jm + q-digest   2

'Medians and Beyond: New Aggregation Techniques for Sensor Networks' [paper, PDF]
'We introduce Quantile Digest or q-digest, a novel data structure which provides provable guarantees on approximation error and maximum resource consumption. In more concrete terms, if the values returned by the sensors are integers in the range [1;n], then using q-digest we can answer quantile queries using message size m within an error of O(log(n)/m). We also outline how we can use q-digest to answer other queries such as range queries, most frequent items and histograms. Another notable property of q-digest is that in addition to the theoretical worst case bound error, the structure carries with itself an estimate of error for this particular query.'
q-digest  algorithms  streams  approximation  histograms  median  percentiles  quantiles 
february 2013 by jm
clearspring / stream-lib
ASL-licensed open source library of stream-processing/approximation algorithms: count-min sketch, space-saving top-k, cardinality estimation, LogLog, HyperLogLog, MurmurHash, lookup3 hash, Bloom filters, q-digest, stochastic top-k
algorithms  coding  streams  cep  stream-processing  approximation  probabilistic  space-saving  top-k  cardinality  estimation  bloom-filters  q-digest  loglog  hyperloglog  murmurhash  lookup3 
february 2013 by jm

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