Text algorithms
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Succinct indexable dictionaries with applications to encoding k-ary trees and multisets
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Compressed indexes for dynamic text collections
ACM Transactions on Algorithms (TALG)
Theoretical Computer Science
An extension of the Burrows–Wheeler Transform
Theoretical Computer Science
Compressed Index for Dictionary Matching
DCC '08 Proceedings of the Data Compression Conference
Breaking a Time-and-Space Barrier in Constructing Full-Text Indices
SIAM Journal on Computing
WALCOM'08 Proceedings of the 2nd international conference on Algorithms and computation
Succinct dictionary matching with no slowdown
CPM'10 Proceedings of the 21st annual conference on Combinatorial pattern matching
Faster compressed dictionary matching
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Succinct indexes for circular patterns
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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Given a set ${\cal P}$ of d patterns, the circular dictionary matching problem is to index ${\cal P}$ such that for any online query text T, we can quickly locate the occurrences of any cyclic shift of any pattern of ${\cal P}$ within T efficiently. This problem can be applied on practical problems that arise in bioinformatics and computational geometry. Recently, Hon et al. (2011) applied a variant of the well-known Burrows-Wheeler transform, called circular Burrows-Wheeler transform (circular BWT) [Mantaci, Restivo, Rosone, and Sciortino, Theoretical Computer Science, 2007], and showed that this can be used to solve the circular dictionary matching problem efficiently. In this paper, we give the first construction algorithm for the circular BWT, which takes O(nlogn) time and requires O(nlogσ) bits working space, where n denotes the total length of the patterns in ${\cal P}$ and σ is the alphabet size.