Efficient duality-based subsequent matching on time-series data in green computing

Ihm, Sun-Young; Nasridinov, Aziz; Lee, Jeong-Hoon

doi:10.1007/s11227-013-1028-2

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…It divides a subsequence of a data sequence into a window, and if the length of the window does not match the size of the subsequence, it cannot be configured as a window; hence, a remainder data value is generated. E-dual matching [11] configures data that cannot be compared because it cannot be configured as a window during query processing as a virtual window. Furthermore, it cannot be utilized to determine similar sequences.…”

Section: Sequence Matching Methodsmentioning

confidence: 99%

Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search

2022

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In this paper, we propose the duality-based image sequence matching method, which is called Dual-ISM, a subsequence matching method for searching for similar images. We first extract feature points from the given image data and configure the feature vectors as one data sequence. Next, the feature vectors are configured in the form of a disjoint window, and a low-dimensional transformation is carried out. Subsequently, the query image that is entered to construct the candidate set is similarly subjected to a low-dimensional transformation, and the low-dimensional transformed window of the data sequence and window that are less than the allowable value, ε, is regarded as the candidate set using a distance calculation. Finally, similar images are searched in the candidate set using the distance calculation that are based on the original feature vector.

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Section: Sequence Matching Methodsmentioning

confidence: 99%

Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search

2022

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“…Once the music sequence is demoted with binary sequence, we can consider it as time sequence and find the subsequence matching to the genre. Given two time sequence S and Q which may have different length, the goal is to find all similar subsequence pairs between two time sequences with a specified threshold [31]. A k-windowSubsequenceMatching algorithm, described in [31], is used in order to find subsequence matching in music sequence.…”

Section: Genre Matchingmentioning

confidence: 99%

A Study on Music Genre Recognition and Classification Techniques

Nasridinov¹

2014

IJMUE

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Automatic classification of music genre is widely studied topic in music information retrieval (MIR) as it is an efficient method to structure and organize the large numbers of music files available on the Internet. Generally, the genre classification process of music has two main steps: feature extraction and classification. The first step obtains audio signal information, while the second one classifies the music into various genres according to extracted features. In this paper, we present a study on techniques for automatic music genre recognition and classification. We first describe machine learning based chord recognition methods, such as hidden Markov models, neural networks, dynamic Bayesian network and rule-based methods, and template matching methods. We then explain supervised, unsupervised and semi-supervised classification methods classifying music genres. Finally, we briefly describe the proposed method for automatic classification of music genres, which consists of three steps: chord labeling, genre matching and classification.

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“…Next paper entitled "Efficient Duality-Based Subsequent Matching on Time-Series Data in Green Computing" by Park et al [3] presents a subsequence matching method for green computing, which is called the efficient duality-based subsequence matching (simply, E-Dual Match). E-Dual Match handles all possible query windows for determining candidates.…”

Section: Related Workmentioning

confidence: 99%

Computational awareness towards green environments

et al. 2014

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This special issue calls for high quality and state-of-the-art research issues and results concerning the development of green environment from the perspective of computing systems. In particular, the special issue is going to showcase the most recent achievements and developments in the realm of awareness computing. Original and research articles are solicited in all aspects of including theoretical studies, practical applications, new communication technology and experimental prototypes. This special issue includes original papers describing the latest developments, trends, and solutions of computational awareness towards green environments including green IT and cloud cluster computing, energy-efficient systems, power-aware software, awareness mechanism and modeling, context, emotion and preference awareness, energy harvesting, sensor, Ad Hoc, P2P networks, and communications, safety or security.

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Efficient duality-based subsequent matching on time-series data in green computing

Cited by 8 publications

References 17 publications

Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search

Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search

A Study on Music Genre Recognition and Classification Techniques

Computational awareness towards green environments

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