Web Service Composition Research Articles

Hybrid deep learning and similarity measures for requirements-driven composition of semantic web services

Hybrid deep learning and similarity measures for requirements-driven composition of semantic web services

An innovative approach for QoS-aware web service composition using whale optimization algorithm

With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent Whale Optimization Algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user’s requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.

A study on the effectiveness of web applications for publishing translations of oriental literature

The article analyzes existing websites for publishing and reading translations of literary works, with an emphasis on identifying their main problems and shortcomings. Three types of such sites are described: universal, specialized, and oriental literature-oriented. Each type has its own peculiarities, but they all face similar problems, including a limited choice of content, difficulties in finding materials, and inconvenient user interface. The main problem with specialized websites created by a single team of translators (e.g., Silver Raven) is the limited number of available works, as well as the inconvenience and time-consuming search for the right job due to the need to visit the website of a new team of translators every time. The design is often mediocre and inconvenient for users due to a lack of experience in website development and limited resources. Some teams do not even have their own websites and work on third-party platforms, such as Telegram. As for universal sites such as Wattpad, the main problem is the lack of a specialized filter for translated works, which makes it difficult to find them. In addition, there are shortcomings in the design of the reading pages, where a lot of free space is not used and the text is difficult to read due to inappropriate size and formatting. Specialized sites for oriental literature, such as uAuthor and Baka, also have their problems. For example, uAuthor does not allow reading works directly on the site, which creates additional inconvenience for users. Baka, while providing such a possibility, has a complex filtering system and limited tools for adding and editing text. The article concludes that the existing platforms do not fully meet the needs of translators and readers of such a type of oriental literature as ranobe, which prompts the development of a new web application. The proposed solution is to develop our own web application specifically aimed at publishing and reading works. The main attention will be paid to the design of both the website and the text editor to ensure ease of use for translators and readers. Another solution is to create a web platform of several web services that are interconnected using the composite web service design and modeling method.

Construction of supply chain management information system based on networked web service composition technology

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods.

Artificial Intelligence-Powered Personalized Web Services Composition

The process of creating new services by integrating existing ones is known as service composition. It is regarded as one of the most difficult tasks in a distributed and dynamic setting. In general, the composition process entails looking for existing services in a certain domain, selecting the suitable service, coordinating composition flow, and activating services. Researchers have been studying the need of web service composition using artificial intelligence extensively over the last few years. As a result, numerous solutions and novel approaches to addressing this issue are given. Our goal in this research is to use artificial intelligence to personalize web services composition. In our case, we employed natural language processing (NLP) to examine the descriptions and functionality of existing online services. Based on the user profile, AI algorithms can recommend and select appropriate services that meet the user’s needs. The experimental study shows that the proposed model can achieve robust results. In fact, our solution achieves 99.45% accuracy and 99.74% Precision.

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

A Knowledge Graph Service Composition Model for E-Government

In an era of Artificial Intelligence (AI), governments around the world are advancing their digital footprints, and using AI to achieving their objectives of effective and efficient public service delivery and citizens engagement. Knowledge Graph (KG), a constituent AI technology, has proven to be the means through which a government can use to manage the complexity of an e-Government instance, and realize its objective. One area in e-Government that KG is used to address, is the challenge of automated composition of web services, where web services are created and maintained by one government department, but is used by another, in an integrated and interoperable manner. Service composition is the combination of two or more web services to create a composite service in order to accomplish a task. In this paper, we present a KG-based approach for automatically composing web services in an e-Government context. Our aim is to advance the e-Government objective of effective and efficient public service delivery and citizens engagement, given a complex e-Government. We focus on creating a Service Dependency Graph (SDG), which captures the semantic relationships between the services, and we further describe the services with QoS characteristics, and then we create a weighted graph based on these two descriptions. We then adapted the Uniform Cost Search algorithm to find the shortest path between a start node and a goal node. We implement our algorithm, and carried-out experiments to obtain execution times, and we compared our results with benchmark results of comparable experimental setup. The difference in execution times of the service composition task is dependent on factors such as the design of the composition model, the complexity of the composition graph, and the hardware and software environments. Our research approach affords us the means to analyze and design our model; and validate and evaluate our work. Our model and the development approach help to achieve the e-Government goals of efficient and effective service delivery and citizens engagement.

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

AbstractWeb service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Verification of transaction-aware web services composition through formal methods

Verification of transaction-aware web services composition through formal methods

HIERARCHICAL MODEL FOR BUILDING COMPOSITE WEB SERVICES

The current evolution of disseminated computer program frameworks is characterized by a growing adherence to the principles of service-oriented engineering (SOA). Simultaneously, these frameworks are becoming more intricate, with an increasing number of components and more complex data connections between them. This situation underscores the importance of employing mechanisms to unify artifacts in the process of developing composite web services, which govern, among other aspects, the architectural plane of the frameworks under construction. A model for constructing composite web services is suggested as a suitable tool, implemented in accordance with a hierarchical approach, intended for use in designing distributed systems. Model is constructed over an assumption that coordination of the components of a composite web service is carried out in a centralized manner. in accordance with the orchestration model. To implement formalization and obtain, based on analytical representations, the corresponding software implementations, it has been decided to use the DEVS mathematical apparatus. The aspect of software implementation is considered pivotal in determining the feasibility of automating the acquisition of composite web services that operate within the orchestration model. Obtained research results has been interpreted as a conrmation of the eectiveness of this approach on the basis of the scenario of querying the database. Resulting artifacts have been represented with UML notation. The relationship between analytical representations and corresponding software implementations has also been demonstrated. Usage of the DEVS Suite tools has made it possible to visualize the process of simulation - to obtain estimated values of the indexes of the resulting solutions

Web Service Composition in Mobile Environment: A survey of Techniques

Web Service Composition in Mobile Environment: A survey of Techniques

Multi-Objective Service Composition in Uncertain Environments

Web services have the potential to offer the enterprises with the ability to compose internal and external business services in order to accomplish complex processes. Service composition then becomes an increasingly challenging issue when complex and critical applications are built upon services with different QoS criteria. However, most of the existing QoS-aware service composition techniques are simply based on the assumption that multiple QoS criteria, no matter whether these multiple criteria are conflicting or not, can be combined into a single criterion to be optimized, according to some utility functions. In practice, this can be very difficult as these utility functions or weights are not well-known a priori. In addition, the existing approaches are designed to work in certain environments, where the QoS parameters are well-known in advance. These approaches will render fruitless when facing uncertain and dynamic environments, e.g., cloud environments, where no prior knowledge of the QoS parameters is available. In this paper, two novel multi-objective approaches are proposed to handle QoS-aware Web service composition with conflicting objectives and various restrictions on the quality matrices. The proposed approaches use reinforcement learning in order to deal with the uncertainty characteristics inherent in open and dynamic environments. Experimental results reveal the ability of the proposed approaches to find a set of Pareto optimal solutions, which have the equivalent quality to satisfy multiple QoS-objectives with different user preferences.

User's intention and context as pertinent factors for optimal web service composition

User's intention and context as pertinent factors for optimal web service composition

Toward an efficient web service composition based on an improved BTLBO algorithm

Toward an efficient web service composition based on an improved BTLBO algorithm

Federated Latent Dirichlet Allocation for User Preference Mining

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

Agent-Based Model for Interpreting a Client Query as Part of Composite Web Service Discovery

Agent-Based Model for Interpreting a Client Query as Part of Composite Web Service Discovery

Efficient algorithm for replanning web service composition

Web-service-composition (WSC) workload execution inside a hybrid cloud environment is challenging. A dynamic approach for allocating resources to various tasks, as well as associated sub-tasks having a satisfactory quality-ofservice (QoS) requirement, is necessary for the present real-time demand. As a result of focusing primarily on decreasing processing time as well as cost, current approaches improve latency as well as energy while executing a given workload. This study introduces an efficient re-planning (ERP) algorithm for running many scientific workloads inside a heterogeneous cloud environment, which is designed to address some of the shortcomings of previous approaches. With a changing workload, this study details a technique to improve the WSC's availability as well as robustness. The workload's processing energy requirements are reduced as a result. The montageworkflow has been used to validate the research findings. Comparison with the current heterogeneous earliest finish time (QL-HEFT) algorithm demonstrates that its ERP-WSC approach is much more efficient and reliable.

Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or non-functional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this paper, we model the problem of collectively handling multiple service composition requests as a new multi-tasking service composition problem and propose a new Permutation-based Multi-factorial Evolutionary Algorithm based on an Estimation of Distribution Algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.