Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data

Rahmani-Boldaji, Sadegh Rahmani; Bateni, Mehdi; Mortazavi Dehkordi, Mahmood

doi:10.22044/jadm.2023.13034.2448

Document Type : Methodologies

Authors

¹ Computer Engineering, Sheikh Bahaei University, Isfahan, Iran.

² Computer Science and Computer Engineering, University of Isfahan, Khansar Campus, Isfahan, Iran.

³ MSE, University Canada West, Vancouver, Canada.

https://doi.org/10.22044/jadm.2023.13034.2448

Abstract

Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate solution. Additionally, an incremental technique is more suitable to mine patterns from big data streams than static methods. This study presents an incremental parallel approach and compact tree structure for extracting regular-frequent patterns from the data of wireless sensor networks. Furthermore, fewer database scans have been performed in an effort to reduce the mining runtime. This study was performed on Intel 5-day and 10-day datasets with 6, 4, and 2 nodes clusters. The findings show the runtime was improved in all 3 cluster modes by 14, 18, and 34% for the 5-day dataset and by 22, 55, and 85% for the 10-day dataset, respectively.

Keywords

Main Subjects

H.6. Pattern Recognition

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Journal of AI and Data Mining

Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data

References

References

Volume 11, Issue 4
November 2023
Pages 639-648

Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data

References

References

Volume 11, Issue 4November 2023Pages 639-648

Volume 11, Issue 4
November 2023
Pages 639-648