From economics to sports to entertainment and social media, ranking objects according to some notion of importance is a fundamental tool we humans use all the time to better understand our world. With the ever-increasing amount of user-generated content found online, "what's trending" is now a commonplace phrase that tries to capture the zeitgeist of the world by ranking the most popular microblogging hashtags in a given region and time. However, before we can understand what these rankings tell us about the world, we need to be able to more easily create and explore them, given the significant scale of today's data. In this paper, we describe the computational challenges in building a real-time visual exploratory tool for finding top-ranked objects; build on the recent work involving in-memory and rank-aware data cubes to propose TOPKUBE: a data structure that answers top-k queries up to one order of magnitude faster than the previous state of the art; demonstrate the usefulness of our methods using a set of real-world, publicly available datasets; and provide a new set of benchmarks for other researchers to validate their methods and compare to our own.

Consider real-time exploration of large multidimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in the data? Answering these questions requires aggregation over arbitrary regions of the domain and attributes of the data. Many relational databases implement the well-known data cube aggregation operation, which in a sense precomputes every possible aggregate query over the database. Data cubes are sometimes assumed to take a prohibitively large amount of space, and to consequently require disk storage. In contrast, we show how to construct a data cube that fits in a modern laptop's main memory, even for billions of entries; we call this data structure a nanocube. We present algorithms to compute and query a nanocube, and show how it can be used to generate well-known visual encodings such as heatmaps, histograms, and parallel coordinate plots. When compared to exact visualizations created by scanning an entire dataset, nanocube plots have bounded screen error across a variety of scales, thanks to a hierarchical structure in space and time. We demonstrate the effectiveness of our technique on a variety of real-world datasets, and present memory, timing, and network bandwidth measurements. We find that the timings for the queries in our examples are dominated by network and user-interaction latencies.

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.

Simulations that require massive amounts of computing power and generate tens of terabytes of data are now part of the daily lives of scientists. Analyzing and visualizing the results of these simulations as they are computed can lead not only to early insights but also to useful knowledge that can be provided as feedback to the simulation, avoiding unnecessary use of computing power. Our work is aimed at making advanced visualization tools available to scientists in a user-friendly, Web-based environment where they can be accessed anytime from anywhere. In the context of turbulent combustion for example, visualization is used to understand the coupling between turbulence and the turbulent mixing of scalars. Although isosurface generation is a useful technique in this scenario, computing and rendering isosurfaces one at a time is expensive and not particularly well-suited for such a Web-based framework. In this paper we propose the use of a summary structure, called contour tree, that captures the topological structure of a scalar field and guides the user in identifying useful isosurfaces. We have also designed an interface which has been integrated with a Web-based simulation monitoring system, that allows users to interact with and explore multiple isosurfaces.

Visualization is essential for understanding the increasing volumes of digital data. However, the process required to create insightful visualizations is involved and time consuming. Although several visualization tools are available, including tools with sophisticated visual interfaces, they are out of reach for users who have little or no knowledge of visualization techniques and/or who do not have programming expertise. In this paper, we propose VisMashup, a new framework for streamlining the creation of customized visualization applications. Because these applications can be customized for very specific tasks, they can hide much of the complexity in a visualization specification and make it easier for users to explore visualizations by manipulating a small set of parameters. We describe the framework and how it supports the various tasks a designer needs to carry out to develop an application, from mining and exploring a set of visualization specifications (pipelines), to the creation of simplified views of the pipelines, and the automatic generation of the application and its interface. We also describe the implementation of the system and demonstrate its use in two real application scenarios.