Interactive visualization of large scale time varying data sets

2002 ◽  
Author(s):  
Patric Ljung ◽  
Mark Dieckmann ◽  
Anders Ynnerman
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Data Sets ◽  
Time Varying

scholarly journals Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets

2010 ◽  
Vol 30 (3) ◽  
pp. 58-70 ◽  
Author(s):  
Won-Ki Jeong ◽  
Johanna Beyer ◽  
Markus Hadwiger ◽  
Rusty Blue ◽  
Charles Law ◽  
...  
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Data Sets ◽  
Analysis Tools

scholarly journals 1Click1View: Interactive Visualization Methodology for RNAi Cell-Based Microscopic Screening

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Lukasz Zwolinski ◽  
Marta Kozak ◽  
Karol Kozak
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Interactive Visualization ◽  
Image Data ◽  
Data Sets ◽  
Visualization Method ◽  
Multiple Cell ◽  
Visualization Tools ◽  
Screening Assays ◽  
Numeric Data

Technological advancements are constantly increasing the size and complexity of data resulting from large-scale RNA interference screens. This fact has led biologists to ask complex questions, which the existing, fully automated analyses are often not adequate to answer. We present a concept of 1Click1View (1C1V) as a methodology for interactive analytic software tools. 1C1V can be applied for two-dimensional visualization of image-based screening data sets from High Content Screening (HCS). Through an easy-to-use interface, one-click, one-view concept, and workflow based architecture, visualization method facilitates the linking of image data with numeric data. Such method utilizes state-of-the-art interactive visualization tools optimized for fast visualization of large scale image data sets. We demonstrate our method on an HCS dataset consisting of multiple cell features from two screening assays.

Grid-Based Visualization and its Medical Applications

2011 ◽  
pp. 25-57 ◽  
Author(s):  
Yingjun Qiu ◽  
Youbing Zhao ◽  
Jiaoying Shi
Keyword(s):  
Grid Computing ◽  
Large Scale ◽  
Interactive Visualization ◽  
Research Direction ◽  
Data Sets ◽  
Computing Technology ◽  
Visualization System ◽  
Huge Data ◽  
Visualization Systems ◽  
Grid Based

Traditional visualization approaches cannot handle new challenges in the visualization field such as visualizing huge data sets, communicating between existing visualization systems and providing interactive visualization services, widely. In this chapter, the authors introduce an emerging research direction in the visualization field, grid-based visualization, which aims to resolves the above problems by utilizing grid computing technology. However, current grid computing technology is almost batch job-oriented and does not support interactive visualization applications natively. In this chapter, the authors implement a grid-based visualization system (GVis) which utilizes large-scale computing resources to achieve large dataset visualization in real time and provides end users with reliable interactive visualization services, widely. In GVis system, current grid computing technology is extended to support interactive visualization applications.

scholarly journals OmicsTIDE: Interactive Exploration of Trends in Multi-Omics Data

2021 ◽  
Author(s):  
Theresa Harbig ◽  
Julian Fratte ◽  
Michael Krone ◽  
Kay Nieselt
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Biological Information ◽  
Data Sets ◽  
Omics Data ◽  
Interactive Analysis ◽  
Analysis Strategies ◽  
Final Integration ◽  
The Impact

AbstractMotivationThe increasing amount of data produced by omics technologies has significantly improved the understanding of how biological information is transferred across different omics layers and to which extent it is involved in the manifestation of a given phenotype. Besides data-driven analysis strategies, interactive visualization tools have been developed to make the analysis in the multi-omics field more transparent. However, most state-of-the-art tools do not reconstruct the impact of a given omics layer on the final integration result. In general, the amount of omics data analyses strategies and the fields of applications lack a clearer classification of the different approaches.ResultsWe developed a classification for omics data focusing on different aspects of multi-omics data sets, such as data type and experimental design. Based on this classification we developed the Omics Trend-comparing Interactive Data Explorer (OmicsTIDE), an interactive visualization tool developed to address the limitations of current visualization approaches in the multi-omics field. The tool consists of an automated part that clusters omics data to determine trends and an interactive visualization. The trends are visualized as profile plots and are connected by a Sankey diagram that allows an interactive pairwise trend comparison to discover concordant and discordant trends. Moreover, large-scale omics data sets are broken down into small subsets of concordant and discordant regulatory trends within few analysis steps. We demonstrate the interactive analysis using OmicsTIDE with two case studies focusing on different types of experimental designs.AvailabilityOmicsTIDE is a web tool and available via http://tuevis.informatik.uni-tuebingen.de/[email protected]

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals Modeling regional impacts and resilience to water service disruptions in urban economies

2021 ◽  
pp. 239980832199870
Author(s):  
Sheree A Pagsuyoin ◽  
Joost R Santos
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Urban Region ◽  
Economic Losses ◽  
Drought Severity ◽  
Severe Drought ◽  
Time Varying ◽  
National Capital Region ◽  
Economic Sectors ◽  
The Impact

Water is a critical natural resource that sustains the productivity of many economic sectors, whether directly or indirectly. Climate change alongside rapid growth and development are a threat to water sustainability and regional productivity. In this paper, we develop an extension to the economic input-output model to assess the impact of water supply disruptions to regional economies. The model utilizes the inoperability variable, which measures the extent to which an infrastructure system or economic sector is unable to deliver its intended output. While the inoperability concept has been utilized in previous applications, this paper offers extensions that capture the time-varying nature of inoperability as the sectors recover from a disruptive event, such as drought. The model extension is capable of inserting inoperability adjustments within the drought timeline to capture time-varying likelihoods and severities, as well as the dependencies of various economic sectors on water. The model was applied to case studies of severe drought in two regions: (1) the state of Massachusetts (MA) and (2) the US National Capital Region (NCR). These regions were selected to contrast drought resilience between a mixed urban–rural region (MA) and a highly urban region (NCR). These regions also have comparable overall gross domestic products despite significant differences in the distribution and share of the economic sectors comprising each region. The results of the case studies indicate that in both regions, the utility and real estate sectors suffer the largest economic loss; nonetheless, results also identify region-specific sectors that incur significant losses. For the NCR, three sectors in the top 10 ranking of highest economic losses are government-related, whereas in the MA, four sectors in the top 10 are manufacturing sectors. Furthermore, the accommodation sector has also been included in the NCR case intuitively because of the high concentration of museums and famous landmarks. In contrast, the Wholesale Trade sector was among the sectors with the highest economic losses in the MA case study because of its large geographic size conducive for warehouses used as nodes for large-scale supply chain networks. Future modeling extensions could potentially include analysis of water demand and supply management strategies that can enhance regional resilience against droughts. Other regional case studies can also be pursued in future efforts to analyze various categories of drought severity beyond the case studies featured in this paper.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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