Data stream prediction based on rule antecedent occurrence tree matching

Tao YOU; Ting-feng LI; Cheng-lie DU; Dong ZHONG; Yi-an ZHU

doi:10.11959/j.issn.1000-436x.2017286

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Data stream prediction based on rule antecedent occurrence tree matching

Papers | 更新时间：2024-06-05

- Data stream prediction based on rule antecedent occurrence tree matching
- Journal on Communications Vol. 38, Issue 12, Pages: 98-108(2017)
- 作者机构：
  
  西北工业大学计算机学院，陕西西安 710129
- 作者简介：
- 基金信息：
  
  2017 Aviation Science Key Foundation of China;2016 Open Fund of State Key Laboratory Intelligent Manufacturing System Technology
- DOI：10.11959/j.issn.1000-436x.2017286
  CLC： TP311
- Online First：2017-12，
  
  Published：25 December 2017
- 稿件说明：
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Tao YOU, Ting-feng LI, Cheng-lie DU, et al. Data stream prediction based on rule antecedent occurrence tree matching[J]. Journal on Communications, 2017, 38(12): 98-108.
DOI：

Tao YOU, Ting-feng LI, Cheng-lie DU, et al. Data stream prediction based on rule antecedent occurrence tree matching[J]. Journal on Communications, 2017, 38(12): 98-108. DOI： 10.11959/j.issn.1000-436x.2017286.

摘要

现有基于规则匹配的数据流预测算法存在前件发生定义不准确、前件相关性未考虑、预测结果描述不严谨等不足，造成预测过程效率较低、精度不高等问题。提出基于前件发生树的概率叠加预测算法，定义区间最小非重叠发生，避免前件的错误匹配；通过前件的合并构建前件发生树，提高前件发生的搜索效率；基于概率叠加的思想计算后件的发生区间和发生概率，使预测精度进一步提高。理论分析和实验结果表明，该算法具有较高的时空效率和预测精度。

Abstract

There are some shortages in the existing rule-based data stream prediction algorithm，such as inaccurate definition of antecedent occurrence，ignoring the correlation between rules and imprecise description of prediction accuracy.These make low forecasting process efficiency and low prediction accuracy.The superposed prediction algorithm was proposed based on antecedent occurrence tree，and interval minimal non-overlapping occurrence was defined to avoid the problem of excessive matching antecedent.The efficiency was improved for searching antecedent’s occurrence by merging rule’s antecedents in antecedent occurrence tree，and the succedent occurrence based on superposed probability was predicted to enhance prediction accuracy.The theoretical analysis and experimental evaluation demonstrate the algorithm is superior to the existing prediction algorithms in terms of time and space efficiency and prediction accuracy.

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references

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