surprising that much of the algorithm research in this field comes either from the multimedia areas of informa- tion retrieval or from computational biology teams, and more specifically, from researchers who have been mainly (but not exclusively) motivated by the need to match DNA sequences with large sequences of characters that may be repeated and/or structured in specific patterns. The sheer size of the DNA molecule, which has parts where pattern matching is needed for many aspects of genetic sequencing as well as protein- and drug-related research, has been a critical factor inciting toward the improvement of the performance of such pattern search and match algorithms, if the researchers were ever to be able to get answers from experiments within a person's lifetime. Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright © 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website. CLASSIFICATION PROCESSORS NETWORK PROCESSORS A couple of helpful references on the subject of this type of algorithm from the two different approaches we just mentioned include Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeieo-Neto²4 and Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology by Dan Gusfield.²5 The latter is an especially good source of information from a tutorial standpoint. Some of the most significant algorithms currently used for layer 7 search and match classifiers such as the Boyer-Moore algorithm, its variation known as Horspool algorithm, the Sunday algorithm, the Knuth-Morris-Pratt (KMP) algorithm, the Aho-Corasick algorithm (an evolu- tion of the KMP approach), the Needleman-Winsch algorithm, the Smith-Waterman algorithm, and others are discussed and analyzed in tremendous detail in this source. Modern Information Retrieval offers quantified graphs with performance comparison curves for several of these algorithms. We will not elaborate on the int