ACM Transactions on Database Systems (TODS)
Data compression: methods and theory
Data compression: methods and theory
Theory and algorithms for plan merging
Artificial Intelligence
On the Approximation of Shortest Common Supersequencesand Longest Common Subsequences
SIAM Journal on Computing
An approximation algorithm for the shortest common supersequence problem: an experimental analysis
Proceedings of the 2001 ACM symposium on Applied computing
Introduction to Algorithms
Efficient Sharing of Encrypted Data
ACISP '02 Proceedings of the 7th Australian Conference on Information Security and Privacy
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"Sequence set" is a mathematical model used in many applications such as biological sequences analysis and text processing. However, "single" sequence set model is not appropriate for the rapidly increasing problem size. For example, very large genome sequences should be separated and processed chunk by chunk. For these applications, the underlying mathematical model is "Multiple Sequence Sets" (MSS). To process multiple sequence sets, sequences are distributed to different sets and then sequences on each set are processed in parallel. Deriving effective algorithm for MSS processing is challenging.In this paper, we have first defined the cost functions for the problem of Process of Multiple Sequence Sets (PMSS). The PMSS problem is then formulated as to minimize the total cost of process. Based on the analysis of the features of multiple sequence sets, we have proposed the Distribution and Deposition (DDA) algorithm and DDA* algorithm for PMSS problem. In DDA algorithm, the sequences are first distributed to multiple sets according to their alphabet contents; then sequences in each set are processed by deposition algorithm. The DDA* algorithm differs from the DDA algorithm in that the DDA* algorithm distributes sequences by clustering based on a set of sequence features. Experiments showed that the results of DDA and DDA* are always smaller than other algorithms, and DDA* outperformed DDA in most instances. The DDA and DDA* algorithms were also efficient both in time and space.