QC-Trees: An Efficient Summary Structure for Semantic OLAP
Recently, a technique called quotient cube was proposed as a summary structure for a data cube that preserves its semantics, with applications for online exploration and visualization. The authors showed that a quotient cube can be constructed very efficiently and it leads to a significant reduction in the cube size. While it is an interesting proposal, that paper leaves many issues unaddressed. Firstly, a direct representation of a quotient cube is not as compact as possible and thus still wastes space. Secondly, while a quotient cube can in principle be used for answering queries, no specific algorithms were given in the paper. Thirdly, maintaining any summary structure incrementally against updates is an important task, a topic not addressed there. In this paper, we propose an efficient data structure called QC-tree and an efficient algorithm for directly constructing it from a base table, solving the first problem. We give efficient algorithms that address the remaining questions. We report results from an extensive performance study that illustrate the space and time savings achieved by our algorithms over previous ones (wherever they exist).
