中文摘要：

数据立方体计算前是为支持 OLAP (处理的 OnlineAnalytical ) 的一个重要概念并且广泛地被学习了。由于巨大的存储器需求计算一个完全的数据立方体经常不是可行的。最近建议的商立方体通过组织立方体房间进等价分区的分割法处理了这个问题。如此的一条途径不仅为象和那样的分发的聚合函数是有用的而且能被用于整体的聚合函数象一样的维护中部它将为每个等价班要求一套元组的存储。不幸地，当变化被做到数据来源，自从划分立方体房间必须也被更新，维持商立方体是重要的。在这篇论文，作者设计增量算法为和和中部的聚合函数高效地更新一个商立方体。为聚合函数和，概念从 Galois 的原则被借开发中央处理器有效的算法更新一个商立方体。为聚合函数中部，一个假班的概念被介绍进一步减少商立方体的尺寸。结合了一种新奇滑动窗口技术，一个有效算法为维持那收起的一个中部的商立方体被开发相当小的存储空间。建议算法在大数据库上有效、可伸缩的性能研究表演。

英文摘要：

Data cube pre-computation is an important concept for supporting OLAP （Online Analytical Processing） and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this issue through a partitioning method that groups cube cells into equivalence partitions. Such an approach not only is useful for distributive aggregate functions such as SUM but also can be applied to the maintenance of holistic aggregate functions like MEDIAN which will require the storage of a set of tuples for each equivalence class. Unfortunately, as changes are made to the data sources, maintaining the quotient cube is non-trivial since the partitioning of the cube cells must also be updated. In this paper, the authors design incremental algorithms to update a quotient cube efficiently for both SUM and MEDIAN aggregate functions. For the aggregate function SUM, concepts are borrowed from the principle of Galois Lattice to develop CPU-efficient algorithms to update a quotient cube. For the aggregate function MEDIAN, the concept of a pseudo class is introduced to further reduce the size of the quotient cube, Coupled with a novel sliding window technique, an efficient algorithm is developed for maintaining a MEDIAN quotient cube that takes up reasonably small storage space. Performance study shows that the proposed algorithms are efficient and scalable over large databases.