The Britney Spears Problem
Tracking who's hot and who's not presents an algorithmic challenge
Getting It Almost Right
For many stream problems of practical interest, computing exact answers is simply not feasible—but it's also not necessary. A good estimate, or an answer that's probably correct, will serve just fine.
One simple approach to approximation is to break a stream into blocks, turning a continuous process into a series of batch operations. Within a block, you are not limited to algorithms that obey the one-pass-only rule. On the other hand, answers derived from a sequence of blocks only approximate answers for the stream as a whole. For example, in a count of distinct elements, an item counted in two successive blocks would be counted only once if the blocks were combined.
A variant of the block idea is the sliding window. A window of length k holds the most recent k elements from the stream, with each new arrival displacing the oldest item in the queue.
A great deal of ingenuity has been applied to the search for better approximate stream algorithms, and there are successes to report. Here I shall briefly mention work in two areas, based on sampling and on hashing.
When the items that interest you are the most frequent ones in a stream, statistics is on your side; any sample drawn from the stream is most likely to include those very items. Indeed, any random sample represents an approximate solution to the most-common-elements problem. But the simplest sampling strategy is not necessarily the most efficient or the most accurate. In 2002 Gurmeet Singh Manku and Rajeev Motwani of Stanford University described two methods they called sticky sampling and lossy counting.
Suppose you want to select a representative sample of 100 items from a stream. If the stream consists of just 100 elements, the task is easy: Take all the elements, or in other words select them with probability 1. When the stream extends to 200 elements, you can make a new random selection with probability 1/2; at 400 elements, the correct probability is 1/4, and so on. Manku and Motwani propose a scheme for continually readjusting the selection probability without having to start over with a new sample. Their sticky-sampling method refines the selection each time the stream doubles in length, maintaining counters that estimate how often each item appears in the full stream. The algorithm solves the most-frequent-elements problem in constant space, but only in a probabilistic sense. If you run the algorithm twice on the same data, the results will likely differ.
Lossy counting is based on a similar idea of continually refining a sample as the stream lengthens, but in this case the algorithm is deterministic, making no use of random choices. Each stream element is checked against a stored list; if the element is already present, a counter is incremented; otherwise a new entry is added. To keep the list from growing uncontrollably, it is periodically purged of infrequent elements. Lossy counting is not guaranteed to run in constant space, but Manku and Motwani report that in practice it performs better than sticky sampling.
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