Data Warehousing and Decision Support

在商业、政府和生活的各个方面，规划都很重要，因此需要能够处理决策支持查询的数据库管理系统。这些复杂的查询涉及大量数据的聚合。通常，专门的“数据仓库”或来自各种来源的信息的副本是为了询问决策支持查询而创建的，而原始数据库则用于交易处理。一些新的数据库产品实现了“数据立方体”，这是一种特殊的数据组织，可以促进常见形式的决策支持查询。一个重要的新研究问题是数据立方体设计，在数据立方体设计中，对于给定的存储量，物化一组选定的视图以优化平均查询时间是可行的。为数据立方体选择物化视图是一个主要的研究挑战。更一般地，当仓库中的视图随着底层数据库的改变而被维护时，期望在仓库处具有视图的选择，使得它们都可以被更新，而不必在底层数据库上发出查询(所谓的视图的自维护性)。可自我维护的仓库是另一个研究挑战。最后，决策支持查询本身通常可以通过最近发现的技术以及一些似乎需要研究的新技术来优化。

In business, government, and all aspects of life where planning is important, there is a need for database management systems that can handle `decision-support queries.` These complex queries involve the aggregation of very large amounts of data. Often, special `data warehouses,` or copies of information from a variety of sources, are created for the purpose of asking decision-support queries, while the original databases are used for transaction processing. Some new database products implement `data cubes,` special organizations of data that facilitate common forms of decision-support queries. One important new research problem is data-cube design, where it is feasible to materialize a selected set of views to optimize average query time, for a given amount of storage. Selection of materialized views for data cubes is a major research challenge. More generally, when views in a warehouse are maintained as the underlying databases change, it is desirable to have at the warehouse a selection of views so that they can all be updated without having to issue queries at the underlying databases (so-called `self-maintainability` of views). Self-maintainable warehouses present another research challenge. Finally, decision-support queries themselves can often be optimized by techniques recently discovered and, it appears, some new techniques that need investigation.