Binary Trie Split Algorithm for Unbalanced Trees

Binary Tries (continued) • split(k). • Similar to split algorithm for unbalanced binary search trees. • Construct S and B on way down the trie. • Follow with a backward cleanup pass over the constructed S and B.

x a b a Forward Pass • Suppose you are at node x, which is at level j of the input trie. • If bit j of k is 1, move to root of b and add to level j of S and to level j of B.

b x a b Forward Pass • If bit j of k is 0, move to root of a and add to level j of B and to level j of S.

a b c d e f g Forward Pass Example S = null B = null k = g.key = 101011

S B a b c d e f g Forward Pass Example k = g.key = 101011

S B a b c e f g Forward Pass Example d k = g.key = 101011

S B a b c d e f Forward Pass Example k = g.key = 101011

S B a b c d e f Forward Pass Example g

Backward Cleanup Pass • Retrace path from current nodes in S and B toward roots of respective tries. • Eliminate branch nodes that are roots of subtries that have fewer than 2 dictionary pairs.

S B a b c d e f Backward Cleanup Pass Example Suppose fis an element node.

S B a b c d e Backward Cleanup Pass Example Now backup on B. f

S B a b c d e Backward Cleanup Pass Example Now backup on B. Assume root of d is a branch node. f

S B a b c d e Backward Cleanup Pass Example Complexity of split is O(height). f

Compressed Binary Tries • No branch node whose degree is 1. • Add a bit# field to each branch node. • bit# tells you which bit of the key to use to decide whether to move to the left or right subtrie.

0 1 0 0 1 0 1 0 0 0 1 0 1 1001 0011 0001 1100 1101 1000 Binary Trie 1 2 3 4 4 bit# field shown in black outside branch node.

Compressed Binary Trie 1 0 1 3 2 0 1 0 1 0001 0011 4 4 0 1 0 1 1000 1001 1100 1101 bit# field shown in black outside branch node.

1 0 1 3 2 0 1 0 1 4 4 0 1 0 1 1001 1000 1100 1101 0011 0001 Compressed Binary Trie #branch nodes = n – 1.

1 0 1 3 2 0 1 0 1 4 4 0 1 0 1 1001 1000 1100 1101 0011 0001 Insert Insert 0010.

1 0 1 3 2 0 1 0 1 4 4 0 1 4 0 1 0 1 0010 0011 1100 1001 1000 0001 1101 Insert Insert 0100.

0 1 0011 0010 0001 1000 1001 0100 1101 1100 Insert 1 0 1 2 2 1 0 1 0 3 4 4 0 1 0 1 4 0 1

1 0 1 2 2 1 0 1 0 3 4 4 0 1 0 1 0 1 1100 1001 1000 0010 0011 0001 1101 0100 4 0 1 Delete Delete 0010.

1 0 1 2 2 1 0 1 0 3 4 4 0 1 0 1 0 1 0011 1101 1100 1000 0001 1001 0100 Delete Delete 1001.

1 0 1 2 2 1 0 1 0 3 4 0 1 0 1 0011 1101 1100 1000 0001 0100 Delete

Split(k) • Similar to splitting an uncompressed binary trie.

Join(S,m,B) • 3-way to 2-way join • Insert m into B to get B’. • |S| <= 1 or |B’| = 1 handled as special cases as in the case of uncompressed tries. • When |S| > 1 and |B’| > 1, let Smaxbe the largest key in S and let B’min be the smallest key in B’. • Let d be the first bit that is different in Smax andB’min.

bit#(S) bit#(B’) 0 1 0 1 a b c d B’ S Cases To Consider • d < min{bit#(S), bit#(B’)} • d >= min{bit#(S), bit#(B’)} • bit#(S) = bit#(B’) • bit#(S) < bit#(B’) • bit#(S) > bit#(B’)

bit#(S) bit#(B’) 0 1 0 1 a b c d d B’ S 0 1 S B’ d < min{bit#(S), bit#(B’)} Bit d ofSmax must be 0.

s s 0 1 0 1 a b c d B’ S bit#(S) = bit#(B’) • Not possible, because keys in b are larger than those in c. • However, all keys in S are supposed to be smaller than those in B’.

s s b’ 0 1 0 1 0 1 a J(b,B’) a b c d B’ S bit#(S) < bit#(B’) = +

b’ s b’ 0 1 0 1 0 1 J(S,c) d a b c d B’ S bit#(S) > bit#(B’) = + Complexity is O(max{height(S), height(B)}). Smax and B’min are found just once.

PATRICIA • Practical Algorithm To Retrieve Information Coded In Alphanumeric. • Compressed binary trie. • All nodes are of the same data type (binary tries use branch and element nodes). • Pointers to only one kind of node. • Simpler storage management.

1 0 1 3 2 0 1 0 1 4 4 0 1 0 1 1001 1000 1100 1101 0011 0001 Compressed Binary Trie To Patricia Move each element into an ancestor or header node.

1000 1100 0011 1101 0001 1001 Compressed Binary Trie To Patricia 0 1 0 1 3 2 0 1 0 1 4 4 1 1 0 0

Binary Trie Split Algorithm for Unbalanced Trees