document: Incremental maintenance of quotient cube for median 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 is not only useful for distributive aggregate functions such as SUM but can also be applied to the holistic aggregate functions like MEDIAN . Maintaining a data cube for holistic aggregation is a hard problem since its difficulty lies in the fact that history tuple values must be kept in order to compute the new aggregate when tuples are inserted or deleted . The quotient cube makes the problem harder since we also need to maintain the equivalence classes . In this paper , we introduce two techniques called addset data structure and sliding window to deal with this problem . We develop efficient algorithms for maintaining a quotient cube with holistic aggregation functions that takes up reasonably small storage space . Performance study shows that our algorithms are effective , efficient and scalable over large databases .
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