Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients' emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

With the rise of digital content and web-based technologies, archaeologists and heritage organisations are increasingly striving to produce digital records of archaeology and heritage sites. The large numbers and geographical spread of these sites means that it would be too time-consuming for any one team to survey them. To meet this challenge, the Heritage Together project has developed a web platform through which members of the public can upload their own photographs of heritage assets to be processed into 3D models using an automated photogrammetry work flow. The web platform is part of a larger project which aims to capture, create and archive digital heritage assets in conjunction with local communities in Wales, UK, with a focus on megalithic monuments. Heritage Together is a digital community and community-built archive of heritage data, developed to inspire local communities to learn more about their heritage and to help to preserve it.

We present Topic View, an application for visually comparing and exploring multiple models of text corpora. Topic View uses multiple linked views to visually analyze both the conceptual content and the document relationships in models generated using different algorithms. To illustrate Topic View, we apply it to models created using two standard approaches: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. We demonstrate the utility of Topic View's visual approach to model assessment by comparing LSA and LDA models of two example corpora.

In this article we describe stress nets, a technique for exploring 2D tensor fields. Our method allows a user to examine simultaneously the tensors' eigenvectors (both major and minor) as well as scalar-valued tensor invariants. By avoiding noise-advection techniques, we are able to display both principal directions of the tensor field as well as the derived scalars without cluttering the display. We present a CPU-only implementation of stress nets as well as a hybrid CPU/GPU approach and discuss the relative strengths and weaknesses of each. Stress nets have been used as part of an investigation into crack propagation. They were used to display the directions of maximum shear in a slab of material under tension as well as the magnitude of the shear forces acting on each point. Our methods allowed users to find new features in the data that were not visible on standard plots of tensor invariants. These features disagree with commonly accepted analytical crack propagation solutions and have sparked renewed investigation. Though developed for a materials mechanics problem, our method applies equally well to any 2D tensor field having unique characteristic directions.

We present an incremental algorithm to compute image-based simplifications of a large environment. We use an optimization-based approach to generate samples based on scene visibility, and from each viewpoint create textured depth meshes (TDMs) using sampled range panoramas of the environment. The optimization function minimizes artifacts such as skins and cracks in the reconstruction. We also present an encoding scheme for multiple TDMs that exploits spatial coherence among different viewpoints. The resulting simplifications, incremental textured depth meshes (ITDMs), reduce preprocessing, storage, rendering costs and visible artifacts. Our algorithm has been applied to large, complex synthetic environments comprising millions of primitives. It is able to render them at 20 -- 40 frames a second on a PC with little loss in visual fidelity.

We introduce the notion of spatially encoded video and use it for efficiently representing image-based impostors for interactive walkthroughs. As part of a pre-process, we automatically decompose the model and compute the far-fields. The resulting texture images are organized along multiple dimensions and can be accessed in a user-steered order at interactive rates. Our encoding algorithm can compress the impostors size by two orders of magnitude. Furthermore, the storage cost for additional impostors or samples grows sub-linearly. The resulting system has been applied to a complex CAD environment composed of 13 million triangles. We are able to render it at interactive rates on a PC with little loss in image quality.

We present a new approach for faster rendering of large synthetic environments using video-based representations. We decompose the large environment into cells and pre-compute video based impostors using MPEG compression to represent sets of objects that are far from each cell. At runtime, we decode the MPEG streams and use rendering algorithms that provide nearly constant-time random access to any frame. The resulting system has been implemented and used for an interactive walkthrough of a model of a house with 260,000 polygons and realistic lighting and textures. It is able to render this model at 16 frames per second (an eightfold improvement over simpler algorithms) on average on a Pentium II PC with an off-the-shelf graphics card.

We present a system for rendering very complex 3D models at interactive rates.We select a subset of the model as preferred viewpoints and partition the space into virtual cells.Each cell contains near geometry, rendered using levels of detail and visibility culling, and far geometry, rendered as a textured depth mesh.Our system automatically balances the screen-space errors resulting from geometric simplification with those from textureddepth-mesh distortion.We describe our prefetching and data management schemes, both crucial for models significantly larger than available system memory.We have successfully used our system to accelerate walkthroughs of a 13 million triangle model of a large coal-fired power plant and of a 1.7 million triangle architectural model.We demonstrate the walkthrough of a 1.3 GB power plant model with a 140 MB cache footprint.