Context-Sensitive Grammar Transform: Compression and Pattern Matching

MARUYAMA, Shirou; Tanaka, Youhei; Sakamoto, H.; Takeda, Masayuki

doi:10.1587/transinf.e93.d.219

Cited by 7 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This idea is an extension of BPE and can be applied even if an input text cannot be separated into words. One direct improvement of BPE is a method using a context-sensitive grammar by Maruyama et al [34], while for compression methods based on context-free grammar we have the methods by Klein and Shapira [25] and Uemura et al [51]. In both methods, a modified version of suffix tree [53] is used as a dictionary tree for constructing grammar.…”

Section: History Of Compressed Pattern Matching Researchmentioning

confidence: 99%

Compression and Pattern Matching

Kida

Furuya

2021

Sublinear Computation Paradigm

View full text Add to dashboard Cite

We introduce our research on compressed pattern matching technology that combines data compression and pattern matching. To show the results of this work, we explain the collage system proposed by Kida et al. in 2003 that is a unifying framework for compressed pattern matching, and we explain the Repair-VF method proposed by Yoshida and Kida in 2013 and the MR-Repair method proposed by Furuya et al. in 2019 as grammar compressions suitable for compressed pattern matching.

show abstract

Section: History Of Compressed Pattern Matching Researchmentioning

confidence: 99%

Compression and Pattern Matching

Kida

Furuya

2021

Sublinear Computation Paradigm

View full text Add to dashboard Cite

show abstract

“…Grammars have been utilized in manifold applications for inferring higher-level semantics, data compression, and pattern matching [12,13]. In this paper, we discuss the invocation of formal grammars to model the structural information of buildings.…”

Section: Indoor Grammarmentioning

confidence: 99%

GraMap: QoS-Aware Indoor Mapping Through Crowd-Sensing Point Clouds with Grammar Support

Abdelaal¹,

Duerr²,

Rothermel³

et al. 2018

Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

View full text Add to dashboard Cite

Recently, several approaches have been proposed to automatically model indoor environments. Most of such efforts principally rely on the crowd to sense data such as motion traces, images, and WiFi footprints. However, large datasets are usually required to derive precise indoor models which can negatively affect the energy efficiency of the mobile devices participating in the crowd-sensing system. Furthermore, the aforementioned data types are hardly suitable for deriving 3D indoor models. To overcome these challenges, we propose GraMap, a QoS-aware automatic indoor modeling approach through crowd-sensing 3D point clouds. GraMap exploits a recently-developed sensors fusion mechanism, namely Tango technology, to cooperatively collect point clouds from the crowd. Afterward, a set of backend servers extracts the required geometrical information to derive indoor models.For the sake of improving the energy efficiency of the mobile devices, GraMap performs data quality assurance along with 3D data compression. Specifically, we propose a probabilistic quality model-implemented on the mobile devices-to ensure high-quality of the captured point clouds. In this manner, we conserve energy via sidestepping the repetition of sensing queries due to uploading low-quality point clouds. Nevertheless, the resultant indoor models may still suffer from incompleteness and inaccuracies. Therefore, GraMap leverages formal grammars which encode design-time knowledge, i.e. structural information about the building, to enhance the quality of the derived models. To demonstrate the effectiveness of GraMap, we implemented a crowd-sensing Android App to collect point clouds from volunteers. We show that GraMap derives highly-accurate models while reducing the energy costs of pre-processing and reporting the point clouds. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile devices;

show abstract

“…Grammar-based compression using a weak form of context-sensitive grammars (so-called †-sensitive grammars) is discussed in [101].…”

Section: Grammar-based Compressionmentioning

confidence: 99%

Algorithmics on SLP-compressed strings: A survey

Lohrey

2012

Groups - Complexity - Cryptology

125

105

View full text Add to dashboard Cite

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.

show abstract

Context-Sensitive Grammar Transform: Compression and Pattern Matching

Cited by 7 publications

References 20 publications

Compression and Pattern Matching

Compression and Pattern Matching

GraMap: QoS-Aware Indoor Mapping Through Crowd-Sensing Point Clouds with Grammar Support

Algorithmics on SLP-compressed strings: A survey

Contact Info

Product

Resources

About