Deborah F. Swayne

XGobi: Interactive Dynamic Data Visualization in the X Window System

Abstract XGobi is a data visualization system with state-of-the-art interactive and dynamic methods for the manipulation of views of data. It implements 2-D displays of projections of points and lines in high-dimensional spaces, as well as parallel coordinate displays and textual views thereof. Projection tools include dotplots of single variables, plots of pairs of variables, 3-D data rotations, various grand tours, and interactive projection pursuit. Views of the data can be reshaped. Points can be labeled and brushed with glyphs and colors. Lines can be edited and colored. Several XGobi processes can be run simultaneously and linked for labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns can be examined; multiple imputations can be given to XGobi for rapid visual diagnostics. XGobi includes an extensive online help facility. XGobi can be integrated in other software systems, as has been done for the data analysis language S, the geographic information system...

GGobi: evolving from XGobi into an extensible framework for interactive data visualization

GGobi is a direct descendent of a data visualization system called XGobi that has been around since the early 1990s. GGobis new features include multiple plotting windows, a color lookup table manager, and an Extensible Markup Language file format for data. Perhaps the biggest advance is that GGobi can be easily extended, either by being embedded in other software or by the addition of plugins; either way, it can be controlled using an Application Programming Interface. An illustration of its extensibility is that it can be embedded in R. The result is a full marriage between GGobis direct manipulation graphical environment and Rs familiar extensible environment for statistical data analysis.

Data Visualization With Multidimensional Scaling

We discuss methodology for multidimensional scaling (MDS) and its implementation in two software systems, GGvis and XGvis. MDS is a visualization technique for proximity data, that is, data in the form of N × N dissimilarity matrices. MDS constructs maps (“configurations,” “embeddings”) in IRk by interpreting the dissimilarities as distances. Two frequent sources of dissimilarities are high-dimensional data and graphs. When the dissimilarities are distances between high-dimensional objects, MDS acts as a (often nonlinear) dimension-reduction technique. When the dissimilarities are shortest-path distances in a graph, MDS acts as a graph layout technique. MDS has found recent attention in machine learning motivated by image databases (“Isomap”). MDS is also of interest in view of the popularity of “kernelizing” approaches inspired by Support Vector Machines (SVMs; “kernel PCA”). This article discusses the following general topics: (1) the stability and multiplicity of MDS solutions; (2) the analysis of structure within and between subsets of objects with missing value schemes in dissimilarity matrices; (3) gradient descent for optimizing general MDS loss functions (“Strain” and “Stress”); (4) a unification of classical (Strain-based) and distance (Stress-based) MDS. Particular topics include the following: (1) blending of automatic optimization with interactive displacement of configuration points to assist in the search for global optima; (2) forming groups of objects with interactive brushing to create patterned missing values in MDS loss functions; (3) optimizing MDS loss functions for large numbers of objects relative to a small set of anchor points (“external unfolding”); and (4) a non-metric version of classical MDS. We show applications to the mapping of computer usage data, to the dimension reduction of marketing segmentation data, to the layout of mathematical graphs and social networks, and finally to the spatial reconstruction of molecules.

Interactive and Dynamic Graphics for Data Analysis

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples. The book may be used as a text in a class on statistical graphics or exploratory data analysis, for example, or as a guide for the independent learner. Each chapter ends with a set of exercises. The authors are both Fellows of the American Statistical Association, past chairs of the Section on Statistical Graphics, and co-authors of the GGobi software. Dianne Cook is Professor of Statistics at Iowa State University. Deborah Swayne is a member of the Statistics Research Department at AT&T Labs.

Exploratory Visual Analysis of Graphs in GGOBI

Graphs have long been of interest in telecommunications and social network analysis, and they are now receiving increasing attention from statisticians working in other areas, particularly in biostatistics. Most of the visualization software available for working with graphs has come from outside statistics and has not included the kind of interaction that statisticians have come to expect. At the same time, most of the exploratory visualization software available to statisticians has made no provision for the special structure of graphs.

Understanding couch potatoes: measurement and modeling of interactive usage of IPTV at large scale

We investigate how consumers view content using Video on Demand (VoD) in the context of an IP-based video distribution environment. Users today can use interactive stream control functions such as skip, replay, fast-forward, pause, and rewind to control their viewing. The use of these functions can place additional demands on the distribution infrastructure (servers, network, and set top boxes) and can be challenging to manage with a large subscriber base. A model of user interaction provides insight into the impact of stream control on server and bandwidth requirements, client responsiveness, etc. We capture the activity users in a natural setting, viewing video at home. We first develop a model for the arrival process of requests for content. We then develop two stream control models that accurately capture user interaction. We show that stream control events can be characterized by a finite state machine and a sojourn time model, parametrized for major periods of usage (weekend and weekday). Our semi-Markov (SM) model for the sojourn time in each stream control state uses a novel technique based on a polynomial fit to the logarithm of the Inverse CDF. A second constrained model(CM) uses a stick-breaking approach familiar in machine learning to model the individual state sojourn time distributions. The SM model seeks to preserve the sojourn time distribution for each state while the CM model puts a greater emphasis on preserving the overall session duration distribution. Using traces across a period of 2 years from a large-scale operational IPTV environment, we validate the proposed model and show that we are able to faithfully predict the workload presented to a video server. We also provide a synthetic trace developed from the model enabling researchers to also study other problems of interest. We also use the techniques to model consumer viewing of video content recorded on their personal Digital Video Recorder (DVR).

Leveraging video viewing patterns for optimal content placement

As IP becomes the predominant choice for video delivery, storing the ever increasing number of videos for delivery will become a challenge. In this paper we focus on how to take advantage of user viewing patterns to place content in provider networks to reduce their storage and network utilization. We first characterize user viewing behavior using data collected from a nationally deployed Video-on-Demand service. We provide proof that users watch only a small portion of videos (not just for short clips, but even with full-length movies). We use this information and a highly flexible Mixed Integer Programming (MIP) formulation to solve the placement problem, in contrast to traditional popularity-based placement and caching strategy. We perform detailed simulations using real traces of user viewing sessions (including stream control operations such as Pause, Skip, etc.). Our results show that the use of a segmentbased placement yields substantial savings both in storage as well as network bandwidth. For example, compared to a simple caching scheme using full videos, our MIP-based placement using segments can achieve up to 71% reduction in peak link bandwidth usage.

Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment

We investigate the user viewing activity for broadcast TV, pre-recorded content using Digital Video Recording (DVR) and video on demand (VoD) in an IP-based content distribution environment. Advanced stream control functions (play, pause, skip, rewind, etc.) provide users with a high level of interactivity, but place demands on the distribution infrastructure (servers, network, home-network) that can be difficult to manage at large scale. To support system design as well as network capacity planning, it is necessary to have a good model of user interaction. Using traces from a well-provisioned operational environment with a large user population, we first characterize interactivity for broadcast TV, DVR and VoD. We then develop parametric models of individual users stream control operations for VoD. Our analysis shows that interactive behavior is adequately characterized by two semi-Markov models, one for weekdays and another for weekends. We propose a parametric model for the underlying sojourn time distributions and show that it results in a superior fit compared to well known distributions (generalized Pareto and Weibull). In order to validate that our models faithfully capture user behavior, we compare the workload that a VoD server experiences in response to actual traces and synthetic data generated from our proposed models.

Capacity requirements for on-demand IPTV services

Service providers are evolving to provide more video content on-demand. Customers like to watch a variety of entertainment content of their choice and at their convenience. Serving this ever-increasing base of on-demand viewers requires careful provisioning by the providers to accommodate for both scale and interactivity. In this paper, we examine long-term usage patterns of hundreds of thousands of consumers of a nationwide IPTV service, and confirm that viewers are indeed migrating to what is called “time-shifted” viewing of television programming and movies using digital video recorders or on-demand viewing. We also examine the impact of such users interactive control of their viewing experience using “stream control” functions (e.g., fast-forward, rewind, skip, replay, etc.) Through careful measurements on an IPTV server, we compute the load due to video streaming and handling these stream control events. We then extrapolate from these micro-benchmark measurements to predict the processing load imposed by users that would resort to using a “network-based” DVR capability if such a service were offered. We use both detailed trace-driven simulations and a simple operational-analysis based model to predict the capacity requirements of the server complex in a video-hub office to serve a large population of customers (e.g., a densely populated city like Mumbai). We provide insights on the number of requests serviced by the server, the average time to service these requests and the response time as perceived by the client.

Combining content analysis of television programs with audience measurement

Combining content analysis of television programs with quantitative audience measurement can provide insights into customer reactions to advertisements and program content. This work introduces a system architecture that incorporates anonymous audience metrics from an operational IPTV environment with metadata from a content-based analysis of recorded programs. Evaluated on a collection of news programs, the system verifies that events derived from the audience metrics data stream correspond to media segmentation boundaries such as commercial breaks and topic changes. An automated system for executing multimodal media segmentation algorithms for commercial break and topic change detection is also discussed. Better understanding of audience reaction can help IPTV service providers plan infrastructure investments and help in managing multimedia content delivery networks.