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

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 ۵-day and ۱۰-day datasets with ۶, ۴, and ۲ nodes clusters. The findings show the runtime was improved in all ۳ cluster modes by ۱۴, ۱۸, and ۳۴% for the ۵-day dataset and by ۲۲, ۵۵, and ۸۵% for the ۱۰-day dataset, respectively.

کلیدواژه ها:

Regular-frequent pattern ، Big streaming data ، Parallel algorithm ، Incremental mining