All of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space, materializing a universal base table by joining multiple tables is often very expensive or even unaffordable in real data warehouses. In this paper, we investigate the problem of computing iceberg cubes from data warehouses. Surprisingly, our study shows that computing iceberg cube from multiple tables directly can be even more efficient in both space and runtime than computing from a materialized universal base table. We develop an efficient algorithm, CTC (for Cross Table Cubing) to tackle the problem. An extensive performance study on synthetic data sets demonstrates that our new approach is efficient and scalable for large data warehouses.

Pattern-based clustering is important in many applications, such as DNA micro-array data analysis, automatic recommendation systems and target marketing systems. However, pattern-based clustering in large databases is challenging. On the one hand, there can be a huge number of clusters and many of them can be redundant and thus make the pattern-based clustering ineffective. On the other hand, the previous proposed methods may not be efficient or scalable in mining large databases. We study the problem of maximal pattern-based clustering. Redundant clusters are avoided completely by mining only the maximal pattern-based clusters. MaPle, an efficient and scalable mining algorithm is developed. It conducts a depth-first, divide-and-conquer search and prunes unnecessary branches smartly. Our extensive performance study on both synthetic data sets and real data sets shows that maximal pattern-based clustering is effective. It reduces the number of clusters substantially. Moreover, MaPle is more efficient and scalable than the previously proposed pattern-based clustering methods in mining large databases.