Efficient Approximate Update to Data Cube
有效的近似数据方体维护算法

It is often not feasible to compute a complete data cube due to the storage requirement. Recently proposed quotient cube addresses this issue through a partitioning method that groups cube cells into equivalence partitions. However, when the data source is updated, the aggregate values need to be recomputed even after one tuple is inserted or deleted. To keep the aggregate values to be always exact can prohibitively expensive in terms of time and/or storage space in a data warehouse environment. In many applications, it is sufficient to generate fast,approximate instead of full precise answers to queries. In this paper, we propose and examine techniques at the maintenance of an approximate quotient cube. Efficient algorithms are proposed and their effectiveness at storage and maintenance is investigated. A systematic performance study is conducted on different kind of data sets, which demonstrates our algorithms are efficient and scalable over large databases.