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Sensemaking is the process through which humans put together understanding of related information. Sensemaking has been said to involve changes in cognitive representations during a human information processing task. Collection sensemaking involves understanding a collection of media entities, as a whole. One example of a sensemaking task is to compare the damage from Hurricane Katrina to homes, personal effects, and community buildings in different areas of New Orleans. Connected visual and semantic representations provide perspective to support users involved in collection sensemaking tasks. A zoomable map organizes images based on location at varying scales. Multiscale clusters based on zoom level organize images associated with events. The clusters afford contextualized thumbnail browsing and also maintain uniform information density on the map. Metadata enhances context and memory in the process of collection sensemaking.

Our community guide allows you to set up your own MPI (Message Passing Interface) based supercomputer cluster with the Playstation 3.

Using segments of rich media makes it possible to aggregate context and meaning on these chunks by using a number of different mechanisms. Starting with a granular node -- be it a sound bite, visual clip or written fact -- it is possible to aggregate contextual metadata through a series of steps that emergently progress from: * Starting with thousands of defined Audio Sound Bites & visual clips * Rating sound bites and clustering them with folksonomy tags * Sequencing audio sound bites within playlists * Collaboratively building larger sequences with nested playlists * Independently controlling the video & audio tracks with 2-dimensional nested playlists * Evaluating Multiple Storylines and Hypotheses with a 2-dimensional playlist matrix * Visualizing complex networks by mapping out feedback loop relationships between nodes

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. UIMA is a framework and SDK for developing such applications. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at. UIMA enables such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages. UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

MarkMail is a free service for searching mailing list archives, with huge advantages over traditional search engines. It is powered by MarkLogic Server: Each email is stored internally as an XML document, and accessed using XQuery. All searches, faceted navigation, analytic calculations, and HTML page renderings are performed by a small MarkLogic Server cluster running against millions of messages.

Corrib.org is a cluster project, has been started on the base of cooperation between DERI International and Gdansk University of Technology. So far, it gathers together both open source projects being developed based on that cooperations and people affiliated to different sites.