Fast range-summable random variables for efficient aggregate estimation

Exact computation for aggregate queries usually requires large amounts of memory - constrained in data-streaming - or communication - constrained in distributed computation - and large processing times. In this situation, approximation techniques with provable guarantees, like sketches, are the only viable solution. The performance of sketches crucially depends on the ability to efficiently generate particular pseudo-random numbers. In this paper we investigate both theoretically and empirically the problem of generating k-wise independent pseudo-random numbers and, in particular, that of generating 3 and 4-wise independent pseudo-random numbers that are fast range-summable (i.e., they can be summed up in sub-linear time). Our specific contributions are: (a) we provide an empirical comparison of the various pseudo-random number generating schemes, (b) we study both theoretically and empirically the fast range-summation practicality for the 3 and 4-wise independent generating schemes and we provide efficient implementations for the 3-wise independent schemes, (c) we show convincing theoretical and empirical evidence that the extended Hamming scheme performs as well as any 4-wise independent scheme for estimating the size of join using AMS-sketches, even though it is only 3-wise independent. We use this generating scheme to produce estimators that significantly out-perform the state-of-the-art solutions for two problems - size of spatial joins and selectivity estimation.