This PDF file contains the front matter associated with IS&T/SPIE Proceedings Volume 7243, including the Title Page, Copyright Information, Table of Contents, Introduction, and the Conference Committee listing.
High throughput instrumentation for genomics is producing data orders of magnitude greater than even a decade before. Biologists often visualize the data of these experiments through the use of heat maps. For large datasets, heat map visualizations do not scale. These visualizations are only capable of displaying a portion of the data, making it difficult for scientists to find and detect patterns that span more than a subsection of the data. We present a novel method that provides an interactive visual display for massive heat maps
[O(108)]. Our process shows how a massive heat map can be decomposed into multiple levels of abstraction to represent the underlying macrostructures. We aggregate these abstractions into a framework that can allow near real-time navigation of the space. To further assist pattern discovery, we ground our system on the principle of focus+context. Our framework also addresses the issue of balancing the memory and display resolution and heat map size. We will show that this technique for biologists provides a powerful new visual metaphor for analyzing massive datasets.
Foot ulcers affect millions of Americans annually. Conventional methods to assess skin, including inspection and palpation, may be valuable approaches, but usually they do not detect changes in skin integrity until an ulcer has already developed. Conversely, thermal imaging is a technology able to assess the integrity of the skin and its many layers, thus having the potential to index the cascade of physiological events in the prevention, assessment, and
management of foot ulcers. In this paper, we propose a methodology based on an asymmetry analysis and a genetic algorithm to analyze the infrared images for early detection of foot ulcers. Preliminary results show that the proposed technique can be reliable and efficient to detect and, hence, predict inflammation and potential ulceration.
The use of Transmission Electron Microscopy (TEM) to characterize the microstructure of a material continues to grow in importance as technological advancements become increasingly more dependent on nanotechnology1 . Since nanoparticle properties such as size (diameter) and size distribution are often important in determining potential applications, a particle analysis is often performed on TEM images. Traditionally done manually, this has the potential to be labor intensive, time consuming, and subjective2. To resolve these issues, automated particle analysis routines are becoming more widely accepted within the community3. When using such programs, it is important to compare their
performance, in terms of functionality and cost. The primary goal of this study was to apply one such software package, ImageJ to grayscale TEM images of nanoparticles with known size. A secondary goal was to compare this popular open-source general purpose image
processing program to two commercial software packages. After a brief investigation of performance and price, ImageJ was identified as the software best suited for the particle analysis conducted in the study. While many ImageJ functions were used, the ability to break agglomerations that occur in specimen preparation into separate particles using a watershed algorithm was particularly helpful4.
This paper introduces an analysis-based zoomable visualization technique for displaying the location of genes across many related species of microbes. The purpose of this visualizatiuon is to enable a biologist to examine the layout of genes in the organism of interest with respect to the gene organization of related organisms. During the genomic annotation process, the ability to observe gene organization in common with previously annotated genomes can help a
biologist better confirm the structure and function of newly analyzed microbe DNA sequences. We have developed a visualization and analysis tool that enables the biologist to observe and examine gene organization among genomes, in the context of the primary sequence of interest. This paper describes the visualization and analysis steps, and presents a case study using a number of Rickettsia genomes.
Maps of the world are common in classroom settings. They are used to teach the juxtaposition of natural and political functions, mineral resources, political, cultural and geographical boundaries; occurrences of processes such as tectonic drift; spreading of epidemics; and weather forecasts, among others. Recent work in scientometrics aims to create a map of science encompassing our collective scholarly knowledge. Maps of science can be used to
see disciplinary boundaries; the origin of ideas, expertise, techniques, or tools; the birth, evolution, merging, splitting, and death of scientific disciplines; the spreading of ideas and technology; emerging research frontiers and bursts of activity; etc. Just like the first maps of our planet, the first maps of science are neither perfect nor correct. Today's science maps are predominantly generated based on English scholarly data: Techniques and procedures to achieve local and global accuracy of these maps are still being refined, and a visual language to communicate something as abstract and complex as science is still being developed. Yet, the maps are successfully used by institutions or individuals who can afford them to guide science policy decision making, economic decision
making, or as visual interfaces to digital libraries. This paper presents the process and results of creating hands-on
science maps for kids that teaches children ages 4-14 about the structure of scientific disciplines. The maps were tested in both formal and informal science education environments. The results show that children can easily transfer their (world) map and concept map reading skills to utilize maps of science in interesting ways.
The diversity of clients in today's network environment compels us to think about solutions that more than satisfy their needs according to their resources. For 3D terrain visualization this translates into two main requirements, namely the scalability and synchronous unification of a disparate data that requires at least two files, the texture image and its corresponding digital elevation model (DEM). In this work the scalability is achieved through the multiresolution discrete wavelet transform (DWT) of the JPEG2000 codec. For the unification of data, a simple DWT-domain spread spectrum (SS) strategy is employed in order to synchronously hide the DEM in the
corresponding texture while conserving the JPEG2000 standard file format. Highest possible quality texture is renderable due to the reversible nature of the SS data hiding. As far as DEM quality is concerned, it is ensured through the adaptation of synchronization in embedding that would exclude some highest frequency subbands.
To estimate the maximum tolerable error in the DEM according to a given viewpoint, a human visual system (HVS) based psycho-visual analysis is being presented. This analysis is helpful in determining the degree of adaptation in synchronization.
This paper describes the social networking questions, analysis, design and approach taken in the realisation of an interactive solution for the VAST 2008 challenge. The solution presented is a case study in this approach and won the phone call mini challenge award. The problem scenario of the competition is used as a case study
to explain the approach and experience with the developed tool. Design considerations and observations on the process used are drawn and suggestions on further research in the area of temporal graph data are made.
In this paper we introduce Musician Map, a web-based interactive tool for visualizing relationships among popular musicians who have released recordings since 1950. Musician Map accepts search terms from the user, and in turn uses these terms to retrieve data from MusicBrainz.org and AudioScrobbler.net, and visualizes the results. Musician Map visualizes relationships of various kinds between music groups and individual musicians, such as band membership, musical collaborations, and linkage to other artists that are generally regarded as being similar in musical style. These
relationships are plotted between artists using a new timeline-based visualization where a node in a traditional node-link diagram has been transformed into a Timeline-Node, which allows the visualization of an evolving entity over time, such as the membership in a band. This allows the user to pursue social trend queries such as "Do Hip-Hop artists collaborate differently than Rock artists".
Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Many analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream.
Mining results are quickly presented in graphical representations (i.e., tooltip) for the user to zoom into the problem
regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have
successfully applied this technique to detect data stream anomalies in large real-world enterprise server performance and data center energy management.
Using Jensen-Shannon divergence to measure differences in collaboration patterns with outside collaborators makes it possible to understand the structure of those collaborations without direct
information about how they collaborate with each other. Applying the approach to data on the outside collaborations of the Chinese Academy of Sciences and visualizing the results reveals interesting structure relevant for science policy decisions.
We have designed a system for presenting and graphically navigating four-variable data in a four-dimensional Cartesian environment. With full translational and rotational freedom, the system provides a 4D scene that can be explored and understood interactively. The system presents a four-dimensional environment to the user as a collection of 3D slices. Volunteers explored and solved randomly-generated 4D mazes of increasing complexity and size by traversing them to the end. Their ability to solve mazes improved significantly with
practice, reflecting an increasing ability to engage the 4D environment and demonstrating the viability of the system.
Neuroscience has benefited from an explosion of new experimental techniques; many have only become feasible in the wake of improvements in computing speed and data storage. At the same time, these new computation-intensive techniques have led to a growing gulf between the data and the knowledge extracted from those data. That is, in the neurosciences there is a paucity of effective knowledge management techniques and an accelerating accumulation of
experimental data. The purpose of the project described in the present paper is to create a visualization of the knowledge
base of the neurosciences. At run-time, this 'BrainFrame' project accesses several web-based ontologies and generates a
semantically zoomable representation of any one of many levels of the human nervous system.
The shift to electronic publishing of scientific journals is an opportunity for the digital library to provide non-traditional
ways of accessing the literature. One method is to use citation metadata drawn from a collection of electronic journals to
generate maps of science. These maps visualize the communication patterns in the collection, giving the user an easy-tograsp
view of the semantic structure underlying the scientific literature. For this visualization to be understandable the complexity of the citation network must be reduced through an algorithm. This paper describes the Citation Pathfinder application and its integration into a prototype digital library. This application generates small-scale citation networks that expand upon the search results of the digital library. These domain maps are linked to the collection, creating an interface that is based on the communication patterns in science. The Main Path Analysis technique is employed to
simplify these networks into linear, sequential structures. By identifying patterns that characterize the evolution of the
research field, Citation Pathfinder uses citations to give users a deeper understanding of the scientific literature.
We present an expansion of the popular open source Visualization Toolkit (VTK) to support the ingestion, processing, and display of informatics data. The result is a flexible, component-based pipeline framework for the integration and deployment of algorithms in the scientific and informatics fields. This project, code named "Titan", is one of the first efforts to address the unification of information and scientific visualization in a systematic fashion. The result includes a wide range of informatics-oriented functionality: database access, graph algorithms, graph layouts, views, charts, UI components and more. Further, the data distribution, parallel processing and client/server capabilities of VTK provide an excellent platform for scalable analysis.
Progressive refinement is commonly understood as a means to solve problems imposed by limited system resources. In this publication, we apply this technology as a novel approach for information presentation and device adaptation. The progressive refinement is able to handle different kinds of data and consists of innovative
ideas to overcome the multiple issues imposed by large data volumes. The key feature is the mature use of multiple incremental previews to the data. This leads to a temporal deskew of the information to be presented and provides a causal flow in terms of a
tour-through-the-data. Such a presentation is scalable leading to a significantly simplified adaptation to the available resources, short response times, and reduced visual clutter. Due to its rather beneficial properties and feedback we received from first implementations, we state that there is high potential of progressive refinement far beyond its currently addressed application context.
Tightly coupled visualization and analysis is a powerful approach to data exploration especially for clustering. We describe such a specific integration of analysis and visualization for the evaluation of multiple partitions of a data set. Partitions are decompositions of a dataset into a family of disjoint subsets. They may be the results of clustering, of groupings of categorical dimensions, of binned numerical dimensions, of predetermined class labeling dimensions, or of prior knowledge structured in mutually exclusive format (one data item associated with one and only one outcome).
Partition or cluster stability analysis can be used to identify near-optimal structures, build ensembles, or conduct validation. We extend Parallel Sets to a new visualization tool which provides for the mutual comparison and evaluation of multiple partitions of the same dataset. We describe a novel layout algorithm for informatively rearranging the order of records and dimensions. We provide examples of its application to data stability and correlation at the record, cluster, and dimension levels within a single interactive display.
Visualization and analysis of very large datasets remains a significant challenge to the visualization community.
Scientists have tried various techniques to deal with large data. Multiresolution data models reduce the size of the data using techniques such as mesh decimation, wavelet transformation, or data compression. The low resolution representation raises issues concerning the authenticity of the data at its resolution level. We address this issue by presenting our extensions to the VisIt visualization environment that enable the scientist to visualize
both multiresolution data and the uncertainty information associated with the lower resolution representations of the data.
We describe a novel data visualization framework named Reservoir Model Information System (REMIS) for the display of complex and multi-dimensional data sets in oil reservoirs. It is aimed at facilitating visual exploration and analysis of data sets as well as user collaboration in an easier way. Our framework consists of two main modules: the data access point module and the data visualization module. For the data access point module, the Phrase-Driven Grammar System (PDGS) is adopted for helping users facilitate the visualization of data. It integrates data source applications and
external visualization tools and allows users to formulate data query and visualization descriptions by selecting graphical icons in a menu or on a map with step-by-step visual guidance. For the data visualization module, we implemented our first prototype of an interactive volume viewer named REMVR to classify and to visualize geo-spatial specific data sets. By combining PDGS and REMVR, REMIS assists users better in describing visualizations and exploring data so that they can easily find desired data and explore interesting or meaningful relationships including trends and exceptions in
oil reservoir model data.
Analytical exploration of large data sets poses fundamental challenges to both database and data visualization. This paper introduces multiresolution data aggregation as an efficient representation of large relational data for interactive data exploration. Such a multiresolution data representation has build-in support of data scalability. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensional tree index. Such a piggyback ride of aggregated data efficiently supports resolution-based data access patterns such as overview-and-drill-down. A software tool is developed to demonstrate the feasibility and effectiveness of this technique for multiresolution visual exploration of general purpose relational data sets.