A unified framework for string similarity search with edit-distance constraint

Yu, Minghe; Wang, Jin; Li, Guoliang; Zhang, Yong; Deng, Dong; Feng, Jianhua

doi:10.1007/s00778-016-0449-y

Cited by 26 publications

(28 citation statements)

References 35 publications

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“…The reported results in this section are aggregated response times from 2000 queries. Since the HSTree technique consistently outperformed other existing techniques as reported in [39], [48], we compared our technique with the original HSTree technique [39], [48]. In the remaining sections, we denote our algorithm by OptSearch and the original HSTree search algorithm by HSSearch.…”

Section: Experiments a Experimental Settingsmentioning

confidence: 94%

“…Therefore, it is hard to apply the approach to the search problem, where a threshold can vary from query to query. To address the problem, HSTree [39], [48] has been proposed, which maintains alternative partitioning results of each data string.…”

Section: Hstreementioning

confidence: 99%

“…This is a challenging problem, because edit distance computation is costly and a scan-based approach that computes the edit distance for each string in the database would incur a prohibiting O(N • n 2 ) cost for a large database, where N is the number of strings in the database and n is the average length of a string. To address the performance challenge, there has been a rich literature on this problem [3], [4], [6], [7], [16], [18], [20]- [23], [29], [39], [41], [46], [48].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a hierarchical index structure, named HSTree, has been proposed to apply the partition signature scheme to string similarity search problem [39], [48]. The HSTree is a full binary tree.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Signature Selection for Efficient String Similarity Search

2020

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In this paper, we study the problem of string similarity search to retrieve in a database all strings similar to a query string within a given threshold. To measure the similarity between strings, we use edit distance. Many algorithms have been proposed under a filtering-and-verification framework to solve the problem. To reduce the overhead of edit distance verification, it is crucial to efficiently generate a small number of candidates in the filtering phase. Recently, an index structure named HSTree has been proposed for efficiently generating candidate strings. To generate candidates, they select and utilize HSTree nodes at a specific level calculated from a given threshold. In this paper, we observe that there are many alternative ways to select HSTree nodes, and propose a novel technique that selects HSTree nodes in an optimized way based on the observation. We also propose a modified HSTree, named a threaded HSTree, which connects inverted lists of an HSTree node to inverted lists of its child nodes. With a threaded HSTree, we can reduce the overhead of index lookups in HSTree nodes while selecting optimal tree nodes. Experimental results show that the proposed technique significantly outperforms the existing technique using the HSTree.

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Section: Experiments a Experimental Settingsmentioning

confidence: 94%

Section: Hstreementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized Signature Selection for Efficient String Similarity Search

2020

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“…Another group of similarity measures suitable for processing unequal-length time series is developed based on the concept of the edit distance for strings [23]. Compared with DTW which only considers the constrain bandwidth, the similarity measure based on the edit distance requires tuning more parameters [24][25][26] to find the most similar set of matching patterns. It is reported [15] that the data amount is huge for vibration responses detected by ultra-weak FBG of each monitoring area under the excitation of passing trains.…”

Section: Introductionmentioning

confidence: 99%

Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures

Zuo

et al. 2020

Sensors

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Quantifying structural status and locating structural anomalies are critical to tracking and safeguarding the safety of long-distance underground structures. Given the dynamic and distributed monitoring capabilities of an ultra-weak fiber Bragg grating (FBG) array, this paper proposes a method combining the stacked denoising autoencoder (SDAE) network and the improved dynamic time wrapping (DTW) algorithm to quantify the similarity of vibration responses. To obtain the dimensionality reduction features that were conducive to distance measurement, the silhouette coefficient was adopted to evaluate the training efficacy of the SDAE network under different hyperparameter settings. To measure the distance based on the improved DTW algorithm, the one nearest neighbor (1-NN) classifier was utilized to search the best constraint bandwidth. Moreover, the study proposed that the performance of different distance metrics used to quantify similarity can be evaluated through the 1-NN classifier. Based on two one-dimensional time-series datasets from the University of California, Riverside (UCR) archives, the detailed implementation process for similarity measure was illustrated. In terms of feature extraction and distance measure of UCR datasets, the proposed integrated approach of similarity measure showed improved performance over other existing algorithms. Finally, the field-vibration responses of the track bed in the subway detected by the ultra-weak FBG array were collected to determine the similarity characteristics of structural vibration among different monitoring zones. The quantitative results indicated that the proposed method can effectively quantify and distinguish the vibration similarity related to the physical location of structures.

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