Efficient maintenance of basic statistical functions in data warehouses

In general, some simple but very meaningful statistical functions are often used to retrieve valuable summary information in corporate databases. However, it is not uncommon that such information is obtained from computerized information systems which spend a great deal of time calculating the large volume of collected data. In practice, such data is usually stored in a data warehouse in which a large number of summary tables or materialized aggregate views are built in order to improve the system performance. Upon changes, most notable new transactional data are collected from various data sources, and all summary tables in the data warehouse that correspond to the transactional data must be updated accordingly. Since the number of summary tables that need to be maintained is often large, efficiently maintaining these is thus a critical issue for managing a data warehouse. In this study, an efficient maintenance approach to enhance the performance of a data warehouse is proposed, in which some additional auxiliary tables are kept inside a data warehouse with the role of improving the maintenance processes of some statistical functions, such as MIN, MAX, MEAN, and MEDIAN. Finally, a comparative analysis is performed to verify the effectiveness of the proposal method.