• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.217-222
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• 2009

Priority queues can be used in applications such as scheduling, sorting, retrival based on a priority like gene searching, shortest paths computation. This paper proposes a data structure using array representation for double-ended priority queue in which insertion and deletion takes O(1) amortized time and O(logn) time, respectively. To the author's knowledge, all the published array-based data structures for double ended priority queue support O(logn) time insertion and deletion operations.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.29-34
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• 2019

Priority queues, one of the fundamental data structures, have been studied for a long time by computer scientists. This paper proposes an implicit double-ended priority queue, called IMI-heap, in which insert operation takes constant amortized time and each of removal operation of the minimum key or the maximum key takes O(logn) time. To the author's knowledge, all implicit double-ended priority queues that have been published, perform insert, removeMin and removeMax operations in O(logn) time each. So, the proposed IMI-heap is superior than the published heaps in terms of insertion time complexity.The abstract should concisely state what was done, how it was done, principal results, and their significance.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.577-582
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• 2004

Double-ended Proirity queues(DEPQ) can be used in applications such as scheduling or sorting. The data structures for DEPQ can be con-structed with or without pointers. The implicit representation without pointers uses less memory space than pointer-based representation. This paper presents a novel fast implicit heap called 4-deapr$\ast$ which utilizes cache memory efficiently. Experimental results show that the 4-deap$\ast$ is faster than symmetric min-max heap as well as deap.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.73-82
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• 1995

This paper presents a data structure that implements a mergeable double-ended priority queue ; namely, an improved relaxed min-max-pair heap. It suggests a sequential algorithm to merge priority queues organized in two relaxed min-max heaps : kheap and nheap of sizes k and n, respecrively. This new data sturuture eliminates the blossomed tree and the lazying method used to merge the relaxed min-max heaps in [8]. As a result, the suggested method in this paper requires the time complexity of O(log(log(n/k))*log(k)) and the space complexity of O(n+), assuming that $k{\leq}{\lfloor}log(size(nheap)){\rfloor}$ are in two heaps of different sizes.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1162-1171
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• 1998

This paper presents a data structure that implements a mergable double-ended priority queue : namely an improved relaxed min-max-pair heap. By means of this new data structure, we suggest a parallel algorithm to merge priority queues organized in two relaxed heaps of different sizes, n and k, respectively. This new data-structure eliminates the blossomed tree and the lazying method used to merge the relaxed min-max heaps in [9]. As a result, employing max($2^{i-1}$,[(m+1/4)]) processors, this algorithm requires O(log(log(n/k))${\times}$log(n)) time. Also, on the MarPar machine, this method achieves a 35.205-fold speedup with 64 processors to merge 8 million data items which consist of two relaxed min-max heaps of different sizes.