基于数据立方体的评估特征项生成方法
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摘要
针对传统评估指标生成模式无法适应作战态势快速变化的问题,提出一种基于数据立方体的评估特征项生成方法。以兵棋推演数据为基础,基于数据立方体框架构建评估特征项生成模型,利用不同维度组合生成评估特征项。围绕数据立方体中存在的"维度爆炸"问题,利用维度组合裁剪模型来缩小搜索空间,通过特征项标识算法进行唯一标识,并采用移动时间窗口提取评估特征数据。实验证明,该方法生成的评估特征项能够有效支撑对特定问题的评估。
For the problem of traditional generation pattern of evaluation indicators not adapting to rapid change of combat situation, a generation method of evaluation features based on data cube is proposed. Based on wargaming data, generation model of evaluation features is built in the data cube framework, and evaluation features are generated by different dimensions combination. Around the problem of dimension explosion in data cube, dimension combination reduction model is used to narrow the search space, and unique identification is calculated by feature identification algorithm, and evaluation feature data is extracted by moving time window. The test result show that evaluation features generated by the method can support special problem evaluation effectively.
For the problem of traditional generation pattern of evaluation indicators not adapting to rapid change of combat situation, a generation method of evaluation features based on data cube is proposed. Based on wargaming data, generation model of evaluation features is built in the data cube framework, and evaluation features are generated by different dimensions combination. Around the problem of dimension explosion in data cube, dimension combination reduction model is used to narrow the search space, and unique identification is calculated by feature identification algorithm, and evaluation feature data is extracted by moving time window. The test result show that evaluation features generated by the method can support special problem evaluation effectively.
引文
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[2] 冷芳玲.支持高效查询的数据立方构建技术研究[D].沈阳:东北大学博士学位论文,2008.
[3] Han J,Kamber M,Pei J.数据挖掘概念与技术[M].范明,孟小峰译.北京:机械工业出版社.2012.
[4] 张亮,白振兴,周军,等.一种生成封闭数据立方体的新算法[J].弹箭与制导学报,2010,30(3):247-250.
[5] 高雅卓.多维联机分析处理中的高效查询关键方法研究[D].合肥:合肥工业大学博士学位论文,2012.
[6] Wang W,Lu H J,Feng J L,et al.Condensed cube:an effective approach to reducing data cube size[C]//Proceedings of the 18th International Conference on Data Engineering(ICDE),San Jose,California,USA,2002.Washington,DC,USA:IEEE Computer Society,2002:155-165.
[7] Lakshmanan L V S,Pei J,Han J W.Quotient cube:how to summarize the semantics of a data cube[C]//Proceedings of the 28th International Conference on Very Large Data Bases(VLDB),Hong Kong,China,2002.[S.l.]:Morgan Kaufmann,2002:778-789.
[8] Sismanis Y,Deligiannakis A,Roussopoulos N,et al.Dwarf:shrinking the PetaCube[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data,Madison,Wisconsin,USA,2002.New York,NY,USA:ACM Press,2002:464-475.
[9] 奚建清,游进国,汤德佑,等.基于MapReduce的封闭立方体并行计算方法[J].华南理工大学学报(自然科学版),2009,37(1):91-95.
[10] 师金钢,鲍玉斌,冷芳玲,等.MapReduce环境下的并行Dwarf立方构建[J].计算机科学与探索,2011,5(5):398-409.
[11] 牟雁超,李红燕,王腾蛟.PHCC:一种处理稀疏变化的封闭数据立方体算法[J].计算机研究与发展,2013,50(Suppl.):85-93.
[12] 唐珊珊,朱跃龙,朱凯.基于Map/Reduce的外壳片段立方体并行计算方法[J].计算机工程与应用,2015,51(22):124-129.
[13] 金苍宏,刘泽民,吴明晖,等.一种流数据立方体分析挖掘框架[J].电信科学,2014,9:61-71.
[14] 张子轩,万定生,朱凯.层次维编码片段立方体生成算法应用研究[J].计算机技术与发展,2017,27(2):134-138.
[15] 王洋,游进国,张婷,等.数据立方体格的图结构特性研究[J].计算机工程,2017,43(2):68-73.
[16] 付岩,冯径,钱越英.面向大数据的物化视图选择算法[J].计算机应用,2017,37(S1):250-254.
[17] 李玮,张大方,黄昆,等.面向大数据处理的高精度多维计数布鲁姆过滤器[J].电子学报,2015,43(4):652-657.
[18] 罗文涛,许蕴山,肖冰松,等.预警探测中的多传感器多目标分配[J].电光与控制,2014,21(11):28-32.