Abon Chaudhuri

Scalable parallel building blocks for custom data analysis

We present a set of building blocks that provide scalable data movement capability to computational scientists and visualization researchers for writing their own parallel analysis. The set includes scalable tools for domain decomposition, process assignment, parallel I/O, global reduction, and local neighborhood communicationtasks that are common across many analysis applications. The global reduction is performed with a new algorithm, described in this paper, that efficiently merges blocks of analysis results into a smaller number of larger blocks. The merging is configurable in the number of blocks that are reduced in each round, the number of rounds, and the total number of resulting blocks. We highlight the use of our library in two analysis applications: parallel streamline generation and parallel Morse-Smale topological analysis. The first case uses an existing local neighborhood communication algorithm, whereas the latter uses the new merge algorithm.

Exploring vector fields with distribution-based streamline analysis

Streamline-based techniques are designed based on the idea that properties of streamlines are indicative of features in the underlying field. In this paper, we show that statistical distributions of measurements along the trajectory of a streamline can be used as a robust and effective descriptor to measure the similarity between streamlines. With the distribution-based approach, we present a framework for interactive exploration of 3D vector fields with streamline query and clustering. Streamline queries allow us to rapidly identify streamlines that share similar geometric features to the target streamline. Streamline clustering allows us to group together streamlines of similar shapes. Based on users selection, different clusters with different features at different levels of detail can be visualized to highlight features in 3D flow fields. We demonstrate the utility of our framework with simulation data sets of varying nature and size.

Efficient Range Distribution Query for Visualizing Scientific Data

Visualization applications implicitly run queries on the data to retrieve distributions and statistical measures derivable from distributions. Distribution based data summaries can substitute for the raw data to answer statistical queries of different kinds. However, frequent access to the raw data is no longer practical, if possible at all, for answering large number of queries on large-scale data. Our work addresses the issue by accelerating range distribution query, which returns the distribution of an axis-aligned query region. Maintaining the interactivity of such query is a challenging task because the workload and the response time of such queries scale up with the data and the query size. In this paper, we present a framework for answering range distribution queries for any arbitrary region in near constant time, regardless of data and query size. We adapt an integral histogram based data structure to bound the workload which is a combination of computation, I/O and communication cost. We propose two novel transformations of this data structure -- a decomposition and a similarity-driven indexing -- to reduce the huge storage cost associated with it. In addition to studying the performance of range distribution query, we also demonstrate the benefits that our technique offers to visualization applications which directly or indirectly require distributions.

Scalable computation of distributions from large scale data sets

As we approach the era of exascale computing, the role of distributions to summarize, analyze and visualize large scale data is becoming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed framework for computing histograms from scalar and vector data at different levels of detail required by various types of analysis algorithms. We present efficient parallel techniques for histogram computation from regular as well as rectilinear grid data. We also study a technique called cross-validation to estimate the quality of computed histograms as a model of the actual data distribution. We parallelize cross-validation in a scalable manner to support histogram evaluation and selection of histogram parameters such as number of bins. We also present our distributed software framework for supporting science applications which require large scale distribution-based data analysis. The presented case studies highlight how the proposed algorithms and the related software benefit information theoretic and other distribution-driven analysis.

A self-adaptive treemap-based technique for visualizing hierarchical data in 3D

In this paper, we present a novel adaptive visualization technique where the constituting polygons dynamically change their geometry and other visual attributes depending on user interaction. These changes take place with the objective of conveying required level of detail to the user through each view. Our proposed technique is successfully applied to build a treemap-based but 3D visualization of hierarchical data, a widely used information structure. This new visualization exploits its adaptive nature to address issues like cluttered display, imperceptible hierarchy, lack of smooth zoom-in and out technique which are common in tree visualization. We also present an algorithm which utilizes the flexibility of our proposed technique to deal with occlusion, a problem inherent in any 3D information visualization. On one hand, our work establishes adaptive visualization as a means of displaying tree-structured data in 3D. On the other, it promotes the technique as a potential candidate for being employed to visualize other information structures also.

Exploring Flow Fields Using Space-Filling Analysis of Streamlines

Large scale scientific simulations frequently use streamline based techniques to visualize flow fields. As the shape of a streamline is often related to some underlying property of the field, it is important to identify streamlines (or their parts) with unique geometric features. In this paper, we introduce a metric, called the box counting ratio, which measures the geometric complexity of streamlines by measuring their space-filling capacity at different scales. We propose a novel interactive visualization framework which utilizes this metric to extract, organize and visualize features of varying density and complexity hidden in large numbers of streamlines. The proposed framework extracts complex regions of varying density from the streamlines, and organizes and presents them on an interactive 2D information space, allowing user selection and visualization of streamlines. We also extend this framework to support exploration using an ensemble of measures including box counting ratio. Our framework allows the user to easily visualize and interact with features otherwise hidden in large vector field data. We strengthen our claims with case studies using combustion and climate simulation data sets.

CycleStack: Inferring periodic behavior via temporal sequence visualization in ultrasound video

A range of well-known treatment methods for destroying tumor and similar harmful growth in human body utilizes the coherence between the inherently periodic movement of the affected body part and periodic respiratory signal of the patient, with the objective of minimizing damage to surrounding normal tissues. Such methods require constant monitoring by an operator who observes the 3D body motion via its 2D projection onto an ultrasound imaging plane and studies the synchronism of this motion with the respiratory signal. Keeping an attentive eye on the respiratory signal as well as the ultrasound video for the entire treatment period is often inconvenient and burdensome. In this paper, we propose a video visualization technique called CycleStack Plot which reduces this cognitive overhead by blending the video and the signal together in a stack-like layout. This visualization reveals the inherent synchronism between the targets movement and the respiratory signal, visually highlights significant phase shifts of either of the two cyclic phenomena, with the hope of arresting the operators attention. Our proposed visualization also provides a visual overview for the post-treatment analysis which enables educated users to quickly and effectively skim through the excessively long process. This paper demonstrates the utility of CycleStack Plot with a case study using real ultrasound videos. In addition, a user study has been performed to evaluate the merits and limitations of the proposed method with respect to the conventional way of watching a video and a signal side-by-side. Even though the motivation of the proposed visualization is improvement of medical applications that use ultrasound, the core techniques discussed here have potential to be extended to other application domains requiring analysis of cyclic patterns from videos.

A self-adaptive technique for visualizing geospatial data in 3D with minimum occlusion

Geospatial data are often visualized as 2D cartographic maps with interactive display of detail on-demand. Integration of the 2D map, which represents high level information, with the location-specific detailed information is a key design issue in geovisualization. Solutions include multiple linked displays around the map which can impose cognitive load on the user as the number of links goes up; and separate overlaid windowed displays which causes occlusion of the map. In this paper, we present a self-adaptive technique which reveals the hidden layers of information in a single display, but minimizes occlusion of the 2D map. The proposed technique creates extra screen space by invoking controlled deformation of the 2D map. We extend our method to allow simultaneous display of multiple windows at different map locations. Since our technique is not dependent on the type of information to display, we expect it to be useful to both common users and the scientists. Case studies are provided in the paper to demonstrate the utility of the method in occlusion management and visual exploration.

Efficient range distribution query in large-scale scientific data

Frequent access to raw data is no longer practical, if possible at all, for answering queries on large-scale data. This has led to the use of distribution-based data summaries, which can substitute for raw data to answer statistical queries of different kinds. Our work is concerned with range distribution query, which returns the distribution of an axis-aligned region of any size. We address the challenge of maintaining the interactivity and accuracy of such query results in the presence of large data. This work presents a novel and efficient framework for pre-computing and storing a set of distributions which can be used to query any arbitrary region during post-processing. We adapt an integral image based data structure to answer such queries in constant time, and propose a similarity-based encoding technique to reduce the storage cost of the data structure. Our scheme utilizes the similarity present among different regions in the data, and hence, their respective distributions. We demonstrate the use our technique in various applications, which directly or indirectly require distributions.