An -appro ximate quantile summary of a sequence of N elements is a data structure that can answer quantile queries about the sequence to within a precision of N. We presen t a new online algorithm for computing -appro ximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirementof O1 log N. This improvesupon the previous best result ofO1 log2 N. Moreover, in con trast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence. Finally, the actual space bounds obtained on experimental data are signi cantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.