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Data mining-based materialized view and index selection in data warehouses

机译：数据仓库中基于数据挖掘的物化视图和索引选择

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摘要

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
机译：物化视图和索引是数据仓库中经常使用的用于加速数据访问的物理结构。但是，这些数据结构会产生一些维护开销。它们还共享相同的存储空间。现有的关于物化视图和索引选择的大多数研究单独考虑了这些结构。在本文中，我们采取相反的立场，并结合了物化视图和索引选择，以考虑视图-索引交互，并实现有效的存储空间共享。候选物化视图和索引是通过数据挖掘过程选择的。我们还利用成本模型评估索引和视图实现的各自好处，并帮助在候选者之间选择索引和实现视图的相关配置。实验结果表明，我们的策略比独立选择的物化视图和索引更好。

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