Yanni Li;Bing Liu;Tihua Duan;Zhi Wang;Hui Li;Jiangtao Cui
{"title":"基于关键点的新型大序列挖掘 MLCS 算法","authors":"Yanni Li;Bing Liu;Tihua Duan;Zhi Wang;Hui Li;Jiangtao Cui","doi":"10.1109/TKDE.2024.3485234","DOIUrl":null,"url":null,"abstract":"Mining multiple longest common subsequences (\n<i>MLCS</i>\n) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length \n<inline-formula><tex-math>$\\geqslant$</tex-math></inline-formula>\n 10,000), referred to as big sequences. Despite efforts in improving the time and space complexities of \n<i>MLCS</i>\n mining algorithms, both existing exact and approximate algorithms face challenges in handling big sequences due to the overwhelming size of their problem-solving graph model \n<i>MLCS-DAG</i>\n (\n<u>D</u>\nirected \n<u>A</u>\ncyclic \n<u>G</u>\nraph), leading to the issue of memory explosion or extremely high time complexity. To bridge the gap, this paper first proposes a new identification and deletion strategy for different classes of non-critical points in the mining of \n<i>MLCS</i>\n, which are the points that do not contribute to their \n<i>MLCS</i>\ns mining in the \n<i>MLCS-DAG</i>\n. It then proposes a new \n<i>MLCS</i>\n problem-solving graph model, namely \n<inline-formula><tex-math>$DAG_{KP}$</tex-math></inline-formula>\n (a new \n<i>MLCS-<u>DAG</u></i>\n containing only \n<u>K</u>\ney \n<u>P</u>\noints). A novel parallel \n<i>MLCS</i>\n algorithm, called \n<i>KP-MLCS</i>\n (\n<u>K</u>\ney \n<u>P</u>\noint based \n<i><u>MLCS</u></i>\n), is also presented, which can mine and compress all \n<i>MLCS</i>\ns of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm \n<i>KP-MLCS</i>\n drastically outperforms the existing state-of-the-art \n<i>MLCS</i>\n algorithms in terms of both efficiency and effectiveness.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"15-28"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Key Point Based MLCS Algorithm for Big Sequences Mining\",\"authors\":\"Yanni Li;Bing Liu;Tihua Duan;Zhi Wang;Hui Li;Jiangtao Cui\",\"doi\":\"10.1109/TKDE.2024.3485234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mining multiple longest common subsequences (\\n<i>MLCS</i>\\n) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length \\n<inline-formula><tex-math>$\\\\geqslant$</tex-math></inline-formula>\\n 10,000), referred to as big sequences. Despite efforts in improving the time and space complexities of \\n<i>MLCS</i>\\n mining algorithms, both existing exact and approximate algorithms face challenges in handling big sequences due to the overwhelming size of their problem-solving graph model \\n<i>MLCS-DAG</i>\\n (\\n<u>D</u>\\nirected \\n<u>A</u>\\ncyclic \\n<u>G</u>\\nraph), leading to the issue of memory explosion or extremely high time complexity. To bridge the gap, this paper first proposes a new identification and deletion strategy for different classes of non-critical points in the mining of \\n<i>MLCS</i>\\n, which are the points that do not contribute to their \\n<i>MLCS</i>\\ns mining in the \\n<i>MLCS-DAG</i>\\n. It then proposes a new \\n<i>MLCS</i>\\n problem-solving graph model, namely \\n<inline-formula><tex-math>$DAG_{KP}$</tex-math></inline-formula>\\n (a new \\n<i>MLCS-<u>DAG</u></i>\\n containing only \\n<u>K</u>\\ney \\n<u>P</u>\\noints). A novel parallel \\n<i>MLCS</i>\\n algorithm, called \\n<i>KP-MLCS</i>\\n (\\n<u>K</u>\\ney \\n<u>P</u>\\noint based \\n<i><u>MLCS</u></i>\\n), is also presented, which can mine and compress all \\n<i>MLCS</i>\\ns of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm \\n<i>KP-MLCS</i>\\n drastically outperforms the existing state-of-the-art \\n<i>MLCS</i>\\n algorithms in terms of both efficiency and effectiveness.\",\"PeriodicalId\":13496,\"journal\":{\"name\":\"IEEE Transactions on Knowledge and Data Engineering\",\"volume\":\"37 1\",\"pages\":\"15-28\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Knowledge and Data Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10731910/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10731910/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A Novel Key Point Based MLCS Algorithm for Big Sequences Mining
Mining multiple longest common subsequences (
MLCS
) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length
$\geqslant$
10,000), referred to as big sequences. Despite efforts in improving the time and space complexities of
MLCS
mining algorithms, both existing exact and approximate algorithms face challenges in handling big sequences due to the overwhelming size of their problem-solving graph model
MLCS-DAG
(
D
irected
A
cyclic
G
raph), leading to the issue of memory explosion or extremely high time complexity. To bridge the gap, this paper first proposes a new identification and deletion strategy for different classes of non-critical points in the mining of
MLCS
, which are the points that do not contribute to their
MLCS
s mining in the
MLCS-DAG
. It then proposes a new
MLCS
problem-solving graph model, namely
$DAG_{KP}$
(a new
MLCS-DAG
containing only
K
ey
P
oints). A novel parallel
MLCS
algorithm, called
KP-MLCS
(
K
ey
P
oint based
MLCS
), is also presented, which can mine and compress all
MLCS
s of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm
KP-MLCS
drastically outperforms the existing state-of-the-art
MLCS
algorithms in terms of both efficiency and effectiveness.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.