It is often too expensive to compute and materialize a complete high-dimensional data cube. Computing an iceberg cube, which contains only aggregates above certain thresholds, is an effective way to derive nontrivial multidimensional aggregations for OLAP and data mining. In this paper, we study efficient methods for computing iceberg cubes with some popularly used complex measures, such as average, and develop a methodology that adopts a weaker but anti-monotonic condition for testing and pruning search space. In particular, for efficient computation of iceberg cubes with the average measure, we propose a top-k average pruning method and extend two previously studied methods, Apriori and BUC, to Top-k Apriori and Top-k BUC. To further improve the performance, an interesting hypertree structure, called H-tree, is designed and a new iceberg cubing method, called Top-k H-Cubing, is developed. Our performance study shows that Top-k BUC and Top-k H-Cubing are two promising candidates for scalable computation, and Top-k H-Cubing has better performance in most cases.
|Original language||English (US)|
|Number of pages||12|
|Journal||SIGMOD Record (ACM Special Interest Group on Management of Data)|
|State||Published - Jun 2001|
ASJC Scopus subject areas
- Information Systems