Smart System: Joint Utility and Frequency for Pattern Classification
This work addresses pattern classification for manufacturing organizations in Industry 4.0 and IoT, but it appears incremental as it builds on existing big data analysis methods.
The paper tackled pattern classification for smart systems by proposing UFC_gen and UFC_fast algorithms that use utility and frequency thresholds to identify interesting patterns from candidate patterns, with UFC_fast outperforming UFC_gen in execution time and memory consumption.
Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things (IoT) are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized and thus help manufacturing organizations to finish another round of upgrading. In this paper, we propose two new algorithms with respect to big data analysis, namely UFC$_{gen}$ and UFC$_{fast}$. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFC$_{fast}$ algorithm outperforms the level-wise-based UFC$_{gen}$ algorithm in terms of both execution time and memory consumption.