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BirdVis: visualizing and Understanding Bird Populations

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2374-2383页

作者： Ferreira, Nivan Lins, Lauro Fink, Daniel Kelling, Steve Wood, Chris Freire, Juliana Silva, Claudio NYU Polytech Inst New York NY 10003 USA Cornell Univ Cornell Lab Ithaca NY 14853 USA

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 spatial-temporal 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 s

关键词： Ornithology species distribution models multiscale analysis spatial data temporal data

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Multiresolution Semantic visualization of Network Traffic

Multiresolution Semantic Visualization of Network Traffic

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5th Annual IEEE International conference on Semantic Computing (ICSC)

作者： Hussain, Alefiya Viswanathan, Arun USC Informat Sci Inst Los Angeles CA 90007 USA

ISBN: (纸本)9780769544922

Multiresolution semantic analysis of data involves inferring increasing levels of meaning from data. Due to the large volume and complexity of network data, multiresolution visualizations allow the user to rapidly focus on meaningful and relevant information. Current tools and techniques for visual analysis of network data are limited in their ability to operate at semantically relevant resolutions and impose great cognitive burden on users to manually infer semantics from low-level details. Extending our previous work that allows users to define semantics as abstract models, we apply these models to construct multiresolution visualizations of network traffic data. Our methodology for visual exploration allows the user to rapidly analyze and understand network traces, by providing intuitive and interactive representations of the network. We demonstrate the effectiveness of our approach by applying it to analyzing network trace data from a cyber security incident involving DNS cache poisoning.

关键词： Analytical models Computer crime data visualization Semantics Spatial resolution visualization networks security visualization

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Impact of near-lossless and lossy coding on information extraction from hyperspectral data

Impact of near-lossless and lossy coding on information extr...

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conference on visual Information Processing and Communication ii

作者： Miguel, Agnieszka Seattle Univ Seattle WA 98122 USA

ISBN: (纸本)9780819484192

We study the ability to derive meaningful information from decompressed imaging spectrometer data. Each hyperspectral band is linearly predicted by a previously transmitted band. The residual, formed by subtracting the prediction from the original data is compressed either with a near-lossless bit plane coder or with the lossy JPEG2000 algorithm. We investigate the effects of these two compression methods on hyperspectral image processing using whole-and mixed-pixel analysis techniques. Surprisingly, the lossy coder outperforms near-lossless method in terms of its impact on final hyperspectral data applications.

关键词： hyperspectral data compression near-lossless information extraction

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BaobabView: Interactive construction and analysis of decision trees

BaobabView: Interactive construction and analysis of decisio...

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2nd IEEE conference on visual Analytics Science and Technology 2011, VAST 2011

作者： Van Den Elzen, Stef Van Wijk, Jarke J. Eindhoven University of Technology Netherlands

ISBN: (纸本)9781467300131

We present a system for the interactive construction and analysis of decision trees that enables domain experts to bring in domain specific knowledge. We identify different user tasks and corresponding requirements, and develop a system incorporating a tight integration of visualization, interaction and algorithmic support. Domain experts are supported in growing, pruning, optimizing and analysing decision trees. Furthermore, we present a scalable decision tree visualization optimized for exploration. We show the effectiveness of our approach by applying the methods to two use cases. The first case illustrates the advantages of interactive construction, the second case demonstrates the effectiveness of analysis of decision trees and exploration of the structure of the data. © 2011 IEEE.

关键词： Decision trees

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An Interactive Bio-Inspired Approach to Clustering and visualizing datasets

An Interactive Bio-Inspired Approach to Clustering and Visua...

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15th International conference on Information visualisation (IV)

作者： Erra, Ugo Frola, Bernardino Scarano, Vittorio Univ Basilicata Dipartimento Matemat & Informat Potenza Italy Univ Salerno Dipartimento Informat Fisciano Italy

ISBN: (纸本)9780769544762

In this work, we present an interactive visual clustering approach for the exploration and analysis of datasets using the computational power of Graphics Processor Units (GPUs). The visualization is based on a collective behavioral model that enables cognitive amplification of information visualization. In this way, the workload of understanding the representation of information moves from the cognitive to the perceptual system. The results enable a more intuitive, interactive approach to the discovery of knowledge. The paper illustrates this behavioral model for clustering data, and applies it to the visualization of a number of real and synthetic datasets.

关键词： visual clustering behavioral model GPU high-dimensional datasets

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Pointwise local pattern exploration for sensitivity analysis

Pointwise local pattern exploration for sensitivity analysis

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2nd IEEE conference on visual Analytics Science and Technology 2011, VAST 2011

作者： Guo, Zhenyu Ward, Matthew O. Rundensteiner, Elke A. Ruiz, Carolina Computer Science Department Worcester Polytechnic Institute United States

ISBN: (纸本)9781467300131

Sensitivity analysis is a powerful method for discovering the significant factors that contribute to targets and understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. Incorporating sensitivity analysis in visual analytic tools is essential for multivariate phenomena analysis. However, most current multivariate visualization techniques do not allow users to explore local patterns individually for understanding the sensitivity from a pointwise view. In this paper, we present a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. During exploration, users are able to interactively change the derivative coefficients to perform sensitivity analysis based on different requirements as well as their domain knowledge. Each local pattern is assigned an outlier factor, so that users can quickly identify anomalous local patterns that do not conform with the global pattern. Users can also compare the local pattern with the global pattern both visually and statistically. Finally, the local pattern is integrated into the original attribute space using color mapping and jittering, which reveals the distribution of the partial derivatives. Case studies with real datasets are used to investigate the effectiveness of the visualizations and interactions. © 2011 IEEE.

关键词： Sensitivity analysis

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VAST 2011 - IEEE conference on visual Analytics Science and Technology 2011, Proceedings

VAST 2011 - IEEE Conference on Visual Analytics Science and ...

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2nd IEEE conference on visual Analytics Science and Technology 2011, VAST 2011

ISBN: (纸本)9781467300131

The proceedings contain 57 papers. The topics discussed include: guiding feature subset selection with an interactive visualization;observation-level interaction with statistical models for visual analytics;pointwise local pattern exploration for sensitivity analysis;interactive decision making using dissimilarity to visually represented prototypes;BaobabView: interactive construction and analysis of decision trees;from movement tracks through events to places: extracting and characterizing significant places from mobility data;visual analysis of route diversity;SensePlace2: GeoTwitter analytics support for situational awareness;visual analytics decision support environment for epidemic modeling and response evaluation;SAVE: sensor anomaly visualization engine;and a visual navigation system for querying neural stem cell imaging data.

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exploration Views: Understanding Dashboard Creation and Customization for visualization Novices

Exploration Views: Understanding Dashboard Creation and Cust...

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13th IFIP TC 13 International conference on Human-Computer Interaction (INTERACT)

作者： Elias, Micheline Bezerianos, Anastasia Ecole Cent Paris MAS Lab Chatenay Malabry France SAP Res Business Intelligence Practice Sophia Antipolis France

ISBN: (纸本)9783642237683;9783642237676

With the increase of visualization platforms targeting novices, researchers are now focusing on gathering insights regarding novice user practices. We describe the design and evaluation of exploration Views (EV), a system that allows novice visualization users to easily build and customize Business Intelligence information dashboards. EV provides an intuitive environment for dynamically creating, rearranging, searching and exploring multiple visual data representations from diverse data-sources. These aspects aid users to better retrieve, experiment and familiarize themselves with their data. We evaluated EV with both novice and expert dashboard designers and report here (i) how novice users interact with the system, (ii) differences in how novice and expert users react to a dashboard systems that targets both, and (iii) provide new design guidelines for practitioners building dashboard applications, on the needs of novice visualization users.

关键词： synchronized views interface customization novice users visual queries business intelligence dashboards

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Controlled Recognition Bounds for Scaling and Occlusion Channels

Controlled Recognition Bounds for Scaling and Occlusion Chan...

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data Compression conference (DCC)

作者： Soatto, Stefano Chiuso, Alessandro Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9780769543529

We tackle the problem of compression in the context of decision and control tasks, as opposed to data transmission and storage tasks implicit in traditional communications theory. We focus on the simplest task (binary decision) for channels subject to scaling and occlusion phenomena, ubiquitous in remote sensing and imaging data. In order to extend Rate-Distortion theory, we characterize the tradeoff between decision performance (expected risk) and resources ("rate"). We show that, in the presence of scaling and occlusions, in order to trade off the expected risk, it is necessary to exercise control on the sensing process. Thus the natural generalization of "rate" is not the volume of the data provided by the sensor, but the amount of control authority that can be exercised on it, measured by the volume of the reachable set and the energy of the input. Scaling poses a challenge to traditional Sampling Theory, as there is no Nyquist Frequency, or complexity bound, that can result in a sufficient discretization. This makes the continuous limit in the analysis relevant. Occlusions poses a challenge to worst-case performance bounds (if the object of interest is not manifest in the data, the probability of error in the decision is no better than "chance", i.e., what is offered by the prior). We prove that, when both occlusion and scaling are present, average performance (expected error) is also at chance (Sect. 1 of [1]) if the data is processed in a passive manner. However, we show that if the sensor is afforded infinite control authority (full reach ability, infinite energy), the expected error can be made arbitrarily small (asymptotically) (Sect. 2 of [1]). In between these two extrema, the amount of control authority, that depends on the geometry of state-space (reachable volume and on the energy of the input, trades off performance in the task (expected error). We prove the extremal cases analytically, and characterize the tradeoff empirically (Sect. 3 of [1] and th

关键词： Discriminative data compression active learning active vision control identification information theory rate-distortion robotics visual exploration visual recognition visual search Occlusion Scale formation active vision Rate distortion visual Search Experiential Learning Information Theory Scaling robotics Standing Orders Dental Scaling Communication Theory Rate distortion theory Bound task performance

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Composite Density Maps for Multivariate Trajectories

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2518-2527页

作者： Scheepens, Roeland Willems, Niels van de Wetering, Huub Andrienko, Gennady Andrienko, Natalia van Wijk, Jarke J. Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands Fraunhofer Inst IAIS St Augustin Germany

We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains.

关键词： Trajectories Kernel Density Estimation Multivariate data Geographical Information Systems Raster Maps

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