Web Service Ecosystems Research Articles

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of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable.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.

A Multi-source Information Graph-based Web Service Recommendation Framework for a Web Service Ecosystem

Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.

T-DSES: A Blockchain-powered Trusted Decentralized Service Eco-System

Existing Web service eco-systems are typically managed in a centralized manner, which hinders their further development due to inherent disadvantages such as trust issues, interest disputes, value separation and so on. The recently emerged blockchains provide distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states, which provide a natural solution. Based on the INKchain, which is an open-source permissioned blockchain mechanism extending the Hyperledger Fabric, this paper proposes Trusted Decentralized Service Eco-System (T-DSES). T-DSES achieves not only fundamental functionalities of conventional systems, but also offers mechanisms to stimulate participants to bring trustworthiness to the whole system. The trustworthiness of T-DSES is realized by three strategies: reliable information of services and mashups, reliable records of participants’ rights, and reliable measurement of participants’ contributions. A customized token “SToken” is created to act as the media of value circulation. In this paper, the overall framework and detailed design of T-DSES are presented, especially including how to utilize Kubernetes to establish a cloud-based environment. A tailored Web front-end ensures the usability of operations. Over real-world data from ProgrammableWeb.com, analyses and experiments have been conducted to verify the feasibility and effectiveness of the presented approach.

Data correction and evolution analysis of the ProgrammableWeb service ecosystem

The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.

Constructing and Evaluating Evolving Web-API Networks - A Complex Network Perspective

Despite the continual increase in the number of Web-APIs available on the internet, it is still challenging for API consumers to discover appropriate Web-APIs that could satisfy requirements. One of the main reasons for this is that Web-APIs registered on online directories such as ProgrammableWeb are in general isolated, as they are registered by diverse providers independently and progressively, ignoring continuous interactions among these APIs, which could enhance their discoverability. In this paper, we propose a method for analyzing the Web service ecosystem, and a complex network-based approach for constructing evolving networks for Web-APIs that are capable of enhancing their discoverability. We first conduct a two-phase analysis: We investigate mashups and Web-APIs interactions in the service ecosystem, and analyze their popularity distributions, and quantitatively measure two key node attachment dimensions within the ecosystem: Preferential Attachment and Similarity. Based on the analysis, we propose two methods for constructing evolving Web-API networks using the theoretical procedures of theBarab asi-Albert and the Popularity-SimilarityOptimizationnetwork models. Finally, we comprehensively evaluate the networks and map their properties with service discoverability using the ProgrammableWeb datasets. The results presented in this work will serve as a practical guide for designing a complex network-based solution for Web-API discovery.

A Web Service ecosystem for high-quality, cost-effective debris-flow hazard assessment

In this work, we present WEEZARD, a smart, cost-effective system, aimed at producing, managing and analysing high-quality debris-flow simulations to be used in hazard assessments. Developing the system was a challenge because of the complexity and the number of interconnected operations involved in such assessments. The goal was achieved by using a web client-server system, where a set of web services are exposed in a way similar to that used in OGC ® WPS solutions. The new system is composed of a previously developed advanced two-phase debris-flow model (TRENT2D), re-coded as a web service, and all the functionalities necessary to exploit the model, from the management of input and output (also provided as web services) to GIS features and two- and three-dimensional dynamic visualizations. Future developments include the possibility of using approaches such as the “Model as a Service” to further increase the accessibility and interoperability of the TRENT2D model.

Sorting Terms of “aaS” of Everything as a Service

Numerous service models have been proposed in the form of ”as a Service” or ”aaS” in the past. This is especially eminent in the era of the Cloud under the term of anything as a service or everything as a service(XaaS). A unified view of XaaS can aid efficient classification of services for service registration, discovery, and composition. In view of that there lacks such a unified view which is demanded by systematic application of XaaS in the Web Service Ecosystem, towards proposing a taxonomy of ”aaS”, we present in this work a collection of ”aaS” based on a throughout literature survey. We also proposed a strategy to identify the terms from the servicelization perspective.

A Study of Semantic Web Services Network

Loosely coupled and cross-platform features make Web services accessible and increasingly popular on the Internet. However, efficient service discovery and automated service composition are still challenges under the conventional practice where services are organized into categories. In this paper, we propose a graph-based method to organize Web services into a service ecosystem interlaced with service relationships at the semantic level. First, Web services are modelled as a set of interfaces, whose input and output parameters are annotated with well-defined ontologies. Secondly, semantic associations and interactions between Web services are mined, and services are constructed into a Web services network (SN), a variant of bipartite graph, by projecting the functional aspects of concrete Web services onto the abstract service layer. Thirdly, from the complex network perspective, the services relations are investigated and the structure of SN is analysed. To demonstrate the basic topological properties of SN, an empirical study is conducted on two data sets for comparative purposes, 10 000+ Web services collected from the Internet and 1231 Web services provided by Titan system of Zhejiang University. The experimental results reveal that SNs, which are built by different data sets on the semantic level, exhibit the same features such as small-world and scale-free. In addition, our results yield valuable insight for developing service discovery and automated composition algorithms, and characterizing the evolution of the entire Web service ecosystem.

Predicting change impact in Web service ecosystems

Purpose – The paper aims to address the issue of Web service providers facing a major issue of estimating the potential effects of changing a Web service to other services, especially in large ecosystems of Web services which have become more common nowadays. Web service providers make constant changes to their Web services to meet the ever-changing business requirements. Design/methodology/approach – The paper proposes an approach to predict change impact by mining a version history of a Web service ecosystem. The proposed approach extracts patterns of Web services that have been changed together from the version history by using association rule data mining techniques. The approach then uses this knowledge of co-changed patterns for predicting the impact of future changes based on the assumption that Web services which have been changed together frequently in the past will likely be changed together in future. Findings – An empirical validation based on the Amazon’s ecosystem of 46 Web services indicates the effectiveness of the proposed approach. After an initial change, the proposed approach can correctly predict up to 25 per cent of further Web services to be changed with the precision of up to 82 per cent. Originality/value – Traditional approaches to predict change impact in Web services tend to rely on having a dependency graph between Web services. However, in practice, building and maintaining inter-service dependencies that capture the precise semantics and behaviours of the Web services are challenging and costly. The proposed approach offers a novel alternative which only requires mining the existing version history of Web services.

Exploring the Categories and Models of Everything as a Service (XaaS)

Researchers have proposed numerous service models for various contents. Providing products, processes, data and information as a service has emerged into the huge stream of Everything as a Service(XaaS). However there lacks a unified view of XaaS which hinders the systemic application of XaaS in the Web Service Ecosystem. The unified view of XaaS can aid the classification of services for service registration, discovery and composition. Based on a throughout literature survey, we propose a taxonomy on service types covering traditional service, telecommunication service, SOA and Cloud service.

The Rise of Web Service Ecosystems

The convergence of Internet marketplaces and service-oriented architectures has spurred the growth of Web service ecosystems. This paper articulates a vision for Web service ecosystems, discusses early manifestations of this vision, and presents a unifying architecture to support the emergence of larger and more sophisticated ecosystems