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Bulk-loading of a multidimensional file: algorithm and its performance evaluation

机译:多维文件的批量加载:算法及其性能评估

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The multilevel grid file (MLGF) is a multidimensional file supporting multi-attribute accesses efficiently. The efficient bulk-loading of a multidimensional file is very important in constructing a database since this should handle an enormous volume of objects. In this paper, we propose the MLGF_BulkLoad, a new algorithm for bulk-loading of the MLGF. The main feature of the MLGF_BulkLoad is to simultaneously process all the objects or directory entries to be placed on each page belonging to the MLGF when accessing the page. This avoids the overhead for accessing the same page multiple times, which results from applying the MLGF insertion algorithm repeatedly. The MLGF_BulkLoad consists of two phases. The first phase relocates all the objects in order that the logically-adjacent objects in multidimensional data space are clustered in one-dimensional physical space. For this, we employ the Z-transformation, which has been known to map effectively multidimensional space into one dimensional space preserving proximity. The second phase paginates the relocated objects and creates the leaf-level directory entries, each of which corresponds to the page containing a set of objects. Simultaneously, it performs the same actions on the directory entries recursively in a bottom-up fashion. For performance evaluation, we analyze the MLGF_BulkLoad in terms of the number of page accesses. The result shows the superiority of the MLGF_Bulk-Load. We also present performance tendency of the MLGF_BulkLoad according to the changes of various parameter values through simulation.
机译:多层网格文件(MLGF)是一个多维文件,可有效支持多属性访问。多维文件的有效批量加载在构建数据库中非常重要,因为这将处理大量对象。在本文中,我们提出了MLGF_BulkLoad,这是一种用于MLGF批量加载的新算法。 MLGF_BulkLoad的主要功能是在访问页面时同时处理要放置在属于MLGF的每个页面上的所有对象或目录条目。这样避免了多次访问同一页面的开销,而这种开销是由于反复应用MLGF插入算法而导致的。 MLGF_BulkLoad由两个阶段组成。第一阶段重新放置所有对象,以使多维数据空间中逻辑相邻的对象聚集在一维物理空间中。为此,我们采用Z变换,已知该变换可将多维空间有效地映射到一维空间,并保持邻近度。第二阶段对重新定位的对象进行分页,并创建叶级目录条目,每个目录级条目对应于包含一组对象的页面。同时,它以自下而上的方式递归地对目录条目执行相同的操作。为了评估性能,我们根据页面访问次数来分析MLGF_BulkLoad。结果显示了MLGF_Bulk-Load的优越性。我们还通过仿真根据各种参数值的变化,给出了MLGF_BulkLoad的性能趋势。

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