一种面向自动化企业的并行频繁项集挖掘算法

IF 4.4 4区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Enterprise Information Systems Pub Date : 2023-05-03 DOI:10.1080/17517575.2023.2204317

Yimin Mao, Bin-Chang Wu, Qianhu Deng, S. Mahmoodi, Zhigang Chen, Yeh-Cheng Chen

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引用次数: 0

摘要

摘要在机器人过程自动化（RPA）中，制造数据的异构性、数量和实时速度会影响数据分析过程中的业务效率。为了提高业务效率，设计了一种基于MapReduce的并行频繁项集挖掘算法（PMRARIM-EG）。该算法旨在解决CanTree过度使用空间、无法动态设置支持阈值以及Map和Reduce阶段耗时的数据传输等问题。实验表明，与传统的并行频繁项集挖掘算法相比，该算法具有较低的内存占用率和较高的并行效率。

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A novel parallel frequent itemset mining algorithm for automatic enterprise

ABSTRACT Heterogeneity, volume and real-time velocity of manufacturing data affect the business efficiency within the process for analyzing data in Robotic Process Automation (RPA). A novel parallel frequent itemset mining algorithm based on MapReduce (PMRARIM-IEG) is designed to improve the business efficiency. The algorithm is designed to address issues such as the CanTree's excessive space usage, the inability to dynamically set the support threshold, and the time-consuming data transmission during the Map and Reduce phases. Experiments show that the proposed algorithm has lower memory usage and higher parallel efficiency than the traditional parallel frequent itemset mining algorithm.

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来源期刊

Enterprise Information Systems 工程技术-计算机：信息系统

CiteScore

11.00

自引率

6.80%

发文量

审稿时长

6 months

期刊介绍： Enterprise Information Systems (EIS) focusses on both the technical and applications aspects of EIS technology, and the complex and cross-disciplinary problems of enterprise integration that arise in integrating extended enterprises in a contemporary global supply chain environment. Techniques developed in mathematical science, computer science, manufacturing engineering, and operations management used in the design or operation of EIS will also be considered.