On Computing Average Common Substring Over Run Length Encoded Sequences
| Autor: | Sharma V. Thankachan, Sahar Hooshmand, Neda Tavakoli, Paniz Abedin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Algebra and Number Theory Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology String searching algorithm Space (mathematics) Measure (mathematics) Substring Theoretical Computer Science Combinatorics Task (computing) 020901 industrial engineering & automation Computational Theory and Mathematics Encoding (memory) Computer Science - Data Structures and Algorithms 0202 electrical engineering electronic engineering information engineering Data Structures and Algorithms (cs.DS) 020201 artificial intelligence & image processing Twist Information Systems Mathematics |
| Zdroj: | Fundamenta Informaticae. 163:267-273 |
| ISSN: | 1875-8681 0169-2968 |
| DOI: | 10.3233/fi-2018-1743 |
| Popis: | The Average Common Substring (ACS) is a popular alignment-free distance measure for phylogeny reconstruction. The ACS can be computed in O(n) space and time, where n=x+y is the input size. The compressed string matching is the study of string matching problems with the following twist: the input data is in a compressed format and the underling task must be performed with little or no decompression. In this paper, we revisit the ACS problem under this paradigm where the input sequences are given in their run-length encoded format. We present an algorithm to compute ACS(X,Y) in O(Nlog N) time using O(N) space, where N is the total length of sequences after run-length encoding. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|