In high-dimensional data sets, both the number of dimensions and the cardinalities of the dimensions are large and data is often very sparse, that is, most cubes are empty. For such large data sets, it is a well-known challenging problem to compute the aggregation of a measure over arbitrary combinations of dimensions efficiently. However, in real-world applications, users are usually not interested in all the sparse cubes, most of which are empty or contain only one or few tuples. Instead, they focus more on the “big picture” information the highly aggregated data, where the “where clauses” of the SQL queries involve only few dimensions. Although the input data set is sparse, this aggregate data is dense. The existing multi-pass, full-cube computation algorithms are prohibitively slow for this type of application involving very large input data sets. We propose a new dynamic data structure called Restricted Sparse Statistics Tree (RSST) and a novel cube evaluation algorithm, which are especially well suited for efficiently computing dense sub-cubes imbedded in high-dimensional sparse data sets. RSST only computes the aggregations of non-empty cube cells where the number of non-star coordinates (i.e., the number of group by attributes) is restricted to be no more than a user-specified threshold. Our innovative algorithms are scalable and I/O efficient. RSST is incrementally maintainable, which makes it suitable for data warehousing and the analysis of streaming data. We have compared our algorithms with top, state-of-the-art cube computation algorithms such as Dwarf and QCT in construction times, query response times, and data compression. Experiments demonstrate the excellent performance and good scalability of our approach.