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Efficient discovery of longest-lasting correlation in sequence databases
Yuhong Li1; Leong Hou U1; Man Lung Yiu2; Zhiguo Gong1
2016-06-23
Source PublicationVLDB Journal
ISSN1066-8888
Volume25Issue:6Pages:767-790
Abstract

The search for similar subsequences is a core module for various analytical tasks in sequence databases. Typically, the similarity computations require users to set a length. However, there is no robust means by which to define the proper length for different application needs. In this study, we examine a new query that is capable of returning the longest-lasting highly correlated subsequences in a sequence database, which is particularly helpful to analyses without prior knowledge regarding the query length. A baseline, yet expensive, solution is to calculate the correlations for every possible subsequence length. To boost performance, we study a space-constrained index that provides a tight correlation bound for subsequences of similar lengths and offset by intraobject and interobject grouping techniques. To the best of our knowledge, this is the first index to support a normalized distance metric of arbitrary length subsequences. In addition, we study the use of a smart cache for disk-resident data (e.g., millions of sequence objects) and a graph processing unit-based parallel processing technique for frequently updated data (e.g., nonindexable streaming sequences) to compute the longest-lasting highly correlated subsequences. Extensive experimental evaluation on both real and synthetic sequence datasets verifies the efficiency and effectiveness of our proposed methods.

KeywordLongest-lasting Correlated Subsequences Similarity Search Time Series Analysis
DOI10.1007/s00778-016-0432-7
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems
WOS IDWOS:000387501000002
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Macau SAR, China
2.Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yuhong Li,Leong Hou U,Man Lung Yiu,et al. Efficient discovery of longest-lasting correlation in sequence databases[J]. VLDB Journal,2016,25(6):767-790.
APA Yuhong Li,Leong Hou U,Man Lung Yiu,&Zhiguo Gong.(2016).Efficient discovery of longest-lasting correlation in sequence databases.VLDB Journal,25(6),767-790.
MLA Yuhong Li,et al."Efficient discovery of longest-lasting correlation in sequence databases".VLDB Journal 25.6(2016):767-790.
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