Sample marks — where it appears
Named by 10 essays across 7 fields — each of them below, with the objects they name alongside it.
Twenty bits apart
Two representations of one sparse set, six thousand seven hundred and forty-five bits against six thousand seven hundred and sixty-five. One exploits sparsity and the other exploits runs, and on this set at this density they price identically.
The flat bottom of a shallow curve
The low width is chosen as the floor of log of the universe over the count. Rounding it up instead costs one bit on five thousand, because the total is m·w plus n over two to the w and the minimum is where those two are equal.
The array that says where is twice the samples
An index keeps one suffix-array value in every thirty-two, and a bit vector over all n rows saying which. The vector is sixteen thousand bits and the values it points at are seven thousand — the index of the samples is twice the samples.
What the locating apparatus becomes
The two parts that answer "where" are half an index at a dense sampling and a fifth at a sparse one, and the fifth does not fall further. Represent the marks properly and it keeps falling, to under four per cent.
Three savings on one structure
A factor of eleven on an extension, a factor of seven on a branching search, and a sixth of the bits. Applied to one bidirectional index they do not give a factor of seventy-seven, and the reason is that two of the three are the same saving.
A position split in two
Write each sorted position as a high part and a low part. Store the low parts packed and the high parts as a bit vector in which the k-th one sits at position (p >> w) + k. A select on that vector and a low read recover any position.
The ladder, and the rung that spends
Two hundred and fifty thousand bits, then two hundred and twenty-six, then two hundred and eleven. The fourth rung takes the whole saving and buys a four-times denser sampling with it, landing at ninety-six per cent of where it started and locating several times faster.
Where the sparse representation loses
At every row marked, Elias–Fano costs twice the plain vector. The crossing is at one row in four, which is a sampling rate a real index uses — so the choice between the two is a choice, not an improvement.
A price with no structure under it
A class in this collection has charged Elias–Fano's price for several strands and stores an array of positions searched by binary search. The accounting is right about space to a bit per thousand and wrong about one operation by a factor of eight.
The floor was the marks
A saving reported as about a sixth of a bidirectional index, falling to an eighth and levelling off. Represent one array properly and it falls to a fiftieth instead — most of what was being dropped was a badly encoded bit vector.
Named alongside it
The objects these essays reach for when they reach for this one.
Index sizeElias fanoBit vectorLocatingSpace accountingSparse setBidirectional indexSelf-indexSuffix array samplingCheckCrossing pointRank