A Tri-Space Visualization Interface for Analyzing Time-Varying Multivariate Volume Data
نویسندگان
چکیده
The dataset generated by a large-scale numerical simulation may include thousands of timesteps and hundreds of variables describing different aspects of the modeled physical phenomena. In order to analyze and understand such data, scientists need the capability to explore simultaneously in the temporal, spatial, and variable domains of the data. Such capability, however, is not generally provided by conventional visualization tools. This paper presents a new visualization interface addressing this problem. The interface consists of three components which abstracts the complexity of exploring in temporal, variable, and spatial domain, respectively. The first component displays time histograms of the data, helps the user identify timesteps of interest, and also helps specify time-varying features. The second component displays correlations between variables in parallel coordinates and enables the user to verify those correlations and possibly identity unanticipated ones. The third component allows the user to more closely explore and validate the data in spatial domain while rendering multiple variables into a single visualization in a user controllable fashion. Each of these three components is not only an interface but is also the visualization itself, thus enabling efficient screen-space usage. The three components are tightly linked to facilitate tri-space data exploration, which offers scientists new power to study their time-varying, multivariate volume data.
منابع مشابه
Interacting with 4D oceanographic volume data using GeoAnalytics tools
The need to turn environmental statistical data into knowledge and make decisions based on large amounts of multivariate, time-varying and geospatial information represent a major challenge for the analyst. These information streams, often in time-critical situations, demands efficient, integrated and interactive tools that aid the user to explore, present and communicate large information spac...
متن کاملFeature-Enhanced Visualization of Multidimensional, Multivariate Volume Data Using Non-photorealistic Rendering Techniques
This paper presents a set of feature enhancement techniques coupled with hardware-accelerated nonphotorealistic rendering for generating more perceptually effective visualization of multidimensional, multivariate volume data, such as those obtained from typical computational fluid dynamics simulations. For time-invariant data, one or more variables are used to either highlight important feature...
متن کاملA Study of Hierarchical Correlation Clustering for Scientific Volume Data
Correlation study is at the heart of time-varying multivariate volume data analysis and visualization. In this paper, we study hierarchical clustering of volumetric samples based on the similarity of their correlation relation. Samples are selected from a time-varying multivariate climate data set according to knowledge provided by the domain experts. We present three different hierarchical clu...
متن کاملVisualizing time-varying volume data
Studying dynamic aspects of physical and chemical processes is critical to the advances of many sciences. State-of-the-art scientific computing technologies allow accurate numerical modeling of many physical and chemical processes in their spatial and temporal domains. However, an increasingly challenging problem scientists must face is how to effectively explore and understand the resulting ti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007