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

Abstract

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.

Business Process Automation: A Workflow Incorporating Optical Character Recognition and Approximate String and Pattern Matching for Solving Practical Industry Problems

“…One of the algorithms used in ASM is measuring the Levenshtein distance between strings. Two strings can be said to match if their dissimilarity (or Levenshtein distance) is below a predetermined threshold [10]. The Levenshtein distance between two strings is the minimum number of character-level edits required to change one word into the other.…”

“…Regular expressions were chosen as a means to help identify candidate values for a field as the values for each key share a common structure but cannot be expressed in a finite dictionary. Key-value matching becomes more complicated where there are multiple string values in the OCR output file that conform to a specific regular expression pattern [10]. The solution to this was to use the field name position on the document and to match the first successful pattern that was located closest to the field name.…”

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations

“…In order to deal with such domains, five mainstream approaches can be pursued [ 10 ]: Feature generation and/or feature engineering, where numerical features are extracted ad-hoc from structured patterns (e.g., using their properties or via measurements) and can be further merged according to different strategies (e.g., in a multi-modal way [ 11 ]); Ad-hoc dissimilarities in the input space, where custom dissimilarity measures are designed in order to process structured patterns directly in the input domain without moving towards Euclidean (or metric) spaces. Common—possibly parametric—edit distances include the Levenshtein distance [ 12 ] for sequence domains and graph edit distances [ 13 ] for graphs domains; Embedding via information granulation and granular computing [ 3 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]; Dissimilarity representations [ 26 , 27 , 28 ], where structured patterns are embedded in the Euclidean space according to their pairwise dissimilarities; Kernel methods, where the mapping between the original input space and the Euclidean space exploits positive-definite kernel functions [ 29 , 30 , 31 , 32 , 33 ]. …”

On-line Elastic Similarity Measures for time series

“…In these approaches the choice of the similarity measure is essential for their performance. Hence, in recent years a diverse collection of similarity measures has been proposed in the literature to further enrich the existing portfolio (Parziale et al, 2018;Cinti et al, 2017;Oregi et al, 2017a;Zhou and De la Torre, 2016;Zhao and Itti, 2018).…”