Scalable methods for data warehousing and knowledge discovery

用于数据仓库和知识发现的可扩展方法

• 批准号: 312261-2008
• 负责人: Eavis, Todd
• 金额: $ 1.36万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=481199
• 关键词: Scalable; methods; data; warehousing; knowledge

Over the past fifteen to twenty years, data warehouses have evolved from haphazard and often poorly understood repositories of operational information to become one of the cornerstones of corporate IT architectures. Not surprisingly, academic researchers have shown great interest in fundamental DW issues, as well as those related to the complementary technology of Online Analytical Processing (OLAP). One over-riding concern with the design and implementation of contemporary warehouses is the sheer scale of the underlying databases, the largest of which now regularly exceed 100 Terabytes. Given the astounding computational resources required to support direct, interactive analysis within these environments, there is clearly a pronounced need for innovative research targeting the architectures, algorithms, design models, and data structures in enterprise DW/OLAP spaces.

在过去的15到20年中，数据仓库已经从杂乱无章且往往难以理解的运营信息存储库发展成为企业IT体系结构的基石之一。毫不奇怪，学术研究人员对基本的数据仓库问题以及与在线分析处理(OLAP)的补充技术相关的问题表现出了极大的兴趣。现代仓库的设计和实施最令人担忧的一个问题是基础数据库的巨大规模，其中最大的数据库现在经常超过100TB。考虑到在这些环境中支持直接、交互分析所需的惊人计算资源，显然需要针对企业DW/OLAP空间中的体系结构、算法、设计模型和数据结构进行创新研究。