Birgitta König-Ries

DIANE: an integrated approach to automated service discovery, matchmaking and composition

Automated matching of semantic service descriptions is the key to automatic service discovery and binding. But when trying to find a match for a certain request it may often happen, that the request cannot be serviced by a single offer but could be handled by combining existing offers. In this case automatic service composition is needed. Although automatic composition is an active field of research it is mainly viewed as a planning problem and treated separatedly from service discovery. In this paper we argue that an integrated approach to the problem is better than seperating these issues as is usually done. We propose an approach that integrates service composition into service discovery and matchmaking to match service requests that ask for multiple connected effects, discuss general issues involved in describing and matching such services and present an efficient algorithm implementing our ideas.

What is needed for semantic service descriptions? A proposal for suitable language constructs

The big promise of service-oriented computing is the ability to form agile networks. Agile networks are networks of loosely coupled participants that cooperate by dynamically discovering and invoking each others services at run-time. The major prerequisite for this promise to be fulfilled is an appropriate semantic service description. In this paper, we identify requirements towards such a service description language and show that neither of the two main current approaches, OWL-S and WSMO, is able to fully meet these requirements. We then proceed to suggest additional language constructs and a prototypical language, the DIANE Service Description (DSD), which implements these constructs. We explain how service offers and requests can be described and matched using DSD.

Coupled signature and specification matching for automatic service binding

Matching of semantic service descriptions is the key to automatic service discovery and binding. Existing approaches split the matchmaking process in two step: signature and specification matching. However, this leads to the problem that offers are not found although they are functionally suitable if their signature is not fitting the requested one. Therefore, in this paper, we propose a matching algorithm that does not use a separated and explicit signature matching step, but derives the necessary messages from the comparison of pre- and postconditions. As a result, the algorithm not only finds all functionally suitable services even if their signatures do not match, but also is able to derive the messages needed for an automatic invocation.

Progressor: social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.

DIANE: A Matchmaking-Centered Framework for Automated Service Discovery, Composition, Binding, and Invocation on the Web

Service-oriented computing will allow for the automatic discovery, composition, binding, and invocation of Web services. The single most important component of this goal is appropriate matchmaking. This paper presents a service-description language and its associated matchmaking algorithms. Together they precisely capture requester preferences through fuzzy sets, express and use instance information for matchmaking, and deal efficiently with multiple effects. The approach described here has been extensively evaluated both in experiments and in the 2006 Semantic Web Services Challenge.

Multi-layer Clusters in Ad-hoc Networks - An Approach to Service Discovery

One of the core functionalities needed in ad-hoc peer-to-peer networks is service discovery. However, none of the existing solutions for service discovery work well in these dynamic, decentralized environments. Therefore, in this paper, we propose a new approach to service discovery which is based on the dynamic organization of the services into multilayer clusters. These clusters are formed based on both physical and semantic proximity.

Evaluation of Semantic Service Discovery—A Survey and Directions for Future Research

In recent years a huge amount of effort, and money has been invested in the area of semantic service discovery and presented approaches have become more sophisticated and mature. Nevertheless surprisingly little effort is being put into the evaluation of these approaches. We argue that the lack of established and theoretically well-founded methodologies and test beds for comparative evaluation of semantic service discovery is a major blocker of the advancement of the field. To lay the ground for a comprehensive treatment of this problem we discuss the applicability of well-known evaluation methodologies from information retrieval and provide an exhaustive survey of the current evaluation approaches.

An approach to controlling user models and personalization effects in recommender systems

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the users previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

Open social student modeling: visualizing student models with parallel introspectiveviews

This paper explores a social extension of open student modeling that we call open social student modeling. We present a specific implementation of this approach that uses parallel IntrospectiveViews to visualize models representing student progress with QuizJET parameterized self-assessment questions for Java programming. The interface allows visualizing not only the students own model, but also displaying parallel views on the models of their peers and the cumulative model of the entire class or group. The system was evaluated in a semester-long classroom study. While the use of the system was non-mandatory, the parallel IntrospectiveViews interface caused an increase in all of the usage parameters in comparison to a regular portal-based access, which allowed the student to achieve a higher success rate in answering the questions. The collected data offer some evidence that a combination of traditional personalized guidance with social guidance was more effective than personalized guidance alone.

IntrospectiveViews: an interface for scrutinizing semantic user models

User models are a key component for user-adaptive systems They represent information about users such as interests, expertise, goals, traits, etc This information is used to achieve various adaptation effects, e.g., recommending relevant documents or products To ensure acceptance by users, these models need to be scrutable, i.e., users must be able to view and alter them to understand and if necessary correct the assumptions the system makes about the user However, in most existing systems, this goal is not met In this paper, we introduce IntrospectiveViews, an interface that enables the user to view and edit her user model Furthermore, we present the results of a formative evaluation that show the importance users give in general to different aspects of scrutable user models and also substantiate our claim that IntrospectiveViews is an appropriate realization of an interface to such models.