On Approximating Frequency Moments of Data Streams with Skewed   Projections
  We propose skewed stable random projections for approximating the pth frequency moments of dynamic data streams (0<p<=2), which has been frequently studied in theoretical computer science and database communities. Our method significantly (or even infinitely when p->1) improves previous methods based on (symmetric) stable random projections.   Our proposed method is applicable to data streams that are (a) insertion only (the cash-register model); or (b) always non-negative (the strict Turnstile model), or (c) eventually non-negative at check points. This is only a minor restriction for practical applications.   Our method works particularly well when p = 1+/- \Delta and \Delta is small, which is a practically important scenario. For example, \Delta may be the decay rate or interest rate, which are usually small. Of course, when \Delta = 0, one can compute the 1th frequent moment (i.e., the sum) essentially error-free using a simple couter. Our method may be viewed as a ``genearlized counter'' in that it can count the total value in the future, taking in account of the effect of decaying or interest accruement.   In a summary, our contributions are two-fold. (A) This is the first propsal of skewed stable random projections. (B) Based on first principle, we develop various statistical estimators for skewed stable distributions, including their variances and error (tail) probability bounds, and consequently the sample complexity bounds. 
Ping Li