Scatterplots are commonly used to visualize multidimensional data; however, 2D projections of data offer limited understanding of the high-dimensional interactions between data points. We introduce an interactive 3D extension of scatterplots called the Regression Cube (RC), which augments a 3D scatterplot with three facets on which the correlations between the two variables are revealed by sensitivity lines and sensitivity streamlines. The sensitivity visualization of local regression on the 2D projections provides insights about the shape of the data through its orientation and continuity cues. We also introduce a series of visual operations such as clustering, brushing, and selection supported in RC. By iteratively refining the selection of data points of interest, RC is able to reveal salient local correlation patterns that may otherwise remain hidden with a global analysis. We have demonstrated our system with two examples and a user-oriented evaluation, and we show how RCs enable interactive visual exploration of multidimensional datasets via a variety of classification and information retrieval tasks. A video demo of RC is available.

This paper studies distribution of product model via peer-to-peer 3D CAD streaming in networked collaborative design. An essential operation of data transfer during the streaming process is that a participant incrementally receives a product model in some level of detail (LOD) from multiple collaborators. This operation requires an effective plan of data relay routing and sequencing. An optimization scheme combining the Dijkstra's algorithm and heuristic rules is proposed to minimize the data transfer time consumed by the operation. The implementation results show that such optimization functionality can significantly improve the efficiency of design information sharing via the 3D CAD streaming.

Scatterplots remain a powerful tool to visualize multidimensional data. However, accurately understanding the shape of multidimensional points from 2D projections remains challenging due to overlap. Consequently, there are a lot of variations on the scatterplot as a visual metaphor for this limitation. An important aspect often overlooked in scatterplots is the issue of sensitivity or local trend, which may help in identifying the type of relationship between two variables. However, it is not well known how or what factors influence the perception of trends from 2D scatterplots. To shed light on this aspect, we conducted an experiment where we asked people to directly draw the perceived trends on a 2D scatterplot. We found that augmenting scatterplots with local sensitivity helps to fill the gaps in visual perception while retaining the simplicity and readability of a 2D scatterplot. We call this augmentation the generalized sensitivity scatterplot (GSS). In a GSS, sensitivity coefficients are visually depicted as flow lines, which give a sense of continuity and orientation of the data that provide cues about the way data points are scattered in a higher dimensional space. We introduce a series of glyphs and operations that facilitate the analysis of multidimensional data sets using GSS, and validate with a number of well-known data sets for both regression and classification tasks.

T his paper concerns product model distribution in network-based collaborative design based on a peer-to-peer 3D streaming method. CAD meshes are transmitted incrementally from a coarse representation to a finer one in a sequence of LODs (Levels of Detail) defined by form features. A scenario of online design collaboration demonstrates how the streaming method facilitates CAD data sharing in real time. In this scenario, one peer is requesting a LOD model concurrently from multiple peers who own the model in different LODs. An optimization scheme combining Dijkstra’s Algorithm with simple heuristics helps determine how the requested model is aggregated from the ones possessed by the others and the transmission routes of individual LOD data. The objective is to minimize the total transfer time, considering the role of each peer in the collaboration and network throughputs. A prototyping system is implemented to verify the streaming method. The implementation result shows that it improves the efficiency and practicality of sharing CAD data of large size in networked engineering collaboration.