The delivery of multimedia content often needs the adaptation of the content in order to satisfy user constraints. With the Digital Item Adaptation part, the MPEG-21 standard already defines a useful frame-work to handle this task. However, in modern service-oriented architectures the functionality of adaptation is split over several services. Hence, the central instantiation of a suitable service chain needs to tackle a complex multiobjective optimization problem. In this problem between content choice and possible adaptations the current preference model in the MPEG-7/21 standard still lacks expressiveness. In the course of this paper we demonstrate this shortcoming and how the integration of more powerful models can ease the instantiation problem. Furthermore we explain how to efficiently evaluate preference trade-offs by evaluating skyline queries as currently investigated in the field of information systems. As a running example we use preference-based content adaptation in a typical media streaming application with Web services as basic modules. The contribution of our framework is to enable a central coordinator to instantiate an executable service composition chain by integrating all needed Web services to adapt the multimedia content in the best possible fashion in the sense of Pareto optimality.

The most important goal for digital libraries is to ensure high quality search experience for all kinds of users. To attain this goal, it is necessary to have as much relevant metadata as possible at hand to assess the quality of publications. Recently, a new group of metrics appeared, that has the potential to raise the quality of publication metadata to the next level -the altmetrics. These metrics try to reflect the impact of publications within the social web. However, currently it is still unclear if and how altmetrics should be used to assess the quality of a publication and how altmetrics are related to classical bibliographical metrics (like e.g. citations). To gain more insights about what kind of concepts are reflected by altmetrics, we conducted an in-depth analysis on a real world dataset crawled from the Public Library of Science (PLOS). Especially, we analyzed if the common approach to regard the users in the social web as one homogeneous group is sensible or if users need to be divided into diverse groups in order to receive meaningful results.

Abstract

In recent years, the vast amount of digitally available content has lead to the creation of many topic-centered digital libraries. Also in the domain of chemistry more and more digital collections are available, but the complex query formulation still hampers their intuitive adoption. This is because information seeking in chemical documents is focused on chemical entities, for which current standard search relies on complex structures which are hard to extract from documents. Moreover, although simple keyword searches would often be sufficient, current collections simply cannot be indexed by Web search providers due to the ambiguity of chemical substance names. In this paper we present a framework for automatically generating metadata-enriched index pages for all documents in a given chemical collection. All information is then linked to the respective documents and thus provides an easy to crawl metadata repository promising to open up digital chemical libraries. Our experiments, indexing an open access journal, show that not only the documents can be found using a simple Google search via the automatically created index pages, but also that the quality of the search is much more efficient than fulltext indexing in terms of both precision/recall and performance. Finally, we compare our indexing against a classical structure search and figured out that keyword-based search can indeed solve at least some of the daily tasks in chemical workflows. To use our framework thus promises to expose a large part of the currently still hidden chemical Web, making the techniques employed interesting for chemical information providers like digital libraries and open access journals.

Today, Web pages are usually accessed using text search engines, whereas documents stored in the deep Web are accessed through domain-specific Web portals. These portals rely on external knowledge bases, respectively ontologies, mapping documents to more general concepts allowing for suitable classifications and navigational browsing. Since automatically generated ontologies are still not satisfactory for advanced information retrieval tasks, most portals heavily rely on hand-crafted domain-specific ontologies. This, however, also leads to high creation and maintaining costs. On the other hand, a freely available community maintained, if somewhat general, knowledge base is offered by Wikipedia. During the last years the coverage of Wikipedia has reached a large pool of information including articles from almost all domains. In this paper, we investigate the use of Wikipedia categories to describe the content of chemical documents in a compact form. We compare the results to the domain-specific ChEBI ontology and the results show that Wikipedia categories indeed allow useful descriptions for chemical documents that are even better than descriptions from the ChEBI ontology.