ABSTRACT
A self-balancing binary search tree is a data structure for solving point query searches. The tree maintains its height from foots to leaves and supports dynamic updates. Determine if a node containing a particular element exists in the tree by performing a binary tree search. Insert a new node in the correct place in the tree given its value. To analyze the complexity of a tango tree, we need to compute the costs of point search queries and updating. Skiplists are probabilistic data structures that may supplant balanced trees as implementations of choice in many applications. The cost of search operation in a binary search tree is computed as the number of comparisons, generally one comparison generally one comparison at every node of the search path, which is equal to the length of search path. The search cost of a search binary search tree algorithm is simply the depth of the node to be searched.
