Uncertain-Driven Analytics of Sequence Data in IoCV Environments

Gautam Srivastava, Jerry Chun Wei Lin, Alireza Jolfaei, Yuanfa Li, Youcef Djenouri

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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, our 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. Our 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.

Original languageEnglish
Pages (from-to)5403-5414
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number8
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Keywords

  • IoT
  • knowledge discovery
  • sequential patterns
  • Uncertainty
  • utility

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