I/O-Efficient Bundled Range Aggregation

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 \(O(\log _B N)\) I/Os, and can be updated in \(O(\log _B N)\) 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.