The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.

Current visual analytics systems provide users with the means to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time. Analysts search for events of interest through statistical tools linked to visual displays, drill down into the data, and form hypotheses based upon the available information. However, current systems stop short of predicting events. In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases where hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data are distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.

Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences.

New source-level models for aggregated HTTP traffic and a design for their integration with the TCP transport layer are built and validated using two large-scale collections of TCP/IP packet header traces. An implementation of the models and the design in the ns network simulator can be used to generate web traffic in network simulations

TCP start times for HTTP are nonstationary. The nonstationarity occurs because the start times on a link, a point process, are a superposition of source traffic point processes, and the statistics of superposition changes as the number of superposed processes changes. The start time rate is a measure of the number of traffic sources. The univariate distribution of the inter-arrival times is approximately Weibull, and as the rate increases, the Weibull shape parameter goes to 1, an exponential distribution. The autocorrelation of the log inter-arrival times is described by a simple, two-parameter process: white noise plus a long-range persistent time series. As the rate increases, the variance of the persistent series tends to zero, so the log times tend to white noise. A parsimonious statistical model for log inter-arrivals accounts for the autocorrelation, the Weibull distribution, and the nonstationarity in the two with the rate. The model, whose purpose is to provide stochastic input to a network simulator, has the desirable property that the superposition point process is generated as a single stream. The parameters of the model are functions of the rate, so to generate start times, only the rate is specified. As the rate increases, the model tends to a Poisson process. These results arise from theoretical and empirical study based on the concept of connection-rate superposition. The theory is the mathematics of superposed point processes, and the empiricism is an analysis of 23 million TCP connections organized into 10704 blocks of approximately 15 minutes each.