Faster Subsequence and Don’t-Care Pattern Matching on Compressed Texts

Yamamoto, Tomokazu; Bannai, Hideo; Inenaga, Shunsuke; Takeda, Masayuki

doi:10.1007/978-3-642-21458-5_27

Cited by 21 publications

(11 citation statements)

References 16 publications

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“…Let us call this problem the semi-compressed subsequence matching problem. There is a straightforward algorithm for semi-compressed subsequence matching that works in time O.jpj jT j/, see [132,142]. Assume that the SLP T is in Chomsky normal form.…”

Section: Inputmentioning

confidence: 99%

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Algorithmics on SLP-compressed strings: A survey

Lohrey

2012

Groups - Complexity - Cryptology

124

105

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Results on algorithmic problems on strings that are given in a compressed form via straight-line programs are surveyed. A straight-line program is a context-free grammar that generates exactly one string. In this way, exponential compression rates can be achieved. Among others, we study pattern matching for compressed strings, membership problems for compressed strings in various kinds of formal languages, and the problem of querying compressed strings. Applications in combinatorial group theory and computational topology and to the solution of word equations are discussed as well. Finally, extensions to compressed trees and pictures are considered.

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Section: Inputmentioning

confidence: 99%

“…The values`.i; X / satisfy a simple recursion, which allows to compute all these values in time O.jpj jT j/. More complex problems related to semi-compressed subsequence matching (e.g., counting the number of shortest factors of eval.T /, which contain p as a subsequence) are considered in [22,133,142].…”

Section: Inputmentioning

confidence: 99%

Algorithmics on SLP-compressed strings: A survey

Lohrey

2012

Groups - Complexity - Cryptology

124

105

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“…Compression Type # of parameters Interpretability Optimization PCA-SL [14,9] Orthogonal rotation Lossy 2 Limited Stable bMH-SL [21] Hashing Lossy 3 Unable Stable SGD [33,8] Sampling -1 Limited Unstable cPLS (this study) Grammar compression Lossless 1 High Stable covered from grammar-compressed data) that also has a wide variety of applications in string processing, such as pattern matching [37], edit-distance computation [13], and q-gram mining [1]. Grammar compression builds a small context-free grammar that generates only the input data and is very effective at compressing sequences that contain many repeats.…”

Section: Approachmentioning

confidence: 99%

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Tabei

Saigo

Yamanishi

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammarcompressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational efficiency, and interpretability.

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“…Representative methods are RePair [12] and LCA [17]. Methods for type (ii) have also been presented for processing repetitive texts, e.g., pattern matching [18], pattern mining [9] and edit distance computation [10]. Table 1.…”

Section: Introductionmentioning

confidence: 99%

Improved ESP-index: A Practical Self-index for Highly Repetitive Texts

Takabatake

Tabei

Sakamoto

2014

Lecture Notes in Computer Science

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Abstract. While several self-indexes for highly repetitive texts exist, developing a practical self-index applicable to real world repetitive texts remains a challenge. ESP-index is a grammar-based self-index on the notion of edit-sensitive parsing (ESP), an efficient parsing algorithm that guarantees upper bounds of parsing discrepancies between different appearances of the same subtexts in a text. Although ESP-index performs efficient top-down searches of query texts, it has a serious issue on binary searches for finding appearances of variables for a query text, which resulted in slowing down the query searches. We present an improved ESP-index (ESP-index-I) by leveraging the idea behind succinct data structures for large alphabets. While ESP-index-I keeps the same types of efficiencies as ESP-index about the top-down searches, it avoid the binary searches using fast rank/select operations. We experimentally test ESP-index-I on the ability to search query texts and extract subtexts from real world repetitive texts on a large-scale, and we show that ESPindex-I performs better that other possible approaches.

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Faster Subsequence and Don’t-Care Pattern Matching on Compressed Texts

Cited by 21 publications

References 16 publications

Algorithmics on SLP-compressed strings: A survey

Algorithmics on SLP-compressed strings: A survey

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Improved ESP-index: A Practical Self-index for Highly Repetitive Texts

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