research-article

A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm

Authors:

Yang ChengAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1725 - 1734

https://doi.org/10.1145/2939672.2939842

Published: 13 August 2016 Publication History

Abstract

Information in various applications is often expressed as character sequences over a finite alphabet (e.g., DNA or protein sequences). In Big Data era, the lengths and sizes of these sequences are growing explosively, leading to grand challenges for the classical NP-hard problem, namely searching for the Multiple Longest Common Subsequences (MLCS) from multiple sequences. In this paper, we first unveil the fact that the state-of-the-art MLCS algorithms are unable to be applied to long and large-scale sequences alignments. To overcome their defects and tackle the longer and large-scale or even big sequences alignments, based on the proposed novel problem-solving model and various strategies, e.g., parallel topological sorting, optimal calculating, reuse of intermediate results, subsection calculation and serialization, etc., we present a novel parallel MLCS algorithm. Exhaustive experiments on the datasets of both synthetic and real-world biological sequences demonstrate that both the time and space of the proposed algorithm are only linear in the number of dominants from aligned sequences, and the proposed algorithm significantly outperforms the state-of-the-art MLCS algorithms, being applicable to longer and large-scale sequences alignments.

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Cited By

Li YLiu BDuan TWang ZLi HCui J(2025)A Novel Key Point Based MLCS Algorithm for Big Sequences MiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348523437:1(15-28)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3485234
Jiang BWang CFu YLiu HGuo PWang Y(2023)A Space-Saving Based MLCS Algorithm2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361492(0589-0594)Online publication date: 14-Nov-2023
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361492
Khan RZakarya MKhan ARahman IAbd Rahman MAbdul Karim MMustafa M(2020)A Heuristic Approach for Finding Similarity Indexes of Multivariate Data SetsIEEE Access10.1109/ACCESS.2020.29682228(21759-21769)Online publication date: 2020
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A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm

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cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 ACM.

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Published: 13 August 2016

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National Natural Science Foundation of China

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KDD '16

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Li YLiu BDuan TWang ZLi HCui J(2025)A Novel Key Point Based MLCS Algorithm for Big Sequences MiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348523437:1(15-28)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3485234
Jiang BWang CFu YLiu HGuo PWang Y(2023)A Space-Saving Based MLCS Algorithm2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361492(0589-0594)Online publication date: 14-Nov-2023
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361492
Khan RZakarya MKhan ARahman IAbd Rahman MAbdul Karim MMustafa M(2020)A Heuristic Approach for Finding Similarity Indexes of Multivariate Data SetsIEEE Access10.1109/ACCESS.2020.29682228(21759-21769)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2968222
Khan RAli IAltowaijri SZakarya MUr Rahman AAhmedy IKhan AGani A(2019)LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN DataSensors10.3390/s1901016619:1(166)Online publication date: 4-Jan-2019
https://doi.org/10.3390/s19010166
Du MLi F(2019)Spell: Online Streaming Parsing of Large Unstructured System LogsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287544231:11(2213-2227)Online publication date: 1-Nov-2019
https://doi.org/10.1109/TKDE.2018.2875442
Islam MSaifullah CAsha ZAhamed R(2019)Chemical reaction optimization for solving longest common subsequence problem for multiple stringSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3200-323:14(5485-5509)Online publication date: 1-Jul-2019
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Xu JZhang HZhou WHe RLi T(2018)Signature based trouble ticket classificationFuture Generation Computer Systems10.1016/j.future.2017.07.05478:P1(41-58)Online publication date: 1-Jan-2018
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Aslanyan L(2017)LCS algorithm with vector-markers2017 Computer Science and Information Technologies (CSIT)10.1109/CSITechnol.2017.8312148(92-96)Online publication date: Sep-2017
https://doi.org/10.1109/CSITechnol.2017.8312148
Du MLi F(2016)Spell: Streaming Parsing of System Event Logs2016 IEEE 16th International Conference on Data Mining (ICDM)10.1109/ICDM.2016.0103(859-864)Online publication date: Dec-2016
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