基于规则前件发生树匹配的数据流预测方法研究

尤涛; 李廷峰; 杜承烈; 钟冬; 朱怡安

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

您当前的位置：

首页 >

文章列表页 >

基于规则前件发生树匹配的数据流预测方法研究

学术论文 | 更新时间：2024-06-05

- 基于规则前件发生树匹配的数据流预测方法研究
- Data stream prediction based on rule antecedent occurrence tree matching
- 通信学报 2017年38卷第12期页码：98-108
- 作者机构：
  
  西北工业大学计算机学院，陕西西安 710129
- 作者简介：
  
  [ "尤涛（1983-），男，河南陕县人，博士，西北工业大学副教授，主要研究方向为分布式数据处理。" ]
  [ "李廷峰（1992-），男，黑龙江鸡西人，西北工业大硕士生，主要研究方向为数据库与数据挖掘。" ]
  [ "杜承烈（1970-），男，陕西西安人，博士，西北工业大学教授，主要研究方向为军用软件工程。" ]
  [ "钟冬（1979-），男，陕西西安人，博士，西北工业大学副教授，主要研究方向为计算机网络技术。" ]
  [ "朱怡安（1961-），男，陕西西安人，博士，西北工业大学教授，主要研究方向为并行计算。" ]
- 基金信息：
  
  2017航空科学重点基金资助项目;2016复杂产品智能制造系统技术国家重点实验开放研究基金资助项目
- DOI：10.11959/j.issn.1000-436x.2017286
  中图分类号： TP311
- 网络出版日期：2017-12，
  
  纸质出版日期：2017-12-25
- 稿件说明：
移动端阅览
尤涛, 李廷峰, 杜承烈, 等. 基于规则前件发生树匹配的数据流预测方法研究[J]. 通信学报, 2017,38(12):98-108.

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.
尤涛, 李廷峰, 杜承烈, 等. 基于规则前件发生树匹配的数据流预测方法研究[J]. 通信学报, 2017,38(12):98-108. DOI： 10.11959/j.issn.1000-436x.2017286.

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.

关键词

Keywords

references

LAXMAN S , TANKASALI V , WHITE R W . Stream prediction using a generative model based on frequent episodes in event sequences [C ] // The 14th ACM SIGKDD International Conference on Knowledge Discovery and data mining . New York,US , 2008 : 453 - 461 .

KHAN M S , COENEN F , REID D , et al . A sliding windows base dual support framework for discovering emerging trends from temporal data [J ] . Knowledge-Based Systems , 2010 , 23 ( 4 ): 316 - 322 .

CHO C W , ZHENG Y , WU Y H , et al . A tree-based approach for event prediction using episode rules over event streams [C ] // Database and Expert Systems Applications.Berlin,Germany . 2008 : 225 - 240 .

CHAVENT M , GIRARD S , KUENTZ-SIMONET V , et al . A sliced inverse regression approach for data stream [J ] . Computational Statistics , 2013 : 1 - 24 .

MONTGOMERY D C , PECK E A , VINING G G . Introduction to linear regression analysis [M ] . Manhattan : John Wiley＆Sons , 2012 .

CHO C W , ZHENG Y , CHEN A L P . Continuously matching episode rules for predicting future events over event streams [M ] // Advances in Data and Web Management . Berlin : Springer , 2007 .

CHO C W , WU Y H , YEN S J , et al . On-line rule matching for event prediction [J ] . The VLDB Journal , 2011 , 20 ( 3 ): 303 - 334 .

YANG Q , LI T Y , WANG K . Building association-rule based sequential classifier for Web-document prediction [J ] . Data Mining and Knowledge Discovery , 2004 , 8 ( 3 ): 253 - 273 .

MANNILA H , TOIVONEN H . Discovering generalized episodes using minimal occurrences [C ] // The Second International Conference on Knowledge Discovery and Data Mining . Portland,USA , 1996 : 146 - 151 .

LAXMAN S , SASTRY P S , UNNIKRISHNAN K P . Discovering frequent episodes and learning hidden Markov models:a formal connection [J ] . IEEE Transactions on Knowledge and Data Engineering , 2005 , 17 ( 11 ): 1505 - 1517 .

ZHU H , WANG P , HE X , et al . Efficient episode mining with minimal and non-overlapping occurrences [C ] // 2010 IEEE 10th International Conference on . Sydney,Australia , 2010 : 1211 - 1216 .

ZHOU W , LIU H , CHENG H . Mining closed episodes from event sequence efficiently [C ] // The 14th Pacific-Asia Conf on Advances in Knowledge Discovery and Data Mining . Berlin,Germany , 2010 : 310 - 318

WAKABAYASHI K , MIURA T . Data stream prediction using incremental hidden Markov models [M ] . Springer Berlin Heidelberg , 2009 .

朱辉生 , 汪卫 , 施伯乐 . 基于情节规则匹配的数据流预测 [J ] . 软件学报 , 2012 , 23 ( 5 ): 1183 - 1194 .

ZHU H S , WANG W , SHI B L . Data stream prediction based on episode rule matching [J ] . Journal of Software , 2012 , 23 ( 5 ): 1183 - 1194 .

GYLLSTROM D , AGRAWAL J , DIAO Y , et al . On supporting Kleene closure over event streams [C ] // The IEEE International Conference on Data Engineering . Cancun,Mexico , 2008 : 1391 - 1393 .

GIANNOZZI P , BARONI S , BONINI N , et al . Quantum ESPRESSO:a modular and open-source software project for quantum simulations of materials [J ] . Journal of Physics:Condensed Matter , 2009 , 21 ( 39 ): 395 - 502 .

MEI Y , MADDEN S . Zstream:a cost-based query processor for adaptively detecting composite events [C ] // The 2009 ACM SIGMOD International Conference on Management of data . Providence,USA , 2009 : 193 - 206 .

浏览量

799

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于负载均衡的随机作业流密码服务调度算法

基于时间密度的数据流匿名方法