Dissertations / Theses on the topic 'Manageable and Adaptive Web Services'

Dynamic selection and composition of autonomous and loosely-coupled Web services is increasingly used to automate business processes. The typical long-running characteristic of business processes imposes new management challenges such as dynamic adaptation of running process instances. However, current process orchestration engines provide limited flexibility to dynamically adapt to changing runtime conditions (e.g., presence of faults). Additionally, current process specification languages exhibit some limitations regarding modularity of crosscutting management concerns. In particular, monitoring and adaptation logic is often scattered across several process definitions and intertwined with the business logic. This leads to monolithic and complex processes that are hard to understand, reuse, maintain, and evolve. To address these limitations, we developed a policy-based change management framework, named Manageable and Adaptable Service Compositions (MASC), to declaratively express crosscutting monitoring and process adaptation concerns in a separate and modular way. MASC policies use a set of simple, but flexible and relatively powerful, constructs to declaratively specify policies that govern: (1) discovery and selection of services to be used, (2) monitoring to detect the need for adaptation, (3) reconfiguration and adaptation of the process to handle special cases (e.g., context-dependant behaviour) and recover from typical faults in service-based processes. The identified constructs are executed by a lightweight service-oriented management middleware named MASC middleware. The adaptation is transparent because it preserves the original functional behaviour of the business process and does not tangle the adaptation logic with that of the business process. Additionally, policies do not have to be necessarily defined when designing the process; they can also be introduced later during deployment or at runtime. We implemented a MASC proof-of-concept prototype and evaluated it on Stock Trading case study scenarios. We conducted extensive studies to demonstrate the feasibility of the proposed techniques and illustrate the benefits of our approach in providing adaptive composite services using the policy-based approach. Our performance and scalability studies indicate that MASC middleware is scalable and the introduced overhead are acceptable.

Web services are often used for search computing where data is retrieved from servers providing information of different kinds. Such data providing web services return a set of objects for a given set of parameters without any side effects. There is need to enable general and scalable search capabilities of data from data providing web services, which is the topic of this Thesis. The Web Service MEDiator (WSMED) system automatically provides relational views of any data providing web service operations by reading the WSDL documents describing them. These views can be queried with SQL. Without any knowledge of the costs of executing specific web service operations the WSMED query processor automatically and adaptively finds an optimized parallel execution plan calling queried data providing web services. For scalable execution of queries to data providing web services, an algebra operator PAP adaptively parallelizes calls in execution plans to web service operations until no significant performance improvement is measured, based on monitoring the flow from web service operations without any cost knowledge or extensive memory usage. To comply with the Everything as a Service (XaaS) paradigm WSMED itself is implemented as a web service that provides web service operations to query and combine data from data providing web services. A web based demonstration of the WSMED web service provides general SQL queries to any data providing web service operations from a browser. WSMED assumes that all queried data sources are available as web services. To make any data providing system into a data providing web service WSMED includes a subsystem, the web service generator, which generates and deploys the web service operations to access a data source. The WSMED web service itself is generated by the web service generator.
eSSENCE

The amount of information and services that are available over the Internet is so overwhelming that ist is very difficult to select the ones that fit our requirements. Recommender Systems are being used for helping whit the selection of services and products. The current approaches use products rating s to compute the reputation of a provider not taking into consideration the possibility of indirect service delivery. For this purpose, we propose a framework and appropriate mechanisms that can be used to better evaluate the reputation of services/providers. Another issue regarding the accuracy of a service reputation update comes from the dynamics of the feedback. Current approaches do not make a correlation with the user’ feedback pattern or with the frequency of the users’ feedback. We take these into consideration when computing a service reputation and propose an approach for modeling the dynamic user feedback. Service similarity is another important part of Recommender Systems. The customer’ satisfaction depends on how close a recommendation is to his requirements, but also on how easy it was to specify the searching criteria. We present a approach for selecting services based on distance and similarity, introducing a similarity taxonomy for adjusting service invocation under certain constraints
La quantité d’informations et de services disponibles sur l’Internet est si importante qu’il est très difficile de choisir ceux qui répondent bien à nos exigences. Dans cette thèse, l’auteure présente un algorithme pour le calcul de la réputation de services en proposant un modèle amélioré du comportement des utilisateurs. Cette technique se révèle bien adaptée pour bâtir des modèles de comportement. De plus elle a travaillé sur les aspects concernant les similarités de services afin d’obtenir des réponses appropriées aux demandes des services pour améliorer la qualité de l’expérience. Dans ce cadre, un algorithme évaluant la proximité des services a été développé. A partir de cet algorithme, une version adaptative avec des intervalles variables pour les paramètres de services également a été proposée. Ces algorithmes ont été testé et validés sur des classes de services

In Multi-agent System (MAS), all agents communicate with each other by sending messages to each other in an expressive agent communication language. Agent communication language (ACL) [12], defines type of messages and their meaning that agents can exchange. Messages that agents communicate have semantic meanings which can be proposition, rules or actions [27]. In other words, multi-agent system is an association of synchronized, autonomous agents, which interact with each other in achieving common goals (objectives). On the other hand, Web services are the services in the shape of software components accessible on the internet, which provide useful information to users, businesses and organizations. The Web service model uses WSDL, an XML format responsible for the service interfaces description along with the binding details to specific protocols. UDDI, a protocol responsible for publishing and finding services, services details and descriptions etc. SOAP, an XML message based envelop format which has the bindings to specific protocols (e.g. HTTP, SMTP etc). These services are invoked over the www (World Wide Web) using the SOAP/XMLP protocol. A Workflow can be defined as “The automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules” [21]. It has many advantages like improved efficiency, better process control, improved customer service, flexibility and business process improvement [7]. Due to rapid advancements in technology and growing needs of business environment, there is a need of adaptive workflow, which could accommodate itself with the changes that occur in the business processes. Traditionally, workflow management systems have not been designed for dynamic environments requiring adaptive response. Currently, the need for adaptive workflow is being driven by the demands of e-commerce in both B2B and B2C space. Adaptive workflows respond to changing conditions through adaptive change. The aim of this thesis is to suggest an adaptive work flow model that can help in eliminating problems in e-commerce domain by using agent based approach. In e-commerce there is always a problem of searching the right item (e.g.construction material) in less time without involving the contractors etc who search for the items with the specifications told by the customers, as the current system does not support the good search. The customers search each time for the required items (e.g. construction material) and stop their search when they have found the desired item according to their budget, cost and quality attributes with up to date market cost about the required items to purchase (construction material). In e-commerce workflow system, in purchasing the required items (construction material), there are processes involved (Order Capture, Order Process, Order Fulfillment) which do not address the adaptability attribute in case of exception [38] or when there is a change in business environment which make changes in the business processes, consequences of which can be in the shape of failure of business objective (unsuccessful business transaction). The proposed approach somehow can eliminate the problem described above and suggests an adaptive workflow system by introducing agents with each of the processes (Order Capture, Order, Process, and Order Fulfillment). A proposed way to design adaptive work flow is explained with the help of agents. Some work is done to relate this framework with web services to provide refined search and purchasing in order to take care of user needs. But still there is need of more research to explore this area of e-commerce workflow system.

Service technology geared by its SOA architecture and enabling Web services is rapidly gaining in maturity and acceptance. Consequently, most worldwide (private and corporate) cross-organizations are embracing this paradigm by publishing, requesting and composing their businesses and applications in the form of (web-)services. Nevertheless, to face harsh competitiveness such service oriented cross-organizational applications are increasingly pressed to be highly composite, adaptive, knowledge-intensive and very reliable. In contrast to that, Web service standards such as WSDL, WSBPEL, WS-CDL and many others offer just static, manual, purely process-centric and ad-hoc techniques to deploy such services. The main objective of this thesis consists therefore in leveraging the development of service-driven applications towards more reliability, dynamically and adaptable knowledge-intensiveness. This thesis puts forward an innovative framework based on distributed high-level Petri nets and event-driven business rules. More precisely, we developed a new variant of high-level Petri Nets formalism called Service-based Petri nets (CSrv-Nets), that exhibits the following potential characteristics. Firstly, the framework is supported by a stepwise methodology that starts with diagrammatical UML-class diagrams and business rules and leads to dynamically adaptive services specifications. Secondly, the framework soundly integrates behavioural event-driven business rules and stateful services both at the type and instance level and with an inherent distribution. Thirdly, the framework intrinsically permits validation through guided graphical animation. Fourthly, the framework explicitly separates between orchestrations for modelling rule-intensive single services and choreography for cooperating several services through their governing interactive business rules. Fifthly, the framework is based on a two-level conceptualization: (1) the modelling of any rule-centric service with CSrv-Nets; (2) the smooth upgrading of this service modelling with an adaptability-level that allows for dynamically shifting up and down any rule-centric behavior of the running business activities.

Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.
Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.

This thesis presents the research and development of robust metamodelling tools for engineering design. Metamodelling in engineering is typically used for reducing computational cost of highly expensive analyses or simulations. Metamodels have been shown to be effective in these problems where an approximation constructed from a limited set of true data points is used in support of optimisation. The inspiration for this work is drawn from the optimisation of aircraft wing structures, constructed using large numbers of rectangular stiffened panels. When optimising such structures to produce a minimum weight design, it is necessary to evaluate multiple design constraints such as buckling load, damage tolerance and repairability. The total computational cost for this aspect of the analysis can become considerable when a large number of evaluations is required and can creates a bottleneck in the optimisation workflow. In response to this industrial design problem, a specification is proposed for an efficient and adaptive metamodelling formulation. Following an extensive literature review the multilevel Radial Basis Function (mRBF) model is highlighted as a promising candidate for further investigation. The mRBF formulation is discussed in detail, and a comparative study is presented comparing mRBF to more established modelling techniques. mRBF is then put to work on a range of optimisation test problems, including an industrial scale multi-panel wing design scenario. Emphasis is placed on the adaptive acquisition of model data as the optimisation process progresses. Implementation details and software development processes are also presented in detail. The case is made for decoupled modelling workflows, and a RESTful web based mRBF modelling framework. Finally the performance of the proposed modelling scheme is compared to the original specification, and recommendations are made for further investigation.

This report covers theory and comparison of techniques for rendering massive scale 3D geospa- tial planet data in a web browser. It also presents implementation details of a few of these tech- niques in WebGL and Javascript, using the Three.js [1] 3D library. The thesis project is part of the implementation of Unitea, a web based education platform for interactive astronomy visualizations. Unitea is a derivative of Uniview, which is a fulldome interactive simulation of the universe. A major part of this thesis is dedicated to the implementa- tion of Hierarchical Level of Detail (HLOD) modules for Three.js based on the theory presented by T. Ulrich [2] and later generalized by Cozzi and Ring [3]. HLOD techniques are dynamic level of detail algorithms that represent the surface of objects as accurately as possible from a certain viewing angle. By using space partitioning tree-structures, view based error metrics and culling techniques detailed representations of the objects (in this case planets) can be efficiently rendered in real-time. The modules developed provide a general-purpose library for rendering planets (or other spher- ical objects) with dynamic level of detail in Three.js. The library also features connections to online web map services (WMS) and tile services.

Context awareness means sending the right information to the right user at the righttime. Context is our environment, which can be anything around us such as location,lights, noises etc. To make the context interact with our mobile devices or sensors,there must be protocols for communication and data formats for the “sent” or “received”contextual information so we can give very specific context information tothe user. Since this communication and adaptive part is not well understood, in thispaper we are interested in investigating the technology used for adaptation. We willalso explain how this technology works to adapt itself to changes in the environment.

Le trafic vidéo a subi une augmentation fulgurante sur Internet ces dernières années. Pour pallier à cette importante demande de contenu vidéo, la technologie du streaming adaptatif sur HTTP est utilisée. Elle est devenue par ailleurs très populaire car elle a été adoptée par les différents acteurs du domaine de la vidéo streaming. C’est une technologie moins couteuse qui permet aux fournisseurs de contenu, la réutilisation des serveurs web et des caches déjà déployés. En plus, elle est exempt de tout blocage car elle traverse facilement les pare-feux et les translations d’adresses sur Internet. Dans cette thèse, nous proposons une nouvelle méthode de vidéo streaming adaptatif appelé “Backward-Shifted Coding (BSC)”. Il se veut être une solution complémentaire au standard DASH, le streaming adaptatif et dynamique utilisant le protocole HTTP. Nous allons d’abord décrire ce qu’est la technologie BSC qui se base sur le codec (encodeur décodeur) à multi couches SVC, un algorithme de compression extensible ou évolutif. Nous détaillons aussi l’implémentation de BSC dans un environnement DASH. Ensuite,nous réalisons une évaluation analytique de BSC en utilisant des résultats standards de la théorie des files d’attente. Les résultats de cette analyse mathématique montrent que le protocole BSC permet de réduire considérablement le risque d’interruption de la vidéo pendant la lecture, ce dernier étant très pénalisant pour les utilisateurs. Ces résultats vont nous permettre de concevoir des algorithmes d’adaptation de qualité à la bande passante en vue d’améliorer l’expérience utilisateur. Ces algorithmes permettent d’améliorer la qualité de la vidéo même étant dans un environnement où le débit utilisateur est très instable.La dernière étape de la thèse consiste à la conception de stratégies de caching pour optimiser la transmission de contenu vidéo utilisant le codec SVC. En effet, dans le réseau, des serveurs de cache sont déployés dans le but de rapprocher le contenu vidéo auprès des utilisateurs pour réduire les délais de transmission et améliorer la qualité de la vidéo. Nous utilisons la programmation linéaire pour obtenir la solution optimale de caching afin de le comparer avec nos algorithmes proposés. Nous montrons que ces algorithmes augmentent la performance du système tout en permettant de décharger les liens de transmission du réseau cœur
Due to the growth of video traffic over the Internet in recent years, HTTP AdaptiveStreaming (HAS) solution becomes the most popular streaming technology because ithas been succesfully adopted by the different actors in Internet video ecosystem. Itallows the service providers to use traditional stateless web servers and mobile edgecaches for streaming videos. Further, it allows users to access media content frombehind Firewalls and NATs.In this thesis we focus on the design of a novel video streaming delivery solutioncalled Backward-Shifted Coding (BSC), a complementary solution to Dynamic AdaptiveStreaming over HTTP (DASH), the standard version of HAS. We first describe theBackward-Shifted Coding scheme architecture based on the multi-layer Scalable VideoCoding (SVC). We also discuss the implementation of BSC protocol in DASH environment.Then, we perform the analytical evaluation of the Backward-Sihifted Codingusing results from queueing theory. The analytical results show that BSC considerablydecreases the video playback interruption which is the worst event that users can experienceduring the video session. Therefore, we design bitrate adaptation algorithms inorder to enhance the Quality of Experience (QoE) of the users in DASH/BSC system.The results of the proposed adaptation algorithms show that the flexibility of BSC allowsus to improve both the video quality and the variations of the quality during thestreaming session.Finally, we propose new caching policies to be used with video contents encodedusing SVC. Indeed, in DASH/BSC system, cache servers are deployed to make contentsclosed to the users in order to reduce network latency and improve user-perceived experience.We use Linear Programming to obtain optimal static cache composition tocompare with the results of our proposed algorithms. We show that these algorithmsincrease the system overall hit ratio and offload the backhaul links by decreasing thefetched content from the origin web servers

Present situation in globalizing economy when margins are falling is forcing businesses to change traditional supply chains to adaptive supply chain networks. Adaptive supply chain network - a network of user oriented enterprises, sharing resources and knowledge, in order to be able to react smoothly to constantly changing and competitive environment. Currently market leading supply chain management systems aren't providing profitable opportunities to organize adaptive supply chain networks. In order to elevate the competitiveness of small and medium enterprises by increasing the effectiveness of their business processes, a supply chain management system, which could make use of e-business concepts, has to be developed. In order to do so, an integrated supply chain network model was created, which serves as a conceptual foundation and provides guidelines for creation of adaptive supply chain network management systems. Because members of adaptive supply chain network are separate enterprises, they must share data by the means of internet. It is purposeful to use web services to implement this requirement, because they enable communication of software systems, which were developed on different platforms or using different programming languages. During a project such a system was developed and it provides an opportunity to integrate an enterprise into an adaptive supply chain network. The solution, which is operating as a set of web services and is implemented in Java, can... [to full text]

Web cluster systems provide cost-effective solutions when scalable and reliable web services are required. However, as the number of servers in web cluster systems increase, web cluster systems incur long and unpredictable delays to manage servers. This study presents the efficient management schemes for web cluster systems. First of all, we propose an efficient request distribution scheme in web cluster systems. Distributor-based systems forward user requests to a balanced set of waiting servers in complete transparency to the users. The policy employed in forwarding requests from the frontend distributor to the backend servers plays an important role in the overall system performance. In this study, we present a proactive request distribution (ProRD) to provide an intelligent distribution at the distributor. Second, we propose the heuristic memory management schemes through a web prefetching scheme. For this study, we design a Double Prediction-by-Partial-Match Scheme (DPS) that can be adapted to the modern web frameworks. In addition, we present an Adaptive Rate Controller (ARC) to determine the prefetch rate depending on the memory status dynamically. For evaluating the prefetch gain in a server node, we implement an Apache module. Lastly, we design an adaptive web streaming system in wireless networks. The rapid growth of new wireless and mobile devices accessing the internet has contributed to a whole new level of heterogeneity in web streaming systems. Particularly, in-home networks have also increased in heterogeneity by using various devices such as laptops, cell phone and PDAs. In our study, a set-top box(STB) is the access pointer between the internet and a home network. We design an ActiveSTB which has a capability of buffering and quality adaptation based on the estimation for the available bandwidth in the wireless LAN.

碩士
銘傳大學
資訊工程學系碩士班
95
Nowadays, people pay more attention to the need of adaptive learning. Adaptive Learning (AL) System can give learners assistance based on their characteristics on the right time. However, development of such a system costs a lot as it requires establishment and integration of several knowledge resources, course adaptation mechanisms and inference methods to generate adaptive courses. To reduce the development cost of AL systems, this research develops a framework for adaptive learning based on semantic web and web services, and integrates popular learning standards to promote sharing and reuse of knowledge resources and adaptation utilities. Specifically, web services technology is used to promote sharing of AL inference functions on the Internet, and semantic web technology is used to promote integration of knowledge resources. Finally, a set of preliminary evaluation results show that the system performance is acceptable under light to middle traffics of requests, and that the students are satisfied with the personalized learning support of the built AL system. Conducting a larger scale experiment is suggested to furher verifry the instructional effectiveness of the resulting system.

博士
國立中正大學
資訊管理系研究所
104
The emerging service-oriented computing (SOC) paradigm promises to enable Business-to-Business (B2B) collaborations by means of Web services. In the last decade, the process-based approach to Web service composition (WSC) has gained considerable momentum and standardization. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. However, the construction of value-added services on a dynamic environment is not a trivial task. A fundamental problem in WSC is to discover and select the most appropriate candidate services in order to generate an optimal composition plan. Typical Web service selection focuses on selecting trustworthy service among a bunch of services with same or similar functions but different QoS. Consumers need to estimate the quality of Web services before selecting. Usually, the estimation is based on the QoS data provided by service providers and the reputation scores evaluated by consumers. Many trust and reputation models have been developed for service selection, but most of them often assume that the QoS and reputation data are trustworthy. Actually it is impossible in reality since the dishonest service providers and malicious customers exist all over the Internet. Another challenge in WSC is the dynamic service composition. Web services operate autonomously in a highly variable environment, therefore, their QoS may alter relatively frequently. In particular, during an execution of a composite service, the component services involved may modify their QoS properties, some may become unavailable, or new ones may emerge. Thus, how to model QoS-driven composite service and optimize the composite service’s QoS under given requester’s requirements in a dynamic environment are also being sought. To enhance availability of services composition, the social-context-aware trust and reputation model (SCATRM) was proposed to monitor and predict the trustworthiness of Web services that are assembled into composite services. We use self-experience and social reputation relationship to calculated trustworthiness of services and utilizes it to perform a preliminary filtering ensuring individual service candidates respect user QoS requirements. Based on these reliable candidate services, a dynamic and adaptive QoS-aware WSC model is proposed and the selection of candidate services is performed in a global optimization approach. It not only provides composite service is to enable these tasks while meeting QoS constraints set by users and user preference, but also producing not a single but multiple service compositions to allows for coping with changing conditions in dynamic environments. The proposed model and algorithm are proven to be reasonable, practical, comparable, and workable in service-oriented environments.

This thesis presents the design and analysis of Spasiba, a context-aware mobile advisor. We argue that current context-aware mobile applications exhibit significant flaws with respect to (1) limited use of context information, (2) incomplete or irrelevant content generation, and (3) low usability. The proposed model attempts to tackle these limitations by advancing the usage and manipulation of context information, automating the back-end systems in terms of self-management and seamless extensibility, and shifting the logic away from the client side. A distinguishing characteristic of Spasiba is the proactive approach to notifying the user of information of interest. In this proactive approach, the user subscribes to the service and receives content updates as the context changes. This proposed model is realised in a proof-of-concept prototype that uses a Nokia Web Runtime widget as the client application. The widget, which sports an elegant, touch-optimised interface, collects multiple context parameters to deliver high-quality results. The server-side architecture employs the publish/subscribe paradigm for managing the active users and Comet—for proactively notifying the clients of updated information of interest. IRS-III, a Semantic Web Services broker, handles the process of content generation. The prototype employs nine data sources, seven of which are open API web services and two of which are regular web pages, to deliver diverse and complete results. A simple autonomic element, implemented with the help of aspect-oriented programming, ensures partial self-management of the back-end systems. Spasiba is evaluated by means of a case study that involves a tourist couple visiting Victoria. The application assists the tourist couple with finding attractions, relevant stores, and places serving food.

User Modeling and Machine Learning for User Modeling have both become important research topics and key techniques in recent adaptive systems. One of the most intriguing problems in the `information age´ is how to filter relevant information from the huge amount of available data. This problem is tackled by using models of the user´s interest in order to increase precision and discriminate interesting information from un-interesting data. However, any user modeling approach suffers from several major drawbacks: User models built by the system need to be inspectable and understandable by the user himself. Secondly, users in general are not willing to give feedback concerning user satisfaction by the delivered results. Without any evidence for the user´s interest, it is hard to induce a hypothetical user model at all. Finally, most current systems do not draw a line of distinction between domain knowledge and user model which makes the adequacy of a user model hard to determine. This thesis presents the novel approach of conceptual user models. Conceptual user models are easy to inspect and understand and allow for the system to explain its actions to the user. It is shown, that ILP can be applied for the task of inducing user models from feedback, and a method for using mutual feedback for sample enlargement is introduced. Results are evaluated independently of domain knowledge within a clear machine learning problem definition. The whole concept presented is realized in a meta web search engine called OySTER.