Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. First, four factors that possibly affect the rasterization efficiency were investigated. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In parallel rasterization, it is difficult to design an effective data decomposition method. It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. Zhou, Chen Chen, Zhenjie Liu, Yongxue Li, Feixue Cheng, Liang Zhu, A.-xing Li, Manchun Lessons learnt from using rasdaman will be discussed as well.ĭata decomposition method for parallel polygon rasterization considering load balancing Earth Observing System (EOS) data collected from NASA's Atmospheric Scientific Data Center (ASDC) will be used and stored in these selected database systems, and a set of spatial and non-spatial queries will be designed to benchmark their performance on retrieving large-scale, multi-dimensional arrays of EOS data. Within this paper, the pros and cons of using rasdaman to manage and query spatial raster data will be examined and compared with other common approaches, including file-based systems, relational databases (e.g., PostgreSQL/PostGIS), and NoSQL databases (e.g., MongoDB and Hive). Recently, rasdaman ( raster data manager) has emerged as a scalable and cost-effective database solution to store and retrieve massive multi-dimensional arrays, such as sensor, image, and statistics data. While high performance computing platforms (e.g., cloud computing) can be used to solve the computing-intensive issues in big data analysis, data has to be managed in a way that is suitable for distributed parallel processing. The volume, velocity, and variety of such spatial data, along with the computational intensive nature of spatial queries, pose grand challenge to the storage technologies for effective big data management. Recent advancements on data acquisition technology, such as remote sensing, have allowed us to collect massive observation data of various spatial resolution and domain coverage. Spatial raster data have grown exponentially over the past decade. This document is intended to serve a varied audience including: users wishing to display and manipulate raster image data, programmers responsible for either interfacing the RM format with other raster formats or for developing new RMT device drivers, and programmers charged with installing the software on a host platform. Both the Raster Metafile (RM) format and the Raster Metafile Translator (RMT) are addressed. The intent is to present an effort undertaken at NASA Langley Research Center to design a generic raster image format and to develop tools for processing images prepared in this format. Raster Metafile and Raster Metafile Translator This method is very helpful for some situations that need to analyse or display vector data and raster data at the same time. Using Morton code to mark geographic information enables storage of data make full use of storage space, simultaneous analysis of vector and raster data more efficient and visualization of vector and raster more intuitive. This method stores vector and raster data to Oracle Database and uses Morton code instead of row and column and X Y to mark the position information of vector and raster data. Then we use ADO interface to connect database to Visual C++ and convert row and column numbers of raster data and X Y of vector data to Morton code in Visual C++ environment. In this paper, we saved the image data and building vector data of Guilin University of Technology to Oracle database. This article proposes a method to interpret vector data and raster data. And there is still not a proper way to solve the problem. Liu, X.Įven though geomatique is so developed nowadays, the integration of spatial data in vector and raster formats is still a very tricky problem in geographic information system environment. Vector and Raster Data Storage Based on Morton Code
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