Data Warehousing and Decision Support

数据仓储和决策支持

• 批准号: 9631952
• 负责人: Jeffrey Ullman
• 金额: $ 42.46万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9631952&HistoricalAwards=false
• 关键词: Data; Warehousing; Decision; Support

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.

在商业、政府以及生活中规划非常重要的各个方面，都需要能够处理“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，特殊的“数据仓库”或来自各种来源的信息副本是为了提出决策支持查询而创建的，而原始数据库则用于事务处理。 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，对于给定的存储量，可以具体化一组选定的视图来优化平均查询时间。 数据立方体的物化视图的选择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更改而维护时，希望在仓库中选择视图，以便它们都可以更新，而不必在底层数据库中发出查询（所谓的视图“自我维护性”）。 可自我维护的仓库提出了另一个研究挑战。 最后，决策支持查询本身通常可以通过最近发现的技术进行优化，而且似乎还有一些需要研究的新技术。