• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.223-235
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• 2011

Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.595-602
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• 2015

One of the challenges of the digital forensics is how to handle certain amounts of data efficiently. Although reliable and various approximate matching algorithms have been presented to quickly identify similarities between digital objects, its practical effectiveness to identify the semantic similarity is low because of frequent false positives. To solve this problem, we suggest adding a pre-processing of the approximate matching target dataset to increase matching accuracy while maintaining the reliability of the approximate matching algorithm. To verify the effectiveness, we experimented with two datasets of eml and hwp using sdhash in order to identify the semantic similarity.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.33-43
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• 2022

Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

• Journal of KIISE
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• v.43 no.10
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• pp.1073-1078
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• 2016

(${\delta}$, ${\gamma}$)-matching for strings over integer alphabets can be applied to such fields as musical melody and share prices on stock markets. In this paper, we define ${\delta}$-approximate periods and ${\gamma}$-approximate periods of strings over integer alphabets. We also present two $O(n^2)$-time algorithms, each of which finds minimum ${\delta}$-approximate periods and minimum ${\gamma}$-approximate periods, respectively. Then, we provide the experimental results of execution times of both algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.955-961
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• 2017

In this paper, we propose a parallel approximate string matching algorithm with k-mismatches for multiple fixed-length patterns (PMASM) in DNA sequences. PMASM is developed from parallel single pattern approximate string matching algorithms to effectively calculate the Hamming distances for multiple patterns with a fixed-length. In the preprocessing phase of PMASM, all target patterns are binary encoded and stored into a look-up memory. With each input character from the input string, the Hamming distances between a substring and all patterns can be updated at the same time based on the binary encoding information in the look-up memory. Moreover, PMASM adopts graphics processing units (GPUs) to process the data computations in parallel. This paper presents three kinds of PMASM implementation methods in GPUs: thread PMASM, block-thread PMASM, and shared-mem PMASM methods. The shared-mem PMASM method gives an example to effectively make use of the GPU parallel capacity. Moreover, it also exploits special features of the CUDA (Compute Unified Device Architecture) memory structure to optimize the performance. In the experiments with DNA sequences, the proposed PMASM on GPU is 385, 77, and 64 times faster than the traditional naive algorithm, the shift-add algorithm and the single thread PMASM implementation on CPU. With the same NVIDIA GPU model, the performance of the proposed approach is enhanced up to 44% and 21%, compared with the naive, and the shift-add algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.284-287
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• 1998

It is important to develop a method of assembling a set of sub pictures automatically into a mosaic picture , because a view through fiberscopes or microscopes with higher magnifying power is much larger than the field of view taken by a camera. This paper presents a method of assembling sub pictures, where roughly estimated junctions called approximate junctions are employed for matching triangles formed by selected junctions in sub pictures. To over come the difficulties in processing speed and noise corruption, fuzzy rules is applied to get fuzzy values for existence of approximate junctions and fuzzy similarity for congruent triangle matching. Some demonstration, exemplified by assembling microscopic metal matrix photographs, are given to show feasibility of this method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1986-1992
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• 2009

A user often searches a data by inputting a variant such as the abbreviation or substring of a word, or a misspelled word. The simple approach to the searching for variants is to build a variants dictionary. However, it entails enormous cost and time and can not handle variants by misspelling. Approximate searching, searching by approximate string matching, is a good approach to the searching. A problem in the approach is that it cannot handle variants by abbreviations. This paper propose a method for searching various variants including abbreviations and misspelled words, by using the trie indexing. First, this paper shows a variant matching method with the calculation of path weighted-metric. In addition, it provides variant searching algorithm to reduce the search time.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.171-176
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• 2007

Parsing 작업의 결과인 Parse Tree 정보는 문장에 관한 구조적 정보를 가지고 있는 Tree 정보로 이 정보를 이용하여 정보 검색에 활용하는 알고리즘을 제안한다. 제안하는 알고리즘은 XML 기술과 스트링 매칭 기법을 이용하였으며, 사용한 스트링 매칭 기법은 Approximate String Matching 기법이다. Query 정보와 문서 정보를 Parsing하여 얻은 Parse Tree를 XML 형태의 정보로 변환한 후, 두 정보를 가지고 Approximate String Matching 기법을 적용하여 Query 정보와 문서 정보 간의 유사도를 계산한다. 제안하는 알고리즘의 장점은 구조 기반의 정보 검색 기능이 가능하고 비슷한 정보에 대한 검색 기능이 가능하며 비슷한 구조에 대한 검색 기능이 가능하다는 것이다.