A New Approach to String Pattern Mining with Approximate Match

Matsui, Tetsushi; Uno, Takeaki; Umemori, Juzoh; Koide, Tsuyoshi

doi:10.1007/978-3-642-40897-7_8

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collecting all the substrings s of length l ∈ [l p − K, l p + K] from the text t that verify Eq. 2 at each position i, may lead to multiple hits of the same occurrence of p [67]. Due to the online setting considered, it is not possible to decide whether an occurrence can be added to S at the time it is found, but it depends on the symbols of t that are not yet processed.…”

Section: An Online Search Algorithm Based On Approximate String Matchingmentioning

confidence: 99%

A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation

2019

View full text Add to dashboard Cite

Accelerating inexact string matching procedures is of utmost importance when dealing with practical applications where huge amount of data must be processed in real time, as usual in bioinformatics or cybersecurity. Inexact matching procedures can yield multiple shadow hits, which must be filtered, according to some criterion, to obtain a concise and meaningful list of occurrences. The filtering procedures are often computationally demanding and are performed offline in a post-processing phase. This paper introduces a novel algorithm for Online Approximate String Matching (OASM) able to filter shadow hits on the fly, according to general purpose priority rules that greedily assign priorities to overlapping hits. An FPGA hardware implementation of OASM is proposed and compared with a serial software version. Even when implemented on entry level FPGAs, the proposed procedure can reach a high degree of parallelism and superior performance in time compared to the software implementation, while keeping low the usage of logic elements. This makes the developed architecture very competitive in terms of both performance and cost of the overall computing system.

show abstract

Section: An Online Search Algorithm Based On Approximate String Matchingmentioning

confidence: 99%

A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation

2019

View full text Add to dashboard Cite

show abstract

“…Consequently, sequential pattern mining [3] has become a significant part of pattern mining studies. Many sequential pattern mining algorithms have been proposed to solve various issues, such as general sequential pattern mining [12], [18], string mining [9], [16], closed sequential pattern mining [20,21], constraint-based sequential pattern mining [11], [19], etc. A pattern is said closed if there is no super-pattern with the same support.…”

Section: Introductionmentioning

confidence: 99%

CCSpan: Mining closed contiguous sequential patterns

Zhang

Wang

Yang

2015

Knowledge-Based Systems

View full text Add to dashboard Cite

Mining closed contiguous sequential patterns has been addressed in the literature only recently, through the CCSpan algorithm. CCSpan mines a set of patterns that contains the same information than traditional sets of closed sequential patterns, while being more compact due to the contiguity. Although CCSpan outperforms closed sequential pattern mining algorithms in the general case, it does not scale well on large datasets with long sequences. Moreover, in the context of noisy datasets, the contiguity constraint prevents from mining a relevant result set. Inspired by BIDE, that has proven to be one of the most efficient closed sequential pattern mining algorithm, we propose CCPM that mines closed contiguous sequential patterns, while being scalable. Furthermore, CCPM introduces usable wildcards that address the problem of mining noisy data. Experiments show that CCPM greatly outperforms CCSpan, especially on large datasets with long sequences. In addition, they show that the wildcards allows to efficiently tackle the problem of noisy data.

show abstract