Scalable methods for data warehousing and knowledge discovery

点击翻译按钮获取中文摘要

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.