Noise Sensitivity of an Information Granules Filtering Procedure by Genetic Optimization for Inexact Sequential Pattern Mining

Maiorino, Enrico; Possemato, Francesca; Modugno, Valerio; Rizzi, Antonello

doi:10.1007/978-3-319-26393-9_9

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…The main application fields are message exchanges, wireless communications, audio, image and video processing [8,9]. ASM is extensively used for information retrieval in large text collections where, due to the large amount of data and the high variety of content, classical string matching procedures are usually not suitable [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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