Today, multimedia system are still widely realized as monolithic systems. But building such applications using Service-Oriented Architectures -especially for the Processing and Delivery of continous Multimedia data streams -has been a controversial topic since years. As a result, applications in the multimedia domain cannot yet benefit from Web service architectures. Thus, building and maintaining large-scale multimedia applications remains a difficult, costly, time-consuming and challenging problem.In this paper we present our approach for building largescale multimedia systems and compare it with the current state of the art, concentrating on the selection and validation of multimedia service composition.

The high abundance of genetic information enables researchers to gain new insights from the comparison of human genes according to their similarities. However, existing tools that allow the exploration of such gene-to-gene relationships, apply each similarity independently. To make use of multidimensional scoring, we developed a new search engine named Genehopper. It can handle two query types: (i) the typical use case starts with a term-to-gene search, i.e. an optimized full-text search for an anchor gene of interest. The web-interface can handle one or more terms including gene symbols and identifiers of Ensembl, UniProt, EntrezGene and RefSeq. (ii) When the anchor gene is defined, the user can explore its neighborhood by a gene-to-gene search as the weighted sum of nine normalized gene similarities based on sequence homology, protein domains, mRNA expression profiles, Gene Ontology Annotation, gene symbols and other features. Each weight can be adjusted by the user, allowing flexible customization of the gene search. All implemented similarities have a low pairwise correlation (max r2 = 0.4) implying a low linear dependency, i.e. any change in a single weight has an effect on the ranking. Thus, we treated them as separate dimensions in the search space. Genehopper is freely available at http://genehopper.ifis.cs.tu-bs.de.

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.