space- and time-efficient deterministic algorithms for biased quantiles over data streams
Skew is prevalent in data streams, and should be taken into account by algorithms that analyze the data. The problem of finding "biased quantiles"that is, approximate quantiles which must be more accurate for more extreme valuesis a framework for summarizing such skewed data on data streams. We present the first deterministic algorithms for answering biased quantiles queries accurately with smallsublinear in the input sizespace and time bounds in one pass. The space bound is near-optimal, and the amortized update cost is close to constant, making it practical for handling high speed network data streams. We not only demonstrate theoretical properties of the algorithm, but also show it uses less space than existing methods in many practical settings, and is fast to maintain.
space timing algorithm biased quantiles data streaming data account approximation queries input update cost practical network demonstrate method maintainability  framework  efficiency values optimality size bias data stream algorithms