Web Service Composition Systems for the Web Service Challenge - A Detailed Review

This report gives a detailed discussion on the system, algorithms, and techniques that we have applied in order to solve the Web Service Challenges (WSC) of the years 2006 and 2007. These international contests are focused on semantic web service composition. In each challenge of the contests, a repository of web services is given. The input and output parameters of the services in the repository are annotated with semantic concepts. A query to a semantic composition engine contains a set of available input concepts and a set of wanted output concepts. In order to employ an offered service for a requested role, the concepts of the input parameters of the offered operations must be more general than requested (contravariance). In contrast, the concepts of the output parameters of the offered service must be more specific than requested (covariance). The engine should respond to a query by providing a valid composition as fast as possible. We discuss three different methods for web service composition: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.

Managing Quality Properties of Web Service Compositions

Web services from different partners can be combined to applications that realize a more complex business goal. Such applications built as Web service compositions define how interactions between Web services take place in order to implement the business logic. Web service compositions not only have to provide the desired functionality but also have to comply with certain Quality of Service (QoS) levels. Maximizing the users' satisfaction, also reflected as Quality of Experience (QoE), is a primary goal to be achieved in a Service-Oriented Architecture (SOA). Unfortunately, in a dynamic environment like SOA unforeseen situations might appear like services not being available or not responding in the desired time frame. In such situations, appropriate actions need to be triggered in order to avoid the violation of QoS and QoE constraints. In this thesis, proper solutions are developed to manage Web services and Web service compositions with regard to QoS and QoE requirements. The Business Process Rules Language (BPRules) was developed to manage Web service compositions when undesired QoS or QoE values are detected. BPRules provides a rich set of management actions that may be triggered for controlling the service composition and for improving its quality behavior. Regarding the quality properties, BPRules allows to distinguish between the QoS values as they are promised by the service providers, QoE values that were assigned by end-users, the monitored QoS as measured by our BPR framework, and the predicted QoS and QoE values. BPRules facilitates the specification of certain user groups characterized by different context properties and allows triggering a personalized, context-aware service selection tailored for the specified user groups. In a service market where a multitude of services with the same functionality and different quality values are available, the right services need to be selected for realizing the service composition. We developed new and efficient heuristic algorithms that are applied to choose high quality services for the composition. BPRules offers the possibility to integrate multiple service selection algorithms. The selection algorithms are applicable also for non-linear objective functions and constraints. The BPR framework includes new approaches for context-aware service selection and quality property predictions. We consider the location information of users and services as context dimension for the prediction of response time and throughput. The BPR framework combines all new features and contributions to a comprehensive management solution. Furthermore, it facilitates flexible monitoring of QoS properties without having to modify the description of the service composition. We show how the different modules of the BPR framework work together in order to execute the management rules. We evaluate how our selection algorithms outperform a genetic algorithm from related research. The evaluation reveals how context data can be used for a personalized prediction of response time and throughput.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 8: Web Technologies 1

This class focuses on a selected subset of web technologies that are of interest to the topics of this course. Readings: Chapter 5 "Representational State Transfer (REST)", in "Architectural Styles and the Design of Network-based Software Architecture", Roy Fielding, Dissertation, University of California Irvine, 2000 Optional: Chapter "Representational State Transfer (REST)" in "Pro PHP XML and Web Services", R. Richards 633--672, 2006

Measuring Research Impact: Bibliometrics for Academic Staff and PGRs

Material to support the library session to support this course.

Measuring Research Impact: Bibliometrics for Research Postgraduates (Gradbook Session)

Material to support the library session to support this course.

Geoservicios web SIOSE: un sistema de información como servicio público

Tras finalizar la producción del Sistema de Información de Ocupación de Suelo en España (SIOSE) afrontamos su publicación en la red. A la hora de decidir la estrategia de difusión de la información debemos tener en cuenta las necesidades de los usuarios, dando acceso a la visualización, consulta y análisis de los datos originales del sistema y de sus diferentes vistas temáticas. SIOSE tiene, para realizar esta tarea de forma adecuada, dos características que nos condicionan: su gran volumen de información y la complejidad semántica de su modelo de datos orientado a objetos. A partir de una arquitectura construida con componentes de software libre, desde el sistema operativo hasta las aplicaciones servidoras; los geoservicios web estándar son la primera opción para comunicar información geográfica a través de la web, pero las posibles soluciones son múltiples y muy variadas. Además los servicios deben combinarse y coordinarse con otras herramientas de publicación, como la libre descarga de los datos en bruto, para conseguir que SIOSE cumpla con su función de infraestructura de información territorial sobre la que generar valor añadido y para que sea una herramienta útil de análisis del territorio. Describiremos como hemos configurado nuestros servicios de visualización y consulta de los datos para mostrar en la red toda la jerarquía de información y los diferentes enfoques de análisis con los que se puede abordar SIOSE y como hemos intentado optimizar el rendimiento de estos servicios para ofrecer tiempos de respuesta adecuados ante cualquier posible petición

Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.

An EPN-based method for web service composition

Web service composition can be facilitated by an automatic process which consists of rules, conditions and actions. This research has adapted ElementaryPetri Net (EPN) to analyze and model the web services and their composition. This paper describes a set of techniques for representing transition rules, algorithm and workflow that web service composition can be automatically carried out.

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Describing Web Service Architectures through Design-by-Contract

Architectural description languages (ADLs) are used to specify a high-level, compositional view of a software application, specifying how a system is to be composed from coarse-grain components. ADLs usually come equipped with a formal dynamic semantics, facilitating specification and analysis of distributed and event-based systems. In this paper, we describe the TrustME, an ADL framework that provides both a process and a structural view of web service-based systems. We use Petri-net descriptions to give a dynamic view of business workflow for web service collaboration. We adapt the approach of Schmidt to define a form of Meyer's design-by-contract for configuring workflow architectures. This serves as a configuration-level means of constructing safer, more robust systems.

A Resource Centric Approach For Advancing Collaboration Through Hydrologic Data And Model Sharing

HydroShare is an online, collaborative system being developed for open sharing of hydrologic data and models. The goal of HydroShare is to enable scientists to easily discover and access hydrologic data and models, retrieve them to their desktop or perform analyses in a distributed computing environment that may include grid, cloud or high performance computing model instances as necessary. Scientists may also publish outcomes (data, results or models) into HydroShare, using the system as a collaboration platform for sharing data, models and analyses. HydroShare is expanding the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated, creating new capability to share models and model components, and taking advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. One of the fundamental concepts in HydroShare is that of a Resource. All content is represented using a Resource Data Model that separates system and science metadata and has elements common to all resources as well as elements specific to the types of resources HydroShare will support. These will include different data types used in the hydrology community and models and workflows that require metadata on execution functionality. The HydroShare web interface and social media functions are being developed using the Drupal content management system. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic datasets. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated data content and perform rule-based background actions on data and model resources, including parsing to generate metadata catalog information and the execution of models and workflows. This presentation will introduce the HydroShare functionality developed to date, describe key elements of the Resource Data Model and outline the roadmap for future development.