Toward optimal disk layout of genome scale suffix trees

Authors:
Vikas K. Garg
Affiliations:
IBM Research - India
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

Citing 6
Cited 0

Algorithms on strings, trees, and sequences: computer science and computational biology

Algorithms on strings, trees, and sequences: computer science and computational biology
Replacing suffix trees with enhanced suffix arrays

Journal of Discrete Algorithms - SPIRE 2002
Linear work suffix array construction

Journal of the ACM (JACM)
Better external memory suffix array construction

Journal of Experimental Algorithmics (JEA)
Optimal self-adjusting trees for dynamic string data in secondary storage

SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
Search-Optimized suffix-tree storage for biological applications

HiPC'05 Proceedings of the 12th international conference on High Performance Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Suffix trees provide for efficient indexing of numerous sequence processing problems in biological databases. We address the pivotal issue of improving the search efficiency of disk-resident suffix trees by improving the storage layout from a statistical learning viewpoint. In particular, we make the following contributions: we (a) introduce the Q-Optimal Disk Layout(Q-OptDL) problem in the context of suffix trees and prove it to be NP-Hard, and (b) propose an algorithm for improving the layout of suffix trees that is guaranteed to perform asymptotically no worse than twice the optimal disk layout.