Classification of Data Granularity in Data Warehouse
Haiyan Lv, Lijun Zhou, Yuan Zhao
Abstract
Haiyan Lv, Lijun Zhou, Yuan Zhao
Abstract
To enhance the performance of the bank-data warehouse and reasonably determine the detail of the data and the data granularity degree of the warehouse, a sample of data granularity classification of bank-data warehouse is presented. Firstly the granularity model of data warehouse is analyzed, and then the strategy of granularity classification is introduced. Based on the strategy a method of estimating the data warehouse size is put forward. On the basis of knowing the principles of designing data granularity, a data granularity classification sample of bank is showed. Combined with the requirement of multiple granularity classification, design and use of granularity table is given to realize the effective management of multiple granularity. This divide method effectively solute one of the most important design questions faced by bank-data warehouse exploiter, and cause the other aspects of design and realization of data warehouse can be carried smoothly.
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To enhance the performance of the bank-data warehouse and reasonably determine the detail of the data and the data granularity degree of the warehouse, a sample of data granularity classification of bank-data warehouse is presented. Firstly the granularity model of data warehouse is analyzed, and then the strategy of granularity classification is introduced. Based on the strategy a method of estimating the data warehouse size is put forward. On the basis of knowing the principles of designing data granularity, a data granularity classification sample of bank is showed. Combined with the requirement of multiple granularity classification, design and use of granularity table is given to realize the effective management of multiple granularity. This divide method effectively solute one of the most important design questions faced by bank-data warehouse exploiter, and cause the other aspects of design and realization of data warehouse can be carried smoothly.
Key concepts: Granularity, Data warehouse, Computer science, Realization (probability), Data mining, Sample (material), Table (database), Database