Papers by Aritra Dasgupta
Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling
IEEE Transactions on Visualization and Computer Graphics, 2014
Visual data analysis often requires grouping of data objects based on their similarity. In many a... more Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.
Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison
IEEE Transactions on Visualization and Computer Graphics, 2015
Evaluation methodologies in visualization have mostly focused on how well the tools and technique... more Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.
The visual uncertainty paradigm for controlling screen-space information in visualization
Measuring Visual Complexity of Cluster-Based Visualizations
In this paper, we reflect on the use of visualization techniques for analyzing electronic health ... more In this paper, we reflect on the use of visualization techniques for analyzing electronic health record data with privacy concerns. Privacy-preserving data visualization is a relatively new area of research compared to the more established research areas of privacy-preserving data publishing and data mining. We describe the opportunities and challenges for privacy-preserving visualization of electronic health record data by analyzing the different disclosure risk types, and vulnerabilities associated with commonly used visualization techniques.
Integrate Data into Scientific Workflows for Terrestrial Biosphere Model Evaluation through Brokers
ABSTRACT Terrestrial biosphere models (TBMs) have become integral tools for extrapolating local o... more ABSTRACT Terrestrial biosphere models (TBMs) have become integral tools for extrapolating local observations and process-level understanding of land-atmosphere carbon exchange to larger regions. Model-model and model-observation intercomparisons are critical to understand the uncertainties within model outputs, to improve model skill, and to improve our understanding of land-atmosphere carbon exchange. The DataONE Exploration, Visualization, and Analysis (EVA) working group is evaluating TBMs using scientific workflows in UV-CDAT/VisTrails. This workflow-based approach promotes collaboration and improved tracking of evaluation provenance. But challenges still remain. The multi-scale and multi-discipline nature of TBMs makes it necessary to include diverse and distributed data resources in model evaluation. These include, among others, remote sensing data from NASA, flux tower observations from various organizations including DOE, and inventory data from US Forest Service. A key challenge is to make heterogeneous data from different organizations and disciplines discoverable and readily integrated for use in scientific workflows. This presentation introduces the brokering approach taken by the DataONE EVA to fill the gap between TBMs' evaluation scientific workflows and cross-organization and cross-discipline data resources. The DataONE EVA started the development of an Integrated Model Intercomparison Framework (IMIF) that leverages standards-based discovery and access brokers to dynamically discover, access, and transform (e.g. subset and resampling) diverse data products from DataONE, Earth System Grid (ESG), and other data repositories into a format that can be readily used by scientific workflows in UV-CDAT/VisTrails. The discovery and access brokers serve as an independent middleware that bridge existing data repositories and TBMs evaluation scientific workflows but introduce little overhead to either component. In the initial work, an OpenSearch-based discovery broker is leveraged to provide a consistent mechanism for data discovery. Standards-based data services, including Open Geospatial Consortium (OGC) Web Coverage Service (WCS) and THREDDS are leveraged to provide on-demand data access and transformations through the data access broker. To ease the adoption of broker services, a package of broker client VisTrails modules have been developed to be easily plugged into scientific workflows. The initial IMIF has been successfully tested in selected model evaluation scenarios involved in the NASA-funded Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP).
Inter-comparison and similarity analysis to gauge consensus among multiple simulation models is a... more Inter-comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain-specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.
Measuring Privacy and Utility in Privacy-Preserving Visualization
Computer Graphics Forum, 2013
ABSTRACT In previous work, we proposed a technique for preserving the privacy of quasi-identifier... more ABSTRACT In previous work, we proposed a technique for preserving the privacy of quasi-identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade-off between privacy and utility: the more the data are protected, the less useful the visualization. Using a visually-oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data-based clustering in the context of privacy-preserving visualization.
Computer Graphics Forum, 2012
Uncertainty is an intrinsic part of any visual representation in visualization, no matter how pre... more Uncertainty is an intrinsic part of any visual representation in visualization, no matter how precise the input data. Existing research on uncertainty in visualization mainly focuses on depicting data-space uncertainty in a visual form. Uncertainty is thus often seen as a problem to deal with, in the data, and something to be avoided if possible. In this paper, we highlight the need for analyzing visual uncertainty in order to design more effective visual representations. We study various forms of uncertainty in the visual representation of parallel coordinates and propose a taxonomy for categorizing them. By building a taxonomy, we aim to identify different sources of uncertainty in the screen space and relate them to different effects of uncertainty upon the user. We examine the literature on parallel coordinates and apply our taxonomy to categorize various techniques for reducing uncertainty. In addition, we consider uncertainty from a different perspective by identifying cases where increasing certain forms of uncertainty may even be useful, with respect to task, data type and analysis scenario. This work suggests that uncertainty is a feature that can be both useful and problematic in visualization, and it is beneficial to augment an information visualization pipeline with a facility for visual uncertainty analysis.
Dean N. Williams, williams13@llnl.gov (Principal Investigator for UV-CDAT.) Dr. Timo Bremer, brem... more Dean N. Williams, williams13@llnl.gov (Principal Investigator for UV-CDAT.) Dr. Timo Bremer, bremer5@llnl.gov (Computer scientist at LLNL, leading analysis and visualization of scientific data and applications.) Charles Doutriaux, doutriaux1@llnl.gov (Computer scientist at LLNL developing diagnostics and visualizations for the climate community.)
ABSTRACT Visualization research focuses either on the transformation steps necessary to create a ... more ABSTRACT Visualization research focuses either on the transformation steps necessary to create a visualization from data, or on the perception of structures after they have been shown on the screen. We argue that an end-to-end approach is necessary that tracks the data all the way through the required steps, and provides ways of measuring the impact of any of the transformations.
Abstract Managing computational complexity and designing effective visual representations are two... more Abstract Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. Parallel coordinates are an effective technique for visualizing high-dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen.
Visualization and Computer Graphics …, Jan 1, 2010
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Papers by Aritra Dasgupta