Clustering of Bootstrap for Web Service Discovery

Web services are accessed using URLs in a distributed environment. WS WSDL document URLs are manually tabulated and clustered which increases the cost and timing for the developer. This paper introduces a new Clustering of URLs (CU) framework for clustering of bootstrap for web service discovery and clustering them in various domains using transfer, filter, spell check and domain set methods. These methods set them under the specific domain or general category. The CU framework is implemented with a sample URLs. The result shows the efficiency of the clustering of WSDL URLs

A service clustering method based on wisdom of crowds

As the number and variety of services increase, it is becoming difficult and time-consuming to locate services that satisfy users’ need. Service clustering is efficacious method to prune the query space, in order to narrow the search space, and improve the accuracy of locating services that satisfied users’ needs. At present, clustering method of web services adopted single or traditional clustering algorithms. However, accuracy and stability of single or traditional clustering algorithms is poor. In this paper, we proposed SWOC a service clustering method based on wisdom of crowd. Firstly, by using SWOC we calculated document similarity. Secondly, we implemented a mapping algorithm that reduces the correlation of web services and improve accuracy of method. And then, we applied different number of clusters using different individual clustering methods that increase the number of partitions in order to enhance the robustness of SWOC. Lastly, the diversity algorithm evaluates and selects the partitions to extract interesting information for the final aggregation with the weight of each individual result. Experiments were conducted on the real web service dataset crawled from ProgrammableWeb which demonstrate the accuracy, recall, F-value and stability of proposed method

Clustering Service Networks with Entity, Attribute, and Link Heterogeneity

Many popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose Service Cluster, a novel heterogeneous service network clustering algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration. Extensive evaluation on real datasets demonstrates that Service Cluster outperforms existing representative methods in terms of both effectiveness and efficiency

Scalable discovery of networked data : Algorithms, Infrastructure, Applications

Harmelen, F.A.H. van [Promotor]Siebes, R.M. [Copromotor

Personal Web API Recommendation Using Network-based Inference

Abstract. In this paper, we evaluate a generic network-based inference algorithm for Web API recommendation. Based on experimental data collected from the Programmable Web repository, we construct two tripartite networks: one where the nodes are Web APIs, users and mashups, and another where the nodes are Web APIs, users and tags. Experimental results show that the network-based inference algorithm yields higher precision, ranking quality and personalization score when applied to the second network. This approach also outperforms three existing methods: a global ranking method, a collaborative filtering method and the Programmable Web recommendation tool

Self-adaptive mobile web service discovery framework for dynamic mobile environment

The advancement in mobile technologies has undoubtedly turned mobile web service (MWS) into a significant computing resource in a dynamic mobile environment (DME). The discovery is one of the critical stages in the MWS life cycle to identify the most relevant MWS for a particular task as per the request's context needs. While the traditional service discovery frameworks that assume the world is static with predetermined context are constrained in DME, the adaptive solutions show potential. Unfortunately, the effectiveness of these frameworks is plagued by three problems. Firstly, the coarse-grained MWS categorization approach that fails to deal with the proliferation of functionally similar MWS. Secondly, context models constricted by insufficient expressiveness and inadequate extensibility confound the difficulty in describing the DME, MWS, and the user’s MWS needs. Thirdly, matchmaking requires manual adjustment and disregard context information that triggers self-adaptation, leading to the ineffective and inaccurate discovery of relevant MWS. Therefore, to address these challenges, a self-adaptive MWS discovery framework for DME comprises an enhanced MWS categorization approach, an extensible meta-context ontology model, and a self-adaptive MWS matchmaker is proposed. In this research, the MWS categorization is achieved by extracting the goals and tags from the functional description of MWS and then subsuming k-means in the modified negative selection algorithm (M-NSA) to create categories that contain similar MWS. The designing of meta-context ontology is conducted using the lightweight unified process for ontology building (UPON-Lite) in collaboration with the feature-oriented domain analysis (FODA). The self-adaptive MWS matchmaking is achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-Negative Selection Algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. The MWS categorization approach was evaluated, and its impact on the effectiveness of the framework is assessed. The meta-context ontology was evaluated using case studies, and its impact on the service relevance learning was assessed. The proposed framework was evaluated using a case study and the ProgrammableWeb dataset. It exhibits significant improvements in terms of binary relevance, graded relevance, and statistical significance, with the highest average precision value of 0.9167. This study demonstrates that the proposed framework is accurate and effective for service-based application designers and other MWS clients

Orchestration de web services fiables

L’Informatique Orienté Services représente un paradigme pour construire des applications distribuées sur Internet. L’Architecture Orientée Services(SOA) est un style architectural qui permet le développement de ces applications à base de services. Au cours de la dernière décennie, l’orchestration des services Web est devenue un domaine très actif dans la recherche scientifique et académique. Bien que de nombreux défis liés à l’orchestration aient été abordés, la fiabilité de l’orchestration et de sa vérification restent encore un sujet ouvert, prérequis et important de fait que ces orchestrations affectent aujourd’hui plusieurs activités quotidiennes. Cette thèse focalise sur le sujet d’orchestration des Services Web Fiables. En particulier, elle contribue avec un ensemble d’approches, de techniques et d’outils pour améliorer la sélection et l’orchestration des services web fiables. Premièrement, elle affine les phases du cycle de vie d’orchestration de services web afin d’assurer une vérification continuée de fiabilité lors des phases de conception et d’exécution. En outre, elle propose une architecture conceptuelle basée sur un registre de service amélioré, pour la mise en œuvre d’orchestrations fiables. Deuxièmement, elle présente une approche de mesure de similarité entre les services web. L’approche repose sur la comparaison des interfaces WSDL de services. L’approche sert à identifier les relations de similarité, de substituabilité et de composabilité entre services. L’outil WSSIM a été développé pour mettre en œuvre l’approche proposée. Pour validation, l’outil a été expérimenté avec un ensemble important de services web réels. Troisièmement, la thèse contribue avec une approche pour l’identification des substituts de services simples et complexes. L’approche utilise les techniques de mesure de similarité, la classification de service avec FCA et l’analyse de fiabilité pour identifier et sélectionner les meilleures substitutes. Un ensemble d’algorithmes aient été proposés pour décrire le processus d’identification. Quatrièmement, pour examiner la réputation des services comme un autre critère de fiabilité, la thèse introduit un Framework et un modèle mathématique pour la gestion de réputation de service We

Service-Oriented Middleware for the Future Internet: State of the Art and Research Directions

International audienceService-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last ten years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today's service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore investigating service description, discovery, access and composition in the Future Internet of services

Scientific Workflows for Metabolic Flux Analysis

Metabolic engineering is a highly interdisciplinary research domain that interfaces biology, mathematics, computer science, and engineering. Metabolic flux analysis with carbon tracer experiments (13 C-MFA) is a particularly challenging metabolic engineering application that consists of several tightly interwoven building blocks such as modeling, simulation, and experimental design. While several general-purpose workflow solutions have emerged in recent years to support the realization of complex scientific applications, the transferability of these approaches are only partially applicable to 13C-MFA workflows. While problems in other research fields (e.g., bioinformatics) are primarily centered around scientific data processing, 13C-MFA workflows have more in common with business workflows. For instance, many bioinformatics workflows are designed to identify, compare, and annotate genomic sequences by "pipelining" them through standard tools like BLAST. Typically, the next workflow task in the pipeline can be automatically determined by the outcome of the previous step. Five computational challenges have been identified in the endeavor of conducting 13 C-MFA studies: organization of heterogeneous data, standardization of processes and the unification of tools and data, interactive workflow steering, distributed computing, and service orientation. The outcome of this thesis is a scientific workflow framework (SWF) that is custom-tailored for the specific requirements of 13 C-MFA applications. The proposed approach – namely, designing the SWF as a collection of loosely-coupled modules that are glued together with web services – alleviates the realization of 13C-MFA workflows by offering several features. By design, existing tools are integrated into the SWF using web service interfaces and foreign programming language bindings (e.g., Java or Python). Although the attributes "easy-to-use" and "general-purpose" are rarely associated with distributed computing software, the presented use cases show that the proposed Hadoop MapReduce framework eases the deployment of computationally demanding simulations on cloud and cluster computing resources. An important building block for allowing interactive researcher-driven workflows is the ability to track all data that is needed to understand and reproduce a workflow. The standardization of 13 C-MFA studies using a folder structure template and the corresponding services and web interfaces improves the exchange of information for a group of researchers. Finally, several auxiliary tools are developed in the course of this work to complement the SWF modules, i.e., ranging from simple helper scripts to visualization or data conversion programs. This solution distinguishes itself from other scientific workflow approaches by offering a system of loosely-coupled components that are flexibly arranged to match the typical requirements in the metabolic engineering domain. Being a modern and service-oriented software framework, new applications are easily composed by reusing existing components