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If I have a static set of $n$ items in a database that are all queried with uniform probability it makes sense to put them in a binary search tree. This way any given search will take, on average, $O(\ln n)$.

What happens now, if the items are not queried uniformly? As a silly example, say that one item is queried 1000 more frequently than any other item. Would it make sense check for that item first and independently, before looking in a search tree of the other items (the so-called LenPEG method)?

For concreteness, let's say the items are queried with a likelyhood that is static, and drawn from a Poisson distribution. What would be the optimal way to structure the data so that the average search time is minimized?

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    $\begingroup$ Hint: Huffman trees. (And as an aside: Splay trees.) $\endgroup$ – Pseudonym Sep 10 '14 at 14:43
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The Optimal Binary Search Tree is exactly what you need.

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