The DIADEM Software Development Methodology extended to Multimedia Interfaces

DIADEM, created by THOMSON-CSF, is a methodology for specifying and developing user interfaces. It improves productivity of the interface development process as well as quality of the interface. The method provides support to user interface development in three aspects. (1) DIADEM defines roles of people involved and their tasks and organises the sequence of activities. (2) It provides graphical formalisms supporting information exchange between people. (3) It offers a basic set of rules for optimum human-machine interfaces. The use of DIADEM in three areas (process control, sales support, and multimedia presentation) was observed and evaluated by our laboratory in the European project DIAMANTA (ESPRIT P20507). The method provides an open procedure that leaves room for adaptation to a specific application and environment. This paper gives an overview of DIADEM and shows how to extend formalisms for developing multimedia interfaces.

Evaluation of Auditory Displays Supporting Aircraft Approach and Landing

Abstract: The paper describes an auditory interface using directional sound as a possible support for pilots during approach in an instrument landing scenario. Several ways of producing directional sounds are illustrated. One using speaker pairs and controlling power distribution between speakers is evaluated experimentally. Results show, that power alone is insufficient for positioning single isolated sound events, although discrimination in the horizontal plane performs better than in the vertical. Additional sound parameters to compensate for this are proposed.

Supporting co-operative work in apron control and resource management offices on airports

A high demand exists to increase the efficiency of present airport ground facilities and the co-ordination of traffic and services. The Traffic Office plays a crucial role in managing the airport. The main tasks of the Traffic Office is management of equipment, services, and ressources based on the flight schedule and resolving conflicts arising from deviations from the schedule. A new tool will support information exchange between Traffic Office and other facilities on the airport.

Quality Criteria for Enterprise Modelling in the context of networked Enterprises

The capability for collaboration is a key success factor for networked enterprises. The paper introduces a methodology supporting the application of Enterprise Modelling in order to improve the maturity for collaboration. The methodology considers the current status of maturity for interoperability for deducing the right modelling approach. The approach is combined with quality criteria of the models in order to guide the modelling process. Both the deducing approach and the quality criteria are related to the levels of interoperability proposed by the ATHENA Interoperability Framework.

Enterprise Engineering Method supporting Six Sigma Approach

Enterprise Modeling (EM) is currently in operation either as a technique to represent and understand the structure and behavior of the enterprise, or as a technique to analyze business processes, and in many cases as support technique for business process reengineering. However, EM architectures and methodes for Enterprise Engineering can also used to support new management techniques like SIX SIGMA, because these new techniques need a clear, transparent and integrated definition and description of the business activities of the enterprise to be able to build up, to optimize and to operate an successful enterprise.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.