has enabled users to invoke various web services in a “pay-as-you-go” manner. However, due to the flexible pricing model of web services in cloud environment, a cloud user’ service invocation cost may be influenced by many factors (e.g., service invocation time), which brings a great challenge for cloud users’ cost-effective web service invocation. In view of this challenge, in this paper, we first investigate the multiple factors that influence the invocation cost of a cloud service, for example, user’s job size, service invocation time, and service quality level; and afterwards, a novel Cloud Service Cost Optimization Method named CS-COM is put forward, by considering the above multiple impact factors. Finally, a set of experiments are designed, deployed, and tested to validate the feasibility of our proposal in terms of cost optimization. The experiment results show that our proposed CS-COM method outperforms other related methods.

Managing QoS degradation of partner web services: A proactive and preventive approach

In the service oriented paradigm, a software solution is a composition of individual web services. These web services, also called partner services, contribute not only to realize the functional capabilities of the software solution but also determine its Quality of Service (QoS). A partner web service operates in a dynamic environment, and hence is vulnerable to failures, or suffers from QoS degradation. The failure of a partner web service compromises QoS of service based solution. Therefore, a research challenge arises as to how to manage web services which suddenly disappear at the time of execution or stop performing as expected. Several solutions exist for run time monitoring of the partner web services so that when QoS values of some of them degrade, the software solution can be adapted and executed using alternative web services with better QoS. But these solutions are confined either to client side or to provider side. In this paper, we propose a solution which is distributed between the clients and the service providers. It is a software agent based framework that prevents invocation of partner web services with degraded QoS. The experimental results show the effectiveness and efficiency of the proposed solution in a dynamic runtime environment.