A review on tree traversal techniques (original) (raw)
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A New Look to Traversal Algorithms Using Set Construct Data Structure
2013
Abstract—Tree traversal refers to the process of visiting or examining or updating each node in a tree data structure, exactly once, in a systematic way. Such traversals are classified by the order in which the nodes of the tree are visited. This paper presents a new and innovative technique using which traversing in trees as well as in graph becomes extremely easy and using this technique explanation & understanding of traversing in trees & graphs also becomes easy using set construct.
Recursive and Nonrecursive Traversal Algorithms for Dynamically Created Binary Trees
The modeling of dynamical systems from a time series implemented by our DSA program introduces binary trees of height with all leaves on the same level, and the related subtrees of height L <= D. These are called epsilon-trees and epsilon-subtrees. The recursive and nonrecursive versions of the traversal algorithms for the trees with dynamically created nodes are discussed. The original nonrecursive algorithms that return the pointer to the next node in preorder, inorder and postorder traversals are presented. The space-time complexity analysis shows, and the execution time measurements confirm, that for these algorithms the recursive versions have approximately 10-25% better time constants. Still, the use of nonrecursive algorithms may be more appropriate in several occasions.
Dynamic Trees in Data Structure
A data structure is proposed to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. The tree is one of the most powerful of the advanced data structures and it often pops up in even more advanced subjects such as AI and compiler design. Surprisingly though the tree is important in a much more basic application-namely the keeping of an efficient index. This research paper gives us brief description of importance of tree in data structure, types of trees, implementation with their examples.
Processing Tree-like Data Structures in Different Computing Platforms
The paper analyzes and compares three different computing platforms for processing tree-like data structures, namely: general purpose computers, embedded processors, and direct mapping of the relevant algorithms to hardware in application-specific circuits. Tree-based recursive data sorting is considered as a case study. The results demonstrate that application-specific hardware is the fastest and processorbased implementation is the slowest. This gives motivation for developing new optimization techniques in the scope of application-specific hardware circuits, which is especially beneficial for FPGA-based design.
Comparison of Advance Tree Data Structures
B-tree and R-tree are two basic index structures; many different variants of them are proposed after them. Different variants are used in specific application for the performance optimization. In this paper different variants of B-tree and R-tree are discussed and compared. Index structures are different in terms of structure, query support, data type support and application. Index structure’s structures are discussed first. B-tree and its variants are discussed and them R-tree and its variants are discussed. Some structures example is also shown for the more clear idea. Then comparison is made between all structure with respect to complexity, query type support, data type support and application.
A file structure supporting traversal recursion
ACM SIGMOD Record, 1989
Traversal recursion is a class of recursive queries where the evaluation of the query involves traversal of a graph or a tree. This limited type of recursion arises in many applications. In this report we investigate a simple file structure that efficiently supports traversal recursion over large, acyclic graphs. The nodes of the graph are sorted in topological order and stored in a B-tree. Hence, traversal of the graph can be done in a single scan. Nodes and edges can also be inserted, deleted, and modified efficiently.
Tree Reconstruction and Bottom-Up Evaluation of Tree Pattern Queries
2010 International Conference on Information Science and Applications, 2010
Since the extensible markup language XML emerged as a new standard for information representation and exchange on the Internet, the problem of storing, indexing, and querying XML documents has been among the major issues of database research. In this paper, we study the tree pattern matching and discuss a new algorithm for processing ordered tree pattern queries, by which not only ancestor/descendant relationships, but also left-to-right ordering of query nodes are considered. Such kind of tree matching has many applications in practice, such as the linguistic analysis, the video content-based retrieval, as well as the computational biology and the data mining. The time complexities of the new algorithm is bounded by O(|D| ⋅ |Q| + |T| ⋅ leaf Q) and its space overhead is by O(leaf T ⋅ leaf Q), where T stands for a document tree, Q for a tree pattern and D is the largest data stream among all the data streams associated with the nodes in Q. Each data stream contains the database nodes that match the predicate at a node q. leaf T (leaf Q) represents the number of the leaf nodes of T (resp. Q). In addition, the algorithm can be adapted to an indexing environment with XB-trees being used. Experiments have been conducted, which shows that our algorithm is promising.
A data tree structure for a hierarchical structure processing
Computers & Industrial Engineering, 1994
In this electronic age, the manufacturing sector has been searching for a unique data structure scheme which can easily represent different data types relating to design, process and inventory management. In this paper, an efficient way of representing the hierarchically significant data, especially in manufacturing, is investigated. Unlike the contemporary tree structure representation using pointers, this approach adopts a unique arithmetic encoding scheme to represent individual paths. The encoded nodes will have the capability to regenerate the entire paths (of a tree) using simple arithmetic procedures. The most significant benefits of this approach are increased data processing efficiency and ease of navigation of data paths in hierarchical data structures. The preliminary testing of this approach on a simulated factory data have indicated significant improvement on data representation and processing efficiency. This structure promises application in Computer Aided Design, Manufacturing Resources Planning, and Hypermedia Data Structure.
2012
Binary tree traversal refers to the process of visiting each node in a specified order. Given the inorder traversal of a binary tree, along with one of its preorder or postorder traversals, the original binary tree can be uniquely identified. Many recursive and non recursive method of construction of the tree from inorder and any of the postorder or preorder traversal have been proposed. In this paper one of the proposed algorithms has been examined. This algorithm computes the wrong tree for some input sequences. We show a particular situation in which the algorithm fails and a solution for this situation is proposed. The proposed a modified non-recursive algorithm for reconstructing a binary tree which generates the correct tree otherwise an error has been reported.