We describe an attempt to overcome information overload through information visualization -in a particular domain, group memory. A brief review of information visualization is followed by a brief description of our methodology. We Ž . discuss our system, which uses multidimensional scaling MDS to visualize relationships between documents, and which Ž . we tested on 60 subjects, mostly students. We found three important and statistically significant differences between task performance on an MDS-generated display and on a randomly generated display. With some qualifications, we conclude that MDS speeds up and improves the quality of manual classification of documents and that the MDS display agrees with subject perceptions of which documents are similar and should be displayed together. q

Augmented fabrication is the practice of designing and fabricating an artifact to work with existing objects. Although common both in the wild and as an area for research tools, little is known about how novices approach the task of designing under the constraints of interfacing with real-world objects. In this paper, we report the results of a study of fifteen novice end users in an augmented fabrication design task. We discuss obstacles encountered in four contexts: capturing information about physical objects, transferring information to 3D modeling software, digitally modeling a new object, and evaluating whether the new object will work when fabricated. Based on our findings, we suggest how future tools can better support augmented fabrication in each of these contexts. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI.

B usiness organizations are generating growing volumes of data about their employees, customers, and suppliers. Much of these data cannot be exploited for business value due to privacy and confidentiality concerns. National statistical agencies share sensitive data collected from individuals and businesses by modifying the data so individuals and firms cannot be identified but statistical utility is preserved. We build on this literature to develop a hybrid approach to data masking for business organizations. We demonstrate the validity of the hybrid approach, which we call multiple imputation with multimodal perturbation (MIMP), using Monte Carlo simulation and illustrate its application in a specific business context. Results of our analysis open new areas of research for information systems scholarship and new potential revenue sources for business organizations.

Summarization and visualization tools are believed to be helpful in navigating through large volumes of data since a visual representation may elicit more deliberate query reformulation and better feedback to the retrieval system. Results from improved feedback explain the growing interest in visualization tools in academic and industrial research (for example, Scatter/Gather by Xerox, ThemeMedia by Batelle, Live Topics by AltaVista).Most summarization and visualization tools rely on the ability of a computer to cluster documents or terms and visualize relationships among them.Prior work has shown that automatically generated concepts and clusters sometimes fall short of user expectations and do not reliably facilitate information access.We demonstrate our prototype system with novel features to overcome these problems. In addition to navigation of clusters of documents and concepts, our prototype adds customization of concepts, forming new clusters adapted to the particular user and task.Our prototype acts as a visualizing layer between the user and a commercial Web search engine. We currently connect to AltaVista, but remain independent of any specific search engine features. Our system summarizes search results and suggests additional terms for query modification. Based on user feedback, the system can create rank ordered lists of found URLs.Our prototype system uses Kohonen's self-organizing map (SOM), an unsupervised two-layer neural network. Our current system extends the one we presented at ACM SIGIR 97. Figure 1 shows the user/system/search engine interaction. The search follows these steps:1. The user enters a query on an HTML form. 2. Our system, ASOM, implemented as a CGI script, routes the query to an external search engine (AltaVista in our prototype).3. The search engine returns a ranked list of urls and their short summaries (called snippets) to ASOM.

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