The second part of the talk examines data mining. The goal is to support ad hoc queries on large data matrices that might not fit on disk. Such a matrix could have, e.g., customers for rows and days of the year for columns, with each cell value representing the amount spent on products. The target queries are single-cell queries ('Find the amount spent by Smith on 1/1/96') and aggregate queries ('Find the sales of customers from New York on December 1st'). We propose a compression format that permits random access, and thus efficiently supports ad hoc queries. Towards this end, we developed SVDD, a novel lossy compression method for very large data matrices, which reduces the matrix to 2% of the original space (i.e., a 50:1 compression ratio) and achieves 0.5% reconstruction error, as experiments on real data (e.g., AT&T customer sales) showed.