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103
Algorithms for Fast Vector Quantization
 Proc. of DCC '93: Data Compression Conference
, 1993
"... Nearest neighbor searching is an important geometric subproblem in vector quantization. ..."
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Cited by 75 (11 self)
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Nearest neighbor searching is an important geometric subproblem in vector quantization.
WorstCase Efficient ExternalMemory Priority Queues
 In Proc. Scandinavian Workshop on Algorithms Theory, LNCS 1432
, 1998
"... . A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from ..."
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Cited by 36 (3 self)
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. A priority queue Q is a data structure that maintains a collection of elements, each element having an associated priority drawn from a totally ordered universe, under the operations Insert, which inserts an element into Q, and DeleteMin, which deletes an element with the minimum priority from Q. In this paper a priorityqueue implementation is given which is efficient with respect to the number of block transfers or I/Os performed between the internal and external memories of a computer. Let B and M denote the respective capacity of a block and the internal memory measured in elements. The developed data structure handles any intermixed sequence of Insert and DeleteMin operations such that in every disjoint interval of B consecutive priorityqueue operations at most c log M=B N M I/Os are performed, for some positive constant c. These I/Os are divided evenly among the operations: if B c log M=B N M , one I/O is necessary for every B=(c log M=B N M )th operation ...
Dominators in Linear Time
, 1997
"... A linear time algorithm is presented for finding dominators in control flow graphs. ..."
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Cited by 34 (0 self)
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A linear time algorithm is presented for finding dominators in control flow graphs.
Fairness in Periodic RealTime Scheduling
, 1995
"... The issue of temporal fairness in periodic realtime scheduling is considered. It is argued that such fairness is often a desirable characteristic in realtime schedules. A concrete criterion for temporal fairness  pfairness  is described. The weightmonotonic scheduling algorithm, a static prio ..."
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Cited by 31 (3 self)
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The issue of temporal fairness in periodic realtime scheduling is considered. It is argued that such fairness is often a desirable characteristic in realtime schedules. A concrete criterion for temporal fairness  pfairness  is described. The weightmonotonic scheduling algorithm, a static priority scheduling algorithm for generating pfair schedules, is presented and proven correct. A feasibility test is presented which, if satisfied by a system of periodic tasks, ensures that the weightmonotonic scheduling algorithm will schedule the system in a pfair manner.
Finding maximal pairs with bounded gap
 Proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching (CPM), volume 1645 of Lecture Notes in Computer Science
, 1999
"... A pair in a string is the occurrence of the same substring twice. A pair is maximal if the two occurrences of the substring cannot be extended to the left and right without making them different. The gap of a pair is the number of characters between the two occurrences of the substring. In this pape ..."
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Cited by 29 (5 self)
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A pair in a string is the occurrence of the same substring twice. A pair is maximal if the two occurrences of the substring cannot be extended to the left and right without making them different. The gap of a pair is the number of characters between the two occurrences of the substring. In this paper we present methods for finding all maximal pairs under various constraints on the gap. In a string of length n we can find all maximal pairs with gap in an upper and lower bounded interval in time O(n log n + z) where z is the number of reported pairs. If the upper bound is removed the time reduces to O(n+z). Since a tandem repeat is a pair where the gap is zero, our methods can be seen as a generalization of finding tandem repeats. The running time of our methods equals the running time of well known methods for finding tandem repeats.
The kClient Problem
 Journal of Algorithms
, 2001
"... Virtually all previous research in online algorithms has focused on singlethreaded systems where only a single sequence of requests compete for system resources. To model multithreaded online systems,we define and analyze the kclient problem,a dual of the wellstudied kserver problem. In the basi ..."
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Cited by 25 (1 self)
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Virtually all previous research in online algorithms has focused on singlethreaded systems where only a single sequence of requests compete for system resources. To model multithreaded online systems,we define and analyze the kclient problem,a dual of the wellstudied kserver problem. In the basic kclient problem,there is a single server and k clients,each of which generates a sequence of requests for service in a metric space. The crux of the problem is deciding which client’s request the single server should service rather than which server should be used to service the current request. We also consider variations where requests have nonzero processing times and where there are multiple servers as well as multiple clients. We evaluate the performance of algorithms using several cost functions including maximum completion time and average completion time. Two of the main results we derive are tight bounds on the performance of several commonly studied disk lg k scheduling algorithms and lower bounds of + 1 on the competitive ratio of any 2 online algorithm for the maximum completion time and average completion time cost functions when k is a power of 2. Most of our results are essentially identical for the maximum completion time and average completion time cost functions.
Efficient and Flexible Fair Scheduling of Realtime Tasks on Multiprocessors
 University
, 2003
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The Soft Heap: An Approximate Priority Queue with Optimal Error Rate
 J. ACM
, 2000
"... A simple variant of a priority queue, called a soft heap, is introduced. The data structure supports the usual operations: insert, delete, meld, and findmin. Its novelty is to beat the logarithmic bound on the complexity of a heap in a comparisonbased model. ..."
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Cited by 20 (1 self)
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A simple variant of a priority queue, called a soft heap, is introduced. The data structure supports the usual operations: insert, delete, meld, and findmin. Its novelty is to beat the logarithmic bound on the complexity of a heap in a comparisonbased model.