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Space-efficient computation of parallel approximate string matching

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Abstract

Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventional solution makes it impractical to search queries from the huge sequences having billions of characters. Therefore, many studies have been proposed that improves on the space and time requirement of the basic solution which includes heuristic, filtration, and index-based solutions. These existing solutions obtain the better performance by compromising on the completeness of the search. In this paper, we proposed the linear space algorithm for the approximate string matching problem while retaining the time complexity of conventional solution. The proposed method works in linear space without omitting any regions in the given text; hence, it finds all the possible matches. Conventional dynamic programming solution is modified in such a way that storage of complete trace back table is avoided by keeping only running count of each edit operation in the memory. A variety of laws and facts are discovered in classical dynamic programming table in that regard. We also presented the parallel approach to the proffered algorithm to improve the running time of the algorithm. The algorithm is evaluated on the CUDA-enabled GPUs. DNA sequences of sizes between 250 and 970 MBP are used for evaluation. Moreover, experiments are also performed by using natural language text to highlight the broader applicability of the proposed algorithm. Results show the substantial superiority of the algorithm in terms of performance and scalability compared to the state-of-the-art algorithms.

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Data availability

All the source code can be found publicly at the following GitHub link: (https://github.com/sadiqumair/Space-efficient-Computation-of-Parallel-Approximate-String-Matching). The open source existing datasets used in this article can be found from the following links: (1) NCBI (ftp.ncbi.nlm.nih.gov/), (2) Smart (https://github.com/smart-tool/smart).

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Muhammad Umair Sadiq and Muhammad Murtaza Yousaf have equally contributed to this work.

Authors and Affiliations

  1. Department of Computer Science, Government College University, Lahore, 54000, Pakistan

    Muhammad Umair Sadiq

  2. Faculty of Computing and Information Technology, University of the Punjab, Lahore, 54000, Pakistan

    Muhammad Umair Sadiq

  3. Department of Software Engineering, Faculty of Computing and Information Technology, University of the Punjab, Lahore, 54000, Pakistan

    Muhammad Murtaza Yousaf

Authors
  1. Muhammad Umair Sadiq
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  2. Muhammad Murtaza Yousaf
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MUS and MMY have contributed equally to the manuscript.

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Correspondence to Muhammad Umair Sadiq.

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Sadiq, M.U., Yousaf, M.M. Space-efficient computation of parallel approximate string matching. J Supercomput 79, 9093–9126 (2023). https://doi.org/10.1007/s11227-022-05038-6

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