Uncertain-Driven Analytics of Sequence Data in IoCV Environments
As the increasing availability and use of dynamic mobile communications, information from an Internet of Things (IoT) subset of devices, known as Internet of Connected Vehicles (IoCV), is collected with a level of uncertainty. To bridge this gap of data analytics, some studies take two factors individually to mine knowledge or information, such as uncertainty and utility as two exemplary factors. However, this approach may cause actual loss of knowledge integrity. In this work, the authors' first result is a knowledge called High Expected Utility Sequential Patterns (HEUSPs) that is both novel and also provides an alternative option for knowledge discovery regarding utility and uncertainty factors by a single threshold in IoCV environments. Furthermore, two PUL-Chain and EUL-Chain structures with six pruning methodologies are respectively developed to maintain information that is necessary and reduce the search space for improving mining performance. The experimental results show both efficiency and strength of the designed algorithm compared to HUS-Span which is considered to be the current standard in utility-oriented sequential pattern mining.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2021, IEEE.
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Authors:
- Srivastava, Gautam
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0000-0001-9851-4103
- Lin, Jerry Chun-Wei
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0000-0001-8768-9709
- Jolfaei, Alireza
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0000-0001-7818-459X
- Li, Yuanfa
- Djenouri, Youcef
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0000-0003-0135-7450
- Publication Date: 2021-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 5403-5414
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 22
- Issue Number: 8
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Connected vehicles; Data analysis; Data mining; Mobile communication systems; Uncertainty
- Identifier Terms: Internet of Moving Things
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01787755
- Record Type: Publication
- Files: TRIS
- Created Date: Nov 11 2021 3:21PM