Hsing-Yen Ann

A unique-ID-based matrix strategy for efficient iconic indexing of symbolic pictures

Abstract In this paper, we propose an efficient iconic indexing strategy called unique-ID-based matrix (UID matrix) for symbolic pictures, in which each spatial relationship between any two objects is assigned with a unique identifier (ID) and is recorded in a matrix. Basically, the proposed strategy can represent those complex relationships that are represented in 2D C-strings in a matrix, and an efficient range checking operation can be used to support pictorial query, spatial reasoning and similarity retrieval; therefore, they are efficient enough as compared to the previous approaches. From our simulation, we show that the proposed UID matrix strategy requires shorter time to convert the input data into the corresponding representation than the 2D C-string strategy, so is the case with query processing. Moreover, our proposed UID matrix strategy may require lesser storage cost than the 2D C-string strategy in some cases.

Efficient algorithms for the longest common subsequence problem with sequential substring constraints

In this paper, we generalize the inclusion constrained longest common subsequence (CLCS) problem to the hybrid CLCS problem which is the combination of the sequence inclusion CLCS and the string inclusion CLCS, called the sequential sub string constrained longest common subsequence (SSCLCS) problem. In the SSCLCS problem, we are given two strings A and B of lengths m and n, respectively, formed by alphabet \Sigma and a constraint sequence C formed by ordered strings (C^1, C^2, C^3, C^l) with total length r. We are to find the longest common subsequence D of A and B containing C^1,C^2,C^3,C^l as sub strings and the order of C¡¦s are retained. This problem have two variants that the strings in C may or may not overlap. We proposed algorithms with O(mnl + (m+ n)(|\Sigma| + r)) and O(mnr + (m+ n)|\Sigma| ) time for the two variants of the problem. For the special case with one or two constraints, our algorithms runs in O(mn+(m+n)(|\Sigma| +r)) and O(mnr +(m+n)j|\Sigma|) time, which are an order faster than the algorithm proposed by Chen and Chao [1].

Efficient algorithms for finding interleaving relationship between sequences

The longest common subsequence and sequence alignment problems have been studied extensively and they can be regarded as the relationship measurement between sequences. However, most of them treat sequences evenly or consider only two sequences. Recently, with the rise of whole-genome duplication research, the doubly conserved synteny relationship among three sequences should be considered. It is a brand new model to find a merging way for understanding the interleaving relationship of sequences. Here, we define the merged LCS problem for measuring the interleaving relationship among three sequences. An O(n^3) algorithm is first proposed for solving the problem, where n is the sequence length. We further discuss the variant version of this problem with the block information. For the blocked merged LCS problem, we propose an algorithm with time complexity O(n^2m), where m is the number of blocks. An improved O(n^2+nm^2) algorithm is further proposed for the same blocked problem.

Dynamic programming algorithms for the mosaic longest common subsequence problem

The longest common subsequence (LCS) problem can be used to measure the relationship between sequences. In general, the inputs of the LCS problem are two sequences. For finding the relationship between one sequence and a set of sequences, we cannot apply the traditional LCS algorithms immediately. In this paper, we define the mosaic LCS (MLCS) problem of finding a mosaic sequence C, composed of repeatable k sequences in source sequence set S, such that the LCS of C and the target sequence T is maximal. Based on the concept of break points in sequence T, we first propose a divide-and-conquer algorithm with O(n^2m|S|+n^3logk) time for solving this problem, where n and m are the length of T and the maximal length of sequences in S, respectively. Furthermore, an improved algorithm with O(n(m+k)|S|) time is proposed by applying an efficient preprocessing for the MLCS problem.

A note on adaptive 2D-H strings

A picture is defined to be ambiguous if there exists more than one diAerent reconstructed picture from its representation. In this paper, we first give an ambiguous case based on the adaptive 2D-H string representation (Chang and Lin, 1996). We next show how to avoid the ambiguous cases. ” 1999 Elsevier Science B.V. All rights reserved.

Efficient algorithms for the block edit problems

In this paper, we focus on the edit distance between two given strings where block-edit operations are allowed and better fitting to the human natural edit behaviors. Previous results showed that this problem is NP-hard when block moves are allowed. Various approximations to this problem have been proposed in recent years. However, this problem can be solved by the polynomial-time optimization algorithms if some reasonable restrictions are applied. The restricted variations which we consider involve character insertions, character deletions, block copies and block deletions. In this paper, three problems are defined with different measuring functions, which are P(EIS,C), P(EI,L) and P(EI,N). Then we show that with some preprocessing, the minimum block edit distances of these three problems can be obtained by dynamic programming in O(nm), O(nmlogm) and O(nm^2) time, respectively, where n and m are the lengths of the two input strings.

Efficient polynomial-time algorithms for the constrained LCS problem with strings exclusion

In this paper, we revisit a recent variant of the longest common subsequence (LCS) problem, the string-excluding constrained LCS (STR-EC-LCS) problem, which was first addressed by Chen and Chao (J Comb Optim 21(3):383–392, 2011). Given two sequences

Minimum Height and Sequence Constrained Longest Increasing Subsequence

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