High-Dimensional OLAP: A Minimal Cubing Approach

Data cube has been playing an essential role in fast OLAP (online analytical processing) in many multi-dimensional data warehouses. However, there exist data sets in applications like bioinformatics, statistics, and text processing that are characterized by high dimensionality, e.g., over 100 dimensions, and moderate size, e.g., around 10 tuples. No feasible data cube can be constructed with such data sets. In this paper we will address the problem of developing an efficient algorithm to perform OLAP on such data sets. Experience tells us that although data analysis tasks may involve a high dimensional space, most OLAP operations are performed only on a small number of dimensions at a time. Based on this observation, we propose a novel method that computes a thin layer of the data cube together with associated value-list indices. This layer, while being manageable in size, will be capable of supporting flexible and fast OLAP operations in the original high dimensional space. Through experiments we will show that the method has I/O costs that scale nicely with dimensionality. Furthermore, the costs are comparable to that of accessing an existing data cube when full materialization is possible.