Second frequency moment — where it appears
Named by 4 essays across 3 fields — each of them below, with the objects they name alongside it.
Also named here as tug-of-war — the same set of essays touches all of them, so they are one junction rather than several.
The estimate that squares the stream
The length of a stream is a counter and the number of distinct keys is a register bank. The sum of the squared frequencies has nothing obvious to count — and one number, one sign per key, and a squaring get within 4% of it in a fortieth of the space.
The independence an estimator spends
Every sketch's analysis begins by assuming a truly random hash, and nobody comes back to that line. Independence has a degree, the degree is enumerable over a small field, and an estimator's mean and its variance spend different amounts of it.
A sketch that is allowed to be under
Count-Min's estimate is never below the truth, and it pays for that with an error proportional to the whole stream. Give every key a sign and take a median instead, and the same table is 2.7 times more accurate on the keys anybody asks about — and wrong in both directions.
The moments past the second
Past the second moment no sign trick works, and the estimator that does — pick one position of the stream, count how often its key comes again, report m(r^k − (r − 1)^k) — is unbiased for every moment on every stream. Its variance can be computed exactly rather than sampled, and on real streams it is nowhere near its guarantee: 103 copies give F3 to ten per cent on a flat stream of 2,048 keys, and 2,630 on a moderately skewed one. The guarantee asks for 48,381, and it is not loose. One heavy key among 2,047 keys seen once needs 11,799.
Named alongside it
The objects these essays reach for when they reach for this one.
Tug-of-warUnbiased estimatorEstimatorMedian of meansVarianceZipf distributionHash familyHonest limitRelative errorSketchState bitsStreaming algorithm