摘要(英) |
Traditional relational database model does not have enough capability
to cope with a great deal of data in finite time. To address these
requirements, data warehouses and online analytical processing (OLAP)
have emerged. Data warehouses improve the productivity of corporate
decision makers through consolidation, conversion, transformation,
and integration of operational data, and supports online analytical
processing (OLAP). The data warehouse design is a complex and knowledge
intensive process. It needs to consider not only the structure of
the underlying operational databases (source-driven), but also the
information requirements of decision makers (user-driven). Past
research focused predominately on supporting the source-driven data
warehouse design process, but paid less attention to supporting
the user-driven data warehouse design process. Thus, the goal of
this research is to propose a user-driven data warehouse design
support system based on the knowledge discovery approach. Specifically,
a Data Warehouse Design Support System was proposed and the generalization
hierarchy and generalized star schemas were used as the data warehouse
design knowledge. The technique for learning these design knowledge
and reasoning upon them were developed. An empirical evaluation
study was conducted to validate the effectiveness on the proposed
techniques in supporting data warehouse design process. The result
of empirical evaluation showed that this technique was useful to
support data warehouse design especially on reducing the missing
design and enhancing the potentially useful design.
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