I/O-Efficient Bundled Range Aggregation
Yufei Tao, Cheng Sheng · IEEE Transactions on Knowledge and Data Engineering · 2013
This paper studies bundled range aggregation, which is conceptually equivalent to running a range aggregate query separately on multiple datasets, returning the query result on each dataset. In particular, the queried datasets can be arbitrarily chosen from a large number (hundreds or even thousands) of candidate datasets. The challenge is to minimize the query cost no matter how many and which datasets are selected. We propose a fully-dynamic data structure called aggregate bundled B-tree (aBB-tree) to settle bundled range aggregation. Specifically, the aBB-tree requires linear space, answers any query in O(logBN) I/Os, and can be updated in O(logBN) I/Os (where N is the total size of all the candidate datasets, and B the disk page size), under the circumstances where the number of datasets is O(B). The practical efficiency of our technique is demonstrated with extensive experiments.