We show that, in order to achieve efficient maintenance of a balanced binary search tree, no shape restriction other than a logarithmic height is required. The obtained class of trees, general balanced trees, may be maintained at a logarithmic amortized cost with no balance information stored in the nodes. Thus, in the case when amortized bounds are sufficient, there is no need for sophisticated balance criteria. The maintenance algorithms use partial rebuilding. This is important for certain applications and has previously been used with weight-balanced trees. We show that the amortized cost incurred by general balanced trees is lower than what has been shown for weight-balanced trees. � 1999 Academic Press 1.

First we present a generalization of symmetric binary B-trees, SBB(k)- trees. The obtained structure has a height of only \Sigma (1 + 1k) log(n + 1)\Upsilon, where k may be chosen to be any positive integer. The maintenance algorithms require only a constant number of rotations per updating operation in the worst case. These properties together with the fact that the structure is relatively simple to implement makes it a useful alternative to other search trees in practical applications. Then, by using an SBB(k)-tree with a varying k we achieve a structure with a logarithmic amortized cost per update and a height of log n + o(log n). This result is an improvement of the upper bound on the height of a dynamic binary search tree. By maintaining two trees simultaneously the amortized cost is transformed into a worstcase cost. Thus, we have improved the worst-case complexity of the dictionary problem.

Binary search tree is a best-suited data structure for data storage and retrieval when entire tree could be accommodated in the primary memory. However, this is true only when the tree is height-balanced. Lesser the height faster the search will be. Despite of the wide popularity of Binary search trees there has been a major concern to maintain the tree in proper shape. In worst case, a binary search tree may reduce to a linear link list, thereby reducing search to be sequential. Unfortunately, structure of the tree depends on nature of input. If input keys are not in random order the tree will become higher and higher on one side. In addition to that, the tree may become unbalanced after a series of operations like insertions and deletions. To maintain the tree in optimal shape many algorithms have been presented over the years. Most of the algorithms are static in nature as they take a whole binary search tree as input to create a balanced version of the tree. In this paper, few techniques have been discussed and analyzed in terms of time and space requirement. Key words:

The balance criterion defining the class of ff-balanced trees states that the ratio between the shortest and longest paths from a node to a leaf be at least ff. We show that a straight-forward use of partial rebuilding for maintenance of ff-balanced trees requires an amortized cost of \Omega\Gamma p n) per update. By slight modifications of the maintenance algorithms the cost can be reduced to O(log n) for any value of ff, 0 ! ff ! 1. KEY WORDS ff-balanced trees, partial rebuilding, search trees. CR CATEGORIES: E.1, F.2, I.1.2. 1 Introduction In his thesis Olivie [9] introduced a class of binary search trees, which he calls ff-balanced trees, or ffBB-trees. Let h(v) denote the length for the longest path from a node v to a leaf and let s(v) denote the length of the shortest path. We give a formal definition of ff-balanced trees below. Definition 1 A binary tree is ff-balanced if the following is true for each node v in the tree: s(v) h(v) ff; h(v) 1 1 \Gamma ff (1) h(v) \...

We propose to maintain a Binary Search Tree in the form of a forest in such a way that – (a) it provides faster node access and, (b) it becomes more compatible with the concurrent environment. Using a small array, the stated goals were achieved without applying any restructuring algorithm. Empirically, we have shown that the proposed method brings down the total internal pathlength of a Binary Search Tree quite considerably. The experiments were conducted by creating two different data structures using the same input- a conventional binary search tree, and a forest of hashed trees. Our empirical results suggest that the forest so produced has lesser internal path length and height in comparison to the conventional tree. A binary search tree is not a well-suited data structure for concurrent processing. The evidence also shows that maintaining a large tree in form of multiple smaller trees (forest) increases the degree of parallelism.