scholarly journals Nearly Balanced Quad List Quad Tree -A Data Structure for VLSI Layout Systems

VLSI Design ◽  
1996 ◽  
Vol 4 (1) ◽  
pp. 17-32
Author(s):  
Pei-Yung Hsiao

In the past ten years, many researchers have focused attention on developing better data structures for storing graphical information. Among the proposed data structures, the quad tree data structure provides a good way to organize objects on a 2-D plane. Region searches proceed at logarithmic speeds a desirable characteristic, but no previously proposed VLSI quad tree data structure distributed objects to subdivide the spatial area. This has been a major drawback for operations such as tree searching and window query. In this paper, we present a new division method to reconstruct those quad trees including the multiple storage quad tree (MSQT) and the quad list quad tree (QLQT) into nearly balanced quad tree data structures. Nearly balanced quad trees based on our new spatial division method are constructed by dynamically translating unbalanced multiple storage quad trees or unbalanced quad list quad trees into balanced structures. All benefits of the original quad tree data structures are completely retained. In addition, this method is simple and balanced quad trees memory require less than the original quad trees. Experimental results illustrate that the improvement in region queries of the presented nearly balanced quad trees to both of the QLQT and the MSQT is better than the improvement of the QLQT to the MSQT.

1999 ◽  
Vol 10 (01) ◽  
pp. 1-17 ◽  
Author(s):  
SEONGHUN CHO ◽  
SARTAJ SAHNI

We show that the leftist tree data structure may be adapted to obtain data structures that permit the double-ended priority queue operations Insert, DeleteMin, DeleteMax, and Merge to be done in O( log n) time where n is the size of the resulting queue. The operations FindMin and FindMax can be done in O(1) time. Experimental results are also presented.


2011 ◽  
Vol 48-49 ◽  
pp. 767-772 ◽  
Author(s):  
Rui Hang Shi ◽  
Lei Luo ◽  
Zhen Qiang Yao

In this paper, a method of automatic NC code programming is studied. The data structure used to store graph information is the base of automatic programming system. By utilizing the object-oriented tree data structure proposed in the paper, the disadvantages of the method caused by using simple data structures can be overcome. In processing information, the judgment of topological relationship between two outlines is very important. The Loop Extremum Method is easy, convenient and with no limitations. The system adds plenty of technological parameters, provides multiple machining patterns. In the end, an example is given to test and verify the system`s functions.


2014 ◽  
Vol 10 (1) ◽  
pp. 42-56 ◽  
Author(s):  
Zailani Abdullah ◽  
Tutut Herawan ◽  
A. Noraziah ◽  
Mustafa Mat Deris

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.


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