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.
Keywords: Information visualization, information landscape, incremental clustering, multi-dimensional scaling