Visualization and data analysis最新文献

{"title":"Visualizing Static Ensembles For Effective Shape and Data Comparison","authors":"L. Hao, C. Healey, S. Bass, Hsuan-Ya Yu","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-509","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-509","url":null,"abstract":"Ensembles are large, multidimensional, multivariate datasets generated in areas like physical and natural science to study real-world phenomena. Simulations or experiments are run repeatedly with slightly different initial parameters, producing members of the ensemble. The need to compare data and spatial properties, both within an individual member and across multiple members, makes analysis challenging. Initial visualization techniques focused on ensembles with a limited number of members. Others generated overviews of larger ensembles, but at the expense of aggregating potentially important details. We propose an approach that combines these two directions by automatically clustering members in ways that help scientists locate interesting subsets, then visualize members within the subset. Our ensemble visualization technique includes: (1) octree comparison and clustering to generate a hierarchical level-of-detail overview of inter-member shape and data similarity; (2) a glyph-based visualization of an ensemble member; and (3) a method of combining multiple glyph visualizations to highlight similarities and differences in shape and data values across a subset of ensemble members. We apply our approach to a Relativistic Heavy Ion Collider ensemble collected by nuclear physics colleagues at Duke University studying quantum chromo-dynamics. Our system allows the physicists to interactively choose when to explore inter-member relationships, and when to visualize fine-grained details in individual member datasets. Introduction An ensemble is formed by executing a simulation or an experiment repeatedly, with slightly different initial conditions or parameterizations for each run. Data produced from a run forms one member of the ensemble. Researchers from a wide range of disciplines are now using ensembles to investigate complex systems, explore a system’s sensitivity to its input parameters, measure uncertainty, and compare both spatial and data characteristics of the resulting models. Not surprisingly, ensembles are difficult to analyze due to their size and complexity. Wilson et. al. compared ensembles to traditional scientific data and summarized the characteristics and challenges unique to ensemble visualization [25]. Different techniques have been developed for ensemble analysis. One approach creates concise overview visualizations, but these may hide potentially important details in the original data [3, 20]. Another method extends existing scientific visualization techniques to support comparison between members [1, 17]. This can offer an improved view of individual members, but often cannot scale beyond small member sets. This suggests the two main approaches to ensemble visualization are currently: (1) generate an overview that scales but may not maintain detail, or (2) present a visualization that maintains detail but can only analyze a small number of members at one time. More recent systems try to support interactive ensemble analysis at ","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"28 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77397464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots","authors":"Kurtis Glendenning, T. Wischgoll, Jack Harris, R. Vickery, L. Blaha","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-490","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-490","url":null,"abstract":"Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect since it provide the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies and extends their functionality through the use of parallel processing. This methodology utilizes state-ofthe-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of HPC systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work will use parameter space data generated from mindmodeling.org to showcase our methodology for handling large-scale datasets while retaining interactivity and user friendliness.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"204 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82820533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation of Zebrafish Larva Inhomogeneous 3D Images Using the Level-Set Method","authors":"Z. Xiong, F. Verbeek","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-480","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-480","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"21 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76546542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flow Visualization Based on A Derived Rotation Field","authors":"Lei Zhang, Guoning Chen, R. Laramee, D. Thompson, A. Sescu","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-478","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-478","url":null,"abstract":"We identify and investigate the Φ field – a derived flow attribute field whose value at a given spatial location is determined by the integral curve initiated at the point. Specifically, we integrate the angle difference between the velocity vectors at two consecutive points along the integral curve to get the Φ field value. Important properties of the Φ field and its gradient magnitude |∇Φ| field are studied. In particular, we show that the patterns in the derived Φ field are generally aligned with the flow direction based on an inequality property. In addition, we compare the Φ field with some other attribute fields and discuss its relation with a number of flow features, such as LCS and cusp-like seeding structures. Furthermore, we introduce a unified framework for the computation of the Φ field and its gradient field, ∇Φ, and employ the Φ field and |∇Φ| field to a number of flow visualization and exploration tasks, including integral curve filtering, seeds generation and flow domain segmentation. We show that these tasks can be conducted more efficiently based on the information encoded in the Φ field.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"32 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79646341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrinsic Geometry Visualization for the Interactive Analysis of Brain Connectivity Patterns","authors":"G. Conte, Allen Q. Ye, K. Almryde, O. Ajilore, A. Leow, A. Forbes","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-481","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-481","url":null,"abstract":"Understanding how brain regions are interconnected is an important topic within the domain of neuroimaging. Advances in non-invasive technologies enable larger and more detailed images to be collected more quickly than ever before. These data contribute to create what is usually referred to as a connectome, that is, a comprehensive map of neural connections. The availability of connectome data allows for more interesting questions to be asked and more complex analyses to be conducted. In this paper we present a novel web-based 3D visual analytics tool that allows user to interactively explore the intrinsic geometry of the connectome. That is, brain data that has been transformed through a dimensionality reduction step, such as multidimensional scaling (MDS), Isomap, or t-distributed stochastic neighbor embedding (t-SNE) techniques. We evaluate our tool through a series of real-world case studies, demonstrating its effectiveness in aiding domain experts for a range of neuroimaging","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"49 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84450108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tweether: A Visualization Tool Displaying Correlation of Weather to Tweets","authors":"Shruti Daggumati, Igor Soares, Jieting Wu, D. Cao, Hongfeng Yu, Jun Wang","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-497","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-497","url":null,"abstract":"As the generation of social media, we can instantly express how our day is going; however, unknowingly the weather can play a key role in how we are feeling. The weather may dictate our lives regardless of what may be happening. The relationship between weather and mood has been immensely studied to show that the weather does play a major factor regarding our emotions. However, how we visualize the relationship and influence between weather and human emotions remains an interesting question. Based on the natural correlation between weather and mood, we propose Tweether, a real-time weather and tweet visualization tool, to see how Twitter users feel regarding the weather they experience. Our visualization displays a current reflection of emotions in a set of select geographic regions and also predicts possible emotions in these regions in response to the weather forecast. The visualization uses multiple layers to show the connection between geolocations, weather, and emotions. By aggregating multiple users with emotions, we create an aesthetic design in a 3D manner that is relatively free of visual clutter and it is simple to understand the relationships between weather and emotions. Introduction Weather affects our daily lives, from what we wear, what activities we do, what type of transportation we use, what we eat, or even how we feel. With the increasing accuracy of weather forecasts, people can gain an idea on the type of weather they can expect for upcoming days. Activities are usually planned according to the weather outside (e.g., weddings) and alternative plans must be made in case of inclement weather. How people dress is also affected by weather; when the temperature drops people need to wear coats to stay warm. The economy is also greatly affected by the weather. Certain weather conditions can lower crop yield and cause higher prices in stores. Disastrous weather phenomena such as hurricanes, tornadoes, or even floods can cause devastation in communities resulting in homelessness, death, and destruction. Inclement weather can also cause delays in transportation on roads or via flights. We can also choose to ride our bike to work instead of driving the car if the temperature is warm enough. One thing that is an effect of all these items is how we feel: • Are you sad that you cannot enjoy the outdoors due to rain? • Do you love that it’s raining so you can bundle up and read your favorite book? • Do you love the snow because it’s close to Christmas? • Do you hate the winter because you want it to be spring? These feelings are all brought out by the weather outside. One person can feel positive about a certain type of weather and one person can feel negative. Categorizing similar feelings from a large number of people may reveal some useful patterns that can help decision makers or shareholders make more appropriate decisions (e.g., carnival or sport game arrangements) with respect to weather conditions. Social media researchers can also ","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"57 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89757555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incremental visual text analytics of news story development","authors":"Milos Krstajic, Mohammad Najm-Araghi, Florian Mansmann, D. Keim","doi":"10.1117/12.912456","DOIUrl":"https://doi.org/10.1117/12.912456","url":null,"abstract":"Online news sources produce thousands of news articles every day, reporting on local and global real-world \u0000events. New information quickly replaces the old, making it difficult for readers to put current events in the \u0000context of the past. Additionally, the stories have very complex relationships and characteristics that are difficult \u0000to model: they can be weakly or strongly connected, or they can merge or split over time. In this paper, we \u0000present a visual analytics system for exploration of news topics in dynamic information streams, which combines \u0000interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge \u0000over time. We employ text clustering techniques to automatically extract stories from online news streams and \u0000present a visualization that: 1) shows temporal characteristics of stories in different time frames with different \u0000level of detail; 2) allows incremental updates of the display without recalculating the visual features of the past \u0000data; 3) sorts the stories by minimizing clutter and overlap from edge crossings. By using interaction, stories \u0000can be filtered based on their duration and characteristics in order to be explored in full detail with details on \u0000demand. To demonstrate the usefulness of our system, case studies with real news data are presented and show \u0000the capabilities for detailed dynamic text stream exploration.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"16 1","pages":"829407"},"PeriodicalIF":0.0,"publicationDate":"2012-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91159572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general approach for similarity-based linear projections using a genetic algorithm","authors":"James Mouradian, B. Hamann, R. Rosenbaum","doi":"10.1117/12.909485","DOIUrl":"https://doi.org/10.1117/12.909485","url":null,"abstract":"A widely applicable approach to visualizing properties of high-dimensional data is to view the data as a linear \u0000projection into two- or three-dimensional space. However, developing an appropriate linear projection is often \u0000difficult. Information can be lost during the projection process, and many linear projection methods only apply \u0000to a narrow range of qualities the data may exhibit. We propose a general-purpose genetic algorithm to develop \u0000linear projections of high-dimensional data sets which preserve a specified quality of the data set as much as \u0000possible. The obtained results show that the algorithm converges quickly and reliably for a variety of different \u0000data sets.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"20 1","pages":"82940L"},"PeriodicalIF":0.0,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83282425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-adaptive technique for visualizing geospatial data in 3D with minimum occlusion","authors":"Abon Chaudhuri, Han-Wei Shen","doi":"10.1117/12.912258","DOIUrl":"https://doi.org/10.1117/12.912258","url":null,"abstract":"Geospatial data are often visualized as 2D cartographic maps with interactive display of detail on-demand. Integration of \u0000the 2D map, which represents high level information, with the location-specific detailed information is a key design issue in \u0000geovisualization. Solutions include multiple linked displays around the map which can impose cognitive load on the user \u0000as the number of links goes up; and separate overlaid windowed displays which causes occlusion of the map. In this paper, \u0000we present a self-adaptive technique which reveals the hidden layers of information in a single display, but minimizes \u0000occlusion of the 2D map. The proposed technique creates extra screen space by invoking controlled deformation of the \u00002D map. We extend our method to allow simultaneous display of multiple windows at different map locations. Since our \u0000technique is not dependent on the type of information to display, we expect it to be useful to both common users and the \u0000scientists. Case studies are provided in the paper to demonstrate the utility of the method in occlusion management and \u0000visual exploration.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"47 1","pages":"82940I"},"PeriodicalIF":0.0,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74746264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDSS Log Viewer : visual exploratory analysis of large-volume SQL log data","authors":"Jian Zhang, Chaomei Chen, M. Vogeley, Danny Pan, Anirudha Thakar, M. Raddick","doi":"10.1117/12.907097","DOIUrl":"https://doi.org/10.1117/12.907097","url":null,"abstract":"User-generated Structured Query Language (SQL) queries are a rich source of information for database analysts, \u0000information scientists, and the end users of databases. In this study a group of scientists in astronomy and computer and \u0000information scientists work together to analyze a large volume of SQL log data generated by users of the Sloan Digital \u0000Sky Survey (SDSS) data archive in order to better understand users' data seeking behavior. While statistical analysis of \u0000such logs is useful at aggregated levels, efficiently exploring specific patterns of queries is often a challenging task due \u0000to the typically large volume of the data, multivariate features, and data requirements specified in SQL queries. To \u0000enable and facilitate effective and efficient exploration of the SDSS log data, we designed an interactive visualization \u0000tool, called the SDSS Log Viewer, which integrates time series visualization, text visualization, and dynamic query \u0000techniques. We describe two analysis scenarios of visual exploration of SDSS log data, including understanding \u0000unusually high daily query traffic and modeling the types of data seeking behaviors of massive query generators. The \u0000two scenarios demonstrate that the SDSS Log Viewer provides a novel and potentially valuable approach to support these \u0000targeted tasks.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"37 1","pages":"82940D"},"PeriodicalIF":0.0,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80177873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}