Efficient repeating pattern finding in music databases
Proceedings of the seventh international conference on Information and knowledge management
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
On the sorting-complexity of suffix tree construction
Journal of the ACM (JACM)
Database indexing for large DNA and protein sequence collections
The VLDB Journal — The International Journal on Very Large Data Bases
A Database Index to Large Biological Sequences
Proceedings of the 27th International Conference on Very Large Data Bases
Linear-Time Longest-Common-Prefix Computation in Suffix Arrays and Its Applications
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Approximate String-Matching over Suffix Trees
CPM '93 Proceedings of the 4th Annual Symposium on Combinatorial Pattern Matching
Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory
IEEE Transactions on Knowledge and Data Engineering
Practical methods for constructing suffix trees
The VLDB Journal — The International Journal on Very Large Data Bases
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Finding maximum-length repeating patterns in music databases
Multimedia Tools and Applications
Genome-scale disk-based suffix tree indexing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Practical suffix tree construction
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient and scalable indexing techniques for biological sequence data
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
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Recently, active prevention healthcares are needed for potential patients to be suffered in the future as the forecasted diseases inherited from ancestors. We call active U-healthcare, for providing active, periodic, and continuous medical treatments depending on inherited heterogeneous states in DNAs of patients, such as diabetes, heart diseases, and female diseases. However, the bottleneck of the aggressive active U-healthcare is memory overhead in DNA sequence analysis of each patient since the sequences of DNAs have massive volume. Thus, the efficient retrieve of the many disease patterns in originally recorded on DNAs of potential patients is a major problem. This paper focuses on a novel method for efficient retrieving of disease patterns using a suffix tree in memory. The suffix tree is widely used in the similarity search for sequences consisting of limited characters. It is efficient when the occurrence frequency of a common prefix is high. Since in-memory suffix tree construction algorithms do not scale up, a large-scale disk-based suffix tree construction algorithm, TRELLIS, has been proposed recently. However, the algorithm requires a large amount of memory, disk space, and disk I/Os in order to merge sub-trees having a common prefix. In this paper, we propose a new non-merging method, called NST. The experimental results show that NST constructs an index using less memory than TRELLIS.