Results 1  10
of
17
Fast Meldable Priority Queues
, 1995
"... We present priority queues that support the operations MakeQueue, FindMin, Insert and Meld in worst case time O(1) and Delete and DeleteMin in worst case time O(log n). They can be implemented on the pointer machine and require linear space. The time bounds are optimal for all implementations wh ..."
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Cited by 11 (2 self)
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We present priority queues that support the operations MakeQueue, FindMin, Insert and Meld in worst case time O(1) and Delete and DeleteMin in worst case time O(log n). They can be implemented on the pointer machine and require linear space. The time bounds are optimal for all implementations where Meld takes worst case time o(n).
Weight Biased Leftist Trees and Modified Skip Lists
 Journal of Experimetnal Algorithmics
, 1996
"... this paper, we are concerned primarily with the insert and delete min operations. The different data structures that have been proposed for the representation of a priority queue differ in terms of the performance guarantees they provide. Some guarantee good performance on a per operation basis whil ..."
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Cited by 10 (1 self)
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this paper, we are concerned primarily with the insert and delete min operations. The different data structures that have been proposed for the representation of a priority queue differ in terms of the performance guarantees they provide. Some guarantee good performance on a per operation basis while others do this only in the amortized sense. Heaps permit one to delete the min element and insert an arbitrary element into an n element priority queue in O(logn) time per operation; a find min takes O(1) time. Additionally, a heap is an implicit data structure that has no storage overhead associated with it. All other priority queue structures are pointerbased and so require additional storage for the pointers. Leftist trees also support the insert and delete min operations in O(log n) time per operation and the find min operation in O(1) time. Additionally, they permit us to meld pairs of priority queues in logarithmic time
Two new methods for transforming priority queues into doubleended priority queues
 CPH STL Report
, 2006
"... Abstract. Two new ways of transforming a priority queue into a doubleended priority queue are introduced. These methods can be used to improve all known bounds for the comparison complexity of doubleended priorityqueue operations. Using an efficient priority queue, the first transformation can pr ..."
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Cited by 5 (5 self)
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Abstract. Two new ways of transforming a priority queue into a doubleended priority queue are introduced. These methods can be used to improve all known bounds for the comparison complexity of doubleended priorityqueue operations. Using an efficient priority queue, the first transformation can produce a doubleended priority queue which guarantees the worstcase cost of O(1) for findmin, findmax, and insert; and the worstcase cost of O(lg n) including at most lg n + O(1) element comparisons for delete, but the data structure cannot support meld efficiently. Using a meldable priority queue that supports decrease efficiently, the second transformation can produce a meldable doubleended priority queue which guarantees the worstcase cost of O(1) for findmin, findmax, and insert; the worstcase cost of O(lg n) including at most lg n + O(lg lg n) element comparisons for delete; and the worstcase cost of O(min {lg m, lg n}) for meld. Here, m and n denote the number of elements stored in the data structures prior to the operation in question, and lg n is a shorthand for log 2 (max {2, n}). 1.
A comparative analysis of three different priority deques
, 2001
"... Abstract. In this project we compare the practical effectiveness of three different algorithms for “priority deques”, namely MinMaxheaps, The Deap and Interval Heaps. By implementing the algorithms and running benchmarks we find that interval heaps are the most effective, mainly due to its simplici ..."
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Cited by 4 (0 self)
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Abstract. In this project we compare the practical effectiveness of three different algorithms for “priority deques”, namely MinMaxheaps, The Deap and Interval Heaps. By implementing the algorithms and running benchmarks we find that interval heaps are the most effective, mainly due to its simplicity and similarity to standard heaps. We also discover some crucial shortcomings of Svante Carlsson’s deap algorithm and propose solutions for these. Our code is targeted towards submission to the Copenhagen STL project so we implement a “PriorityDeque”class, in which the programmer can choose the underlying algorithm.
Optimal Median Smoothing
, 1994
"... Median smoothing of a series of data values is considered. Naive programming of such an algorithm would result in large amount of computation, especially when the series of data values is long. By maintaining a heap structure that we update when moving along the data we obtain an optimal median smoo ..."
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Cited by 2 (0 self)
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Median smoothing of a series of data values is considered. Naive programming of such an algorithm would result in large amount of computation, especially when the series of data values is long. By maintaining a heap structure that we update when moving along the data we obtain an optimal median smoothing algorithm.
Efficient Fuzzy TypeAhead Search in XML Data
 IEEE TKDE
"... Abstract—In a traditional keywordsearch system over XML data, a user composes a keyword query, submits it to the system, and retrieves relevant answers. In the case where the user has limited knowledge about the data, often the user feels “left in the dark ” when issuing queries, and has to use a t ..."
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Cited by 2 (1 self)
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Abstract—In a traditional keywordsearch system over XML data, a user composes a keyword query, submits it to the system, and retrieves relevant answers. In the case where the user has limited knowledge about the data, often the user feels “left in the dark ” when issuing queries, and has to use a tryandsee approach for finding information. In this paper, we study fuzzy typeahead search in XML data, a new informationaccess paradigm in which the system searches XML data on the fly as the user types in query keywords. It
Memory Efficient PropagationBased Watershed and Influence Zone Algorithms for Large Images
 IEEE Transactions on Image Processing
, 2000
"... Propagation front or grassfire methods are very popular in image processing because of their efficiency and because of their inherent geodesic nature. However, because of their randomaccess nature, they are inefficient in large images that cannot fit in available random access memory. In this paper ..."
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Cited by 1 (0 self)
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Propagation front or grassfire methods are very popular in image processing because of their efficiency and because of their inherent geodesic nature. However, because of their randomaccess nature, they are inefficient in large images that cannot fit in available random access memory. In this paper, we explore ways to increase the memory efficiency of two algorithms that use propagation fronts: the skeletonization by influence zones and the watershed transform. Two algorithms are presented for the skeletonization by influence zones. The first computes the skeletonization on surfaces without storing the enclosing volume. The second performs the skeletonization without any region reference, by using only the propagation fronts. The watershed transform algorithm that was developed keeps in memory the propagation fronts and only one greylevel of the image. All three algorithms use much less memory than the ones presented in the literature so far. Several techniques have been developed in ...
Mergeable DoubleEnded Priority Queues
, 1999
"... We show that the leftist tree data structure may be adapted to obtain data structures that permit the doubleended priority queue operations Insert, DeleteMin, DeleteMax, and Merge to be done in O(logn) time where n is the size of the resulting queue. The operations FindMin and FindMax can be don ..."
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Cited by 1 (0 self)
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We show that the leftist tree data structure may be adapted to obtain data structures that permit the doubleended priority queue operations Insert, DeleteMin, DeleteMax, and Merge to be done in O(logn) 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.
1 Signal Codes
, 806
"... Abstract — Motivated by signal processing, we present a new class of channel codes, called signal codes, for continuousalphabet channels. Signal codes are lattice codes whose encoding is done by convolving an integer information sequence with a fixed filter pattern. Decoding is based on the bidirect ..."
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Cited by 1 (1 self)
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Abstract — Motivated by signal processing, we present a new class of channel codes, called signal codes, for continuousalphabet channels. Signal codes are lattice codes whose encoding is done by convolving an integer information sequence with a fixed filter pattern. Decoding is based on the bidirectional sequential stack decoder, which can be implemented efficiently using the heap data structure. Error analysis and simulation results indicate that signal codes can achieve low error rate at approximately 1dB from channel capacity. I.
Correspondence Based Data Structures for Double Ended Priority Queues
"... this paper is to demonstrate the generality of two techniques used in [6] to develop an MDEPQ representation from an MPQ representation  height biased leftist trees. These methods  total correspondence and leaf correspondence  may be used to arrive at efficient DEPQ and MDEPQ data structures from ..."
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this paper is to demonstrate the generality of two techniques used in [6] to develop an MDEPQ representation from an MPQ representation  height biased leftist trees. These methods  total correspondence and leaf correspondence  may be used to arrive at efficient DEPQ and MDEPQ data structures from PQ and MPQ data structures such as the pairing heap [8; 18], Binomial and Fibonacci heaps [9], and Brodal's FMPQ [2] which also provide efficient support for the operation: Delete(Q,p): delete and return the element located at p We begin, in Section 2, by reviewing a rather straightforward way, dual priority queues, to obtain a (M)DEPQ structure from a (M)PQ structure. This method [2; 6] simply puts each element into both a minPQ and a maxPQ. In Section 3, we describe the total correspondence method and in Section 4, we describe leaf correspondence. Both sections provide examples of PQs and MPQs and the resulting DEPQs and MDEPQs. Section 5 gives complexity results. In Section 6, we provide the result of experiments that compare the performance of the MDEPQs based on height biased leftist tree [7], pairing heaps [8; 18], and FMPQs [2]. For reference purpose, we also provide run times for the splay tree data structure [16]. Although splay trees were not specifically designed to represent DEPQs, it is easy min Heap max Heap Fig. 1. Dual heap structure to use them for this purpose. Note that splay trees do not provide efficient support for the Meld operation