Virtual reality technology is increasingly being applied to globally distributed teams engaged in collaborative product design. Observations of product design teams have suggested four distinct patterns of collaboration-complementary, competitive, peer-to-peer, and leader-follower. Another insight from observation is that collaboration consists of fluid transitions between these patterns in the accomplishment of the design task, driven by a flexible process of subgrouping and regrouping which reflects the structure and progress of the task. Yet most collaborative virtual environment systems support only one pattern of collaboration-peer-to-peer-and those that do explicitly support multiple patterns or roles do not allow fluid transitions between them in the context of the same task. In addition, no explicit support is provided to allow subgroups to be formed and dissolved. A collaborative virtual environment that supports multiple collaboration patterns and fluid transitions was developed using the Shared Simple Virtual Environment (SSVE) application framework. A novel user interface widget, the collaboration tree, was created to drive the subgrouping and regrouping process. Group experiments were performed to test the operating hypothesis that support for group collaboration patterns led to higher performance. The result was that the operating hypothesis was confirmed; however, the conceptual approach to problem solving, suggested by the presence of support for collaboration patterns, may have been more significant than the actual mechanism provided.

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The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. Unlike existing algorithms, D-HOTM requires neither full knowledge of the global schema nor that the distribution of data be horizontal or vertical. D-HOTM discovers rules based on higher-order associations between distributed database records containing the extracted entities. In this paper, two approaches to the definition and discovery of higher order itemsets are presented. The implementation of D-HOTM is based on the TMI [20] and tested on a cluster at the National Center for Supercomputing Applications (NCSA). Results on a real-world dataset from the Richmond, VA police department demonstrate the performance and relevance of D-HOTM in law enforcement and homeland defense.

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