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首页> 外文期刊>Journal of systems architecture >A new indexing scheme supporting multi-attribute database applications: MAX
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A new indexing scheme supporting multi-attribute database applications: MAX

机译：支持多属性数据库应用程序的新索引方案：MAX

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Multi-attribute indexing schemes can be classified into seven classes according to the manner of partitioning multi-di-mensional data space into regions, each of which contains all the records of a single data page. On the basis of this principle, we classify according to three properties of the hyperplane partitioning a region: the dimension of hyperplane, the number of hyperplanes, and the normal vector of hyperplane. Among the seven classes, we select a class as our indexing scheme model according to the complexity for maintaining hyperplane. From our model, we derive an indexing scheme, MAX, which handles multi-attribute data efficiently. In addition, a number of algorithms for manipulating multi-attribute data are given, together with their computational and I/O complexity. Moreover, we show that MAX is a kind of generalized B-tree. This means that MAX can be easily implemented on existing built-in B-trees in most storage managers in the sense that the structure of MAX is like that of B-tree. We measure the performance by testing against various realistic data and query sets. Results from the benchmark test indicate that MAX outperforms other indexing schemes on insertion, range query, and spatial join. For insertion, together with B-tree using z-transformation, MAX presents good performance in the aspects of CPU and I/O cost. Regardless of various clustering factors, the storage cost of MAX is remarkably low compared with KDB-tree and R-tree. We conclude that MAX is an efficient indexing scheme for multi-attribute database. If MAX is fully implemented as an index method of storage manager, the performance of many applications using multi-attribute will be remarkably enhanced.
机译：根据将多维数据空间划分为多个区域的方式，可以将多属性索引方案分为七个类别，每个区域都包含单个数据页的所有记录。基于此原理，我们根据划分区域的超平面的三个属性进行分类：超平面的尺寸，超平面的数量和超平面的法向矢量。在这七个类别中，我们根据维护超平面的复杂性选择一个类别作为索引方案模型。从我们的模型中，我们得出一个索引方案MAX，它可以有效地处理多属性数据。此外，还提供了许多用于操纵多属性数据的算法，以及它们的计算和I / O复杂性。此外，我们证明了MAX是一种广义的B树。这意味着在大多数存储管理器中，就MAX的结构就像B-tree一样，可以很容易地在现有的内置B-tree上实现MAX。我们通过针对各种实际数据和查询集进行测试来衡量性能。基准测试的结果表明，在插入，范围查询和空间连接方面，MAX的性能优于其他索引方案。对于插入，以及使用z变换的B树，MAX在CPU和I / O成本方面表现出良好的性能。不管各种聚类因素如何，与KDB树和R树相比，MAX的存储成本都非常低。我们得出结论，MAX是用于多属性数据库的有效索引方案。如果将MAX完全实现为存储管理器的索引方法，则使用多属性的许多应用程序的性能将得到显着提高。

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