PARALLEL CREATION OF VARIO-SCALE DATA STRUCTURES FOR LARGE DATASETS
نویسندگان
چکیده
منابع مشابه
Parallel Creation of Vario-scale Data Structures for Large Datasets
Processing massive datasets which are not fitting in the main memory of computer is challenging. This is especially true in the case of map generalization, where the relationships between (nearby) features in the map must be considered. In our case, an automated map generalization process runs offline to produce a dataset suitable for visualizing at arbitrary map scale (vario-scale) and efficie...
متن کاملLarge scale road network generalization for vario-scale map
The classical approach for road network generalization consists of producing multiple maps, for a different scale or purpose, from a single detailed data source and quite often roads are represented by line objects. Our target is the generalization of a road network for the whole scale range from large scale, where roads are represented as area objects, to mid and small scales, where roads are ...
متن کاملVario-scale data structures supporting smooth zoom and progressive transfer of 2D and 3D data
This paper introduces the concept of the smooth tGAP structure represented by a space-scale partition, which we term the space-scale cube. We take the view of 'map generalization is extrusion of data into an additional dimension'. For 2D objects the resulting vario-scale representation is a 3D structure, while for 3D objects the resulting vario-scale representation is a 4D structure. This paper...
متن کاملLarge-Scale Parallel Visualization of Particle Datasets using Point Sprites
Massive particle-based datasets can be efficiently rendered using point sprites. Among other advantages, the method is efficient in its use of memory, preserves the dynamic range of the simulations, and provides excellent image quality. Preliminary results of large-scale parallel rendering of particle data sets using point sprites are presented in this poster. Performance and scalability are ev...
متن کاملLarge-scale parallel data clustering
Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in co...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2015
ISSN: 2194-9034
DOI: 10.5194/isprsarchives-xl-4-w7-1-2015