Moses Charikar, Venkatesan Guruswami, et al.
Annual Symposium on Foundations of Computer Science - Proceedings
We present new algorithms for computing approximate quantiles of large datasets in a single pass. The approximation guarantees are explicit, and apply for arbitrary value distributions and arrival distributions of the dataset. The main memory requirements are smaller than those reported earlier by an order of magnitude. We also discuss methods that couple the approximation algorithms with random sampling to further reduce memory requirements. With sampling, the approximation guarantees are explicit but probabilistic, i.e. they apply with respect to a (user controlled) confidence parameter. We present the algorithms, their theoretical analysis and simulation results on different datasets. © 1998 ACM.
Moses Charikar, Venkatesan Guruswami, et al.
Annual Symposium on Foundations of Computer Science - Proceedings
Qiong Luo, Sailesh Krishnamurthy, et al.
SIGMOD 2002
Stephen Dill, Ravi Kumar, et al.
VLDB 2001
Bruce G. Lindsay
SIGMOD Record