We studied files stored by members of a work organization for patterns of social commonality. Discovering identical or similar documents, applications, developer libraries, or other files may suggest shared interests or experience among users. Examining actual file data revealed a number of individual and aggregate practices around file storage. For example, pairs of users typically have many (over 13,000) files in common. A prototype called LiveWire exploits this commonality to make file backup and restore more efficient for a work organization. We removed commonly shared files and focused on specific filetypes that represent user activity to find more meaningful files in common. The Consolidarity project explores how patterns of file commonality could encourage social networking in an organizational context. Mechanisms for addressing the privacy concerns raised by this approach are discussed.

Instant messaging (IM) in the workplace has proven to be a valuable tool for facilitating informal communication. Its benefits, however, are generally limited to times when users are in front of their computers. Because so much work takes place while people are mobile within their workplace, we sought to extend the benefits of IM beyond people's personal machines and into publicly accessible groupware. We first conducted a study of large display groupware applications (LDGAs) to understand the affordances that large displays offer for groupware, and the factors surrounding their adoption. We developed the IM Here system for shared IM on large displays using the lessons learned from the study. In this paper, we present the findings of our LDGA study, the design of IM Here and the preliminary results of our evaluation of IM as a public resource for workgroups.

In a ten-session experiment, six participants practiced typing with an expanding rehearsal method on an optimized virtual keyboard. Based on a large amount of in-situ performance data, this paper reports the following findings. First, the Fitts-digraph movement efficiency model of virtual keyboards is revised. The format and parameters of Fitts' law used previously in virtual keyboards research were incorrect. Second, performance limit predictions of various layouts are calculated with the new model. Third, learning with expanding rehearsal intervals for maximum memory benefits is effective, but many improvements of the training algorithm used can be made in the future. Finally, increased visual load when typing previously practiced text did not significantly change users' performance at this stage of learning, but typing unpracticed text did have a performance effect, suggesting a certain degree of text specific learning when typing on virtual keyboards