Shahrood University of Technology Journal of AI and Data Mining 2322-5211 11 4 2023 11 01 Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data 639 648 3033 10.22044/jadm.2023.13034.2448 EN Sadegh Rahmani Rahmani-Boldaji Computer Engineering, Sheikh Bahaei University, Isfahan, Iran. Mehdi Bateni Computer Science and Computer Engineering, University of Isfahan, Khansar Campus, Isfahan, Iran. Mahmood Mortazavi Dehkordi MSE, University Canada West, Vancouver, Canada. Journal Article 2023 05 05 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. https://jad.shahroodut.ac.ir/article_3033_5d560ccdd58e2aa4704b74bb6fe347b2.pdf