M. Cox and D. Ellsworth, "Managing big data for scientific visualization," in ACM Siggraph '97 Course #4 Exploring gigabyte datasets in real-time: Algorithms, data management, and time-critical design, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....approach that bridges the gap between adjacent levels in the AMR hierarchy. The resulting computation is then used for isosurface extraction (for example) However, the results can also be used for other purposes such as direct volume rendering. 3. 2 Static MR Representation Cox and Ellsworth [2, 1] observe that the amount of data generated by a visualization algorithm is relatively small compared to the total amount of data. This implies that sparse traversal methods can be created that reduce the amount of data needed to be accessed [7] In these approaches the entire octree is constructed ....

Michael B. Cox and David Ellsworth. Managing big data for scientific visualization. ACM Siggraph '97, 21, August 1997. Course #4 Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design.

....the disk I O performance overhead in volume rendering computation, particularly ray casting algorithms. Out of core rendering refers to the case where the rendering machine s physical memory can not hold the entire data set and thus need to perform disk I O during the rendering process. Cox [CE97, Cox97] studied this problem by examining the performance impacts of the operating system interfaces on the disk I O cost, as well as related file cache management issues. In contrast, our work attempts to use algorithm specific prefetching to ensure that the data blocks could be brought in before they ....

M. Cox. Managing big data for scientific visualization. ACM SIGGRAPH '98 Course, August 1997.

....of companies, this makes it the ideal graphics API for developing fast and portable visualization systems. 3 Figure 1: Overview of the user interface 5 Visualization Techniques This section presents all visualization techniques offered by our system. For further information consider [5] 6][7]. 5.1 Color Mapping Color mapping is a scalar visualization technique in which a surface is continuously colored in accordance with a scalar field. It is applied to either polygonal surfaces, or on cutting planes, which are decomposed into triangles. The scalar mapping is implemented by mapping ....

Michael Cox, David Ellsworth, "Managing Big Data for Scientific Visualization", MRJ/NASA Ames Research Center, Microcomputer Research Lab, Intel Corporation, 1997.

.... of each cell of 3 dimensional data results in the generation of geometry, traversal of the entire data set would generate geometry to fill 3 space, making the image visually difficult to comprehend There are of course algorithms that today must traverse the full data set, but we argue in [9] that finding the algorithm with the most parsimonious traversal is an important step in out of core visualization. The most common approach today is to pre load the entire data set before traversing it for visualization. If traversal really is sparse, more data are touched than need be. In ....

M. Cox and D. Ellsworth, "Managing Big Data for Scientific Visualization," ACM SIGGRAPH '97 Course #4, Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design, ACM SIGGRAPH '97, August 1997.

....problems (but aggravated) of consistency, heterogeneous database access, and of locating relevant data. 1 The latest version of these course notes can be found at http: science.nas.nasa.gov mbc home.html. The current notes build upon notes from previous SIGGRAPH courses, in particular [10] and [8] Both can be found at the same URL. 29 Big Data Michael Cox The Earth Observing System (EOS) whose development is overseen by NASA Goddard is an instructive example of the problem of big collections. The goal of EOS is to provide a long term repository of environmental measurements ....

COX,M.,AND ELLSWORTH, D. Managing big data for scientific visualization. In ACM SIGGRAPH '97 Course 4, Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design (August 1997). Los Angeles CA.

....problems (but aggravated) of consistency, heterogeneous database access, and of locating relevant data. 1 The latest version of these course notes can be found at http: science.nas.nasa.gov mbc home.html. The current notes build upon notes from previous SIGGRAPH courses, in particular [10] and [8]. Both can be found at the same URL. 29 Big Data Michael Cox The Earth Observing System (EOS) whose development is overseen by NASA Goddard is an instructive example of the problem of big collections. The goal of EOS is to provide a long term repository of environmental measurements (e.g. ....

COX, M. Managing big data for scientific visualization. In ACM SIGGRAPH '98 Course 2, Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design (August 1998).

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M. Cox and D. Ellsworth, "Managing big data for scientific visualization," in ACM Siggraph '97 Course #4 Exploring gigabyte datasets in real-time: Algorithms, data management, and time-critical design, 1997.

No context found.

M. Cox and D. Ellsworth, "Managing big data for scientific visualization," in ACM Siggraph '97 Course #4 Exploring gigabyte datasets in real-time: Algorithms, data management, and time-critical design, 1997.

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