Smart System: Joint Utility and Frequency for Pattern Classification

TL;DR

This paper introduces two novel algorithms, UFC_gen and UFC_fast, for pattern classification in big data environments, specifically tailored for Industry 4.0 and IoT applications, demonstrating improved efficiency and effectiveness.

Contribution

The paper presents two new algorithms for pattern classification that incorporate utility and frequency, enhancing data analysis in smart manufacturing systems.

Findings

UFC_fast outperforms UFC_gen in speed and memory efficiency.

Both algorithms successfully classify patterns using utility and frequency thresholds.

Experimental results validate the effectiveness of the proposed algorithms on real and synthetic datasets.

Abstract

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.