Scalable Annotation Mechanisms for Digital Content Aggregation and Context-Aware Authoring

This paper discusses the role of context information in building the next generation of human-centered information systems, and classifies the various aspects of contextualization with a special emphasis on the production and consumption of digital content. The real-time annotation of resources is a crucial element when moving from content aggregators (which process third-party digital content) to context-aware visual authoring environments (which allow users to create and edit their own documents). We present a publicly available prototype of such an environment, which required a major redesign of an existing Web intelligence and media monitoring framework to provide real-time data services and synchronize the text editor with the frontend’s visual components. The paper concludes with a summary of achieved results and an outlook on possible future research avenues including multi-user support and the visualization of document evolution

Extraction and Interactive Exploration of Knowledge from Aggregated News and Social Media Content

The webLyzard media monitoring and Web intelligence platform (www.webLyzard.com) presented in this paper is a flexible tool for assessing the positioning of an organization and the effectiveness of its communications. The platform aggregates large archives of digital content from multiple stakeholders. Each week it processes millions of documents and user comments from news media, blogs, Web 2.0 platforms such as Facebook, Twitter and YouTube, and the Web sites of companies and NGOs. An interactive dashboard with trend charts and complex map projections shows how often and where information is published. It also provides a real-time account of topics that stakeholders associate with an organization. Positive or negative sentiment is computed automatically, which reflects the impact of public relations and marketing campaigns

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes

Incremental and Scalable Computation of Dynamic Topography Information Landscapes

Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to be processed by state-of-the-art Web intelligence applications, we present a scalable, incremental approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Processing steps central to incremental processing are found in the projection stage which consists of document clustering, cluster force-directed placement, and fast document positioning. We introduce two different positioning methods and compare them in an incremental setting using two different quality measures. The evaluation is performed on a set of approximately 5000 documents taken from the environmental blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate), a Web content aggregator about climate change and related environmental issues that serves static versions of the information landscapes presented in this paper as part of a multiple coordinated view representation