ALGORITHMICA
2005
13 years 4 months ago
2005
ALGORITHMICA
2005
13 years 4 months ago
2005
In an adversarial queueing network, the incoming traffic is decided by an adversary, who operates under a reasonable rate restriction. This model provides a valuable, complementar...
ALGORITHMICA
2005
13 years 4 months ago
2005
We consider the setting of a web server that receives requests for documents from clients, and returns the requested documents over a multicast/broadcast channel. We compare the q...
ALGORITHMICA
2005
13 years 4 months ago
2005
Abstract. The Quality of Service Multicast Tree Problem is a generalization of the Steiner tree problem which appears in the context of multimedia multicast and network design. In ...
ALGORITHMICA
2005
13 years 4 months ago
2005
An optimal prefetching and I/O scheduling algorithm L-OPT, for parallel I/O systems, using a read-once model of block references is presented. The algorithm uses knowledge of the n...
ALGORITHMICA
2005
13 years 4 months ago
2005
A new priority queue structure, the queap, is introduced. The queap executes insertion in O(1) amortized time and extract-min in O(log(k+2)) amortized time if there are k items th...
ALGORITHMICA
2005
13 years 4 months ago
2005
A new form of optimality for comparison based static dictionaries is introduced. This type of optimality, keyindependent optimality, is motivated by applications that assign key v...
ALGORITHMICA
2005
13 years 4 months ago
2005
We present a new bit-parallel technique for approximate string matching. We build on two previous techniques. The first one, BPM [Myers, J. of the ACM, 1999], searches for a patte...
ALGORITHMICA
2005
13 years 4 months ago
2005
We consider history independent data structures as proposed for study by Naor and Teague [3]. In a history independent data structure, nothing can be learned from the memory repre...
ALGORITHMICA
2005
13 years 4 months ago
2005
We present polynomial upper and lower bounds on the number of iterations performed by the k-means method (a.k.a. Lloyd's method) for k-means clustering. Our upper bounds are ...