多维联机分析处理中的高效查询关键方法研究
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摘要
联机分析处理（OLAP）是商务智能（BI）的关键技术之一，已成为人们获取知识和辅助决策的重要工具。然而，由信息化程度的提高所引起的数据量增大和数据维度数增多，以及决策支持所要求的高效率等特点，都促使着OLAP高效查询技术的不断发展和进步。因此，在多维海量数据环境中，如何提高OLAP查询的效率，缩短查询时间，以达到辅助决策的目的，就成为研究工作的焦点。
本文以实现多维OLAP高效查询为目标，对提高OLAP查询效率的若干关键方法进行了研究。为了充分发挥数据分析的功能，本文借鉴了联机分析挖掘（OLAM）的概念，将数据挖掘、统计分析方法运用到研究过程中，设计了一个集成了数据挖掘、统计分析方法的OLAP查询框架，以提高OLAP查询效率。在这个框架中，对提高OLAP查询效率的三项关键技术进行了研究：由于数据立方体是进行OLAP查询的数据基础，其构建方法直接影响OLAP查询的效率，因此本文对其物化方法进行了研究；OLAP近似查询方法能够在查询时间与查询精度之间实现很好的折衷，有利于OLAP查询效率的显著提升，因此也成为本文研究的主要内容之一；OLAP查询维度推荐也是本文的研究内容之一，它以辅助决策为出发点，为用户提供与查询目标密切相关的维度，以缩短OLAP查询时间。具体来说，本文针对上述OLAP高效查询方法，进行了以下研究：
(1)本文将数据挖掘的思想引入到提高OLAP查询效率的研究过程中。将关联规则挖掘技术中经典Apriori理论的思想运用于OLAP查询的数据基础——数据立方体的构建过程中，并提出用户兴趣度的概念，以此作为约束条件，以用户使用系统进行查询的实际情况为依据，设计了数据立方体部分物化的冰山立方体构建算法，以及增量式更新冰山立方体的方法。该方法通过对数据方体进行有选择的物化，使得系统在处理用户查询时不需通过即时计算。同时，由于考虑了用户查询的实际情况，该方法在大大节省数据存储空间的同时，使得此数据立方体支持用户查询的程度保持在较高水平，进一步提高了OLAP查询的效率。
(2)本文首次将统计方法中的Copula理论引入到OLAP近似查询建模过程中，扩大了Copula理论的应用领域，针对连续维度建立了OLAP范围查询模型。本文所建立的模型提取了大量数据中的概要信息，只需要存储相关样本及参数信息，大大节省了数据的存储空间，且在保证查询准确率的前提下大大提高了OLAP查询效率。为了提升OLAP近似查询模型的精确度，本文采取了一系列措施。首先，在对各个维度样本数据进行边缘分布拟合时，为了使拟合效果更准确，本文采用了非参数核密度估计方法代替分布已知的参数方法对样本数据进行拟合，将模型的实用性推广到大部分数据；其次，本文充分考虑了各维度之间可能存在相关性的情况，使用Copula函数对联合分布进行拟合，提取维度间的相依结构，使分布拟合的结果更精确。同时，模型支持在连续维度上直接进行OLAP查询，当进行钻取操作时不需要事先设定维度的层次，大大增加了OLAP查询的灵活性。
(3)针对维度较高的OLAP数据集，本文将适用于OLAP数据立方体的基于“C藤”结构的Pair Copula方法引入到OLAP近似查询的建模过程中，在使用Copula函数的基础上进一步考虑了不同观察维度与度量维度之间相关性的差异，根据样本数据的特征自由选取和构造其相关结构，使得模型拟合结果的精确度得到进一步提升，并使得模型适用于高维数据环境。
(4)在将数据挖掘思想引入OLAP查询的研究过程中，本文还针对高维OLAP数据集，将变量选择方法运用到OLAP查询维度推荐上。由于高维OLAP数据集具有所含信息量大、不同维度间具有不同程度的相关性等特点，因此一定程度上影响了用户OLAP查询的效率，从而干扰了用户决策的效率和准确性。针对数据中存在对查询目标而言的冗余维度，本文设计了一种支持OLAP查询维度推荐的维度选择算法。该算法根据用户提供的决策属性分类信息来有针对性地去除与决策目标不相关的维度，并且同时找出具有线性相关性的维度集合，在有效识别观察维度之间的相关关系的同时提取与用户查询目标关联最紧密的维度集合，从而大大提高用户的查询与决策效率。
As one of the key techniques of business intelligence (BI), on-lineanalytical processing (OLAP) is a very important way for knowledgeacquisition and decision support. Meanwhile, due to the increase of datavolumn and data dimensionality caused by the improvement ofinformatization, and the high-efficiency required by decision support, theresearch on efficient OLAP query is developing. Aiming at decisionsupport, it becomes an important research topic to study how to improvethe multi-dimensional OLAP query efficiency in massive high-dimensional data sets so as to shorten the query time.
The purpose of this dissertation is to realize the high-efficiency ofmulti-dimensional OLAP query by studying the key approaches forimproving the OLAP query efficiency. In order to bring the full play ofdata analysis, the concept of on-line analytical mining (OLAM) is usedfor reference in this dissertation. Data mining techniques and statistical analysis approaches are integrated to form an OLAP query frame. Thenthe key techniques are studied in the framework. Since data cube is thebase of OLAP query of which the construction way directly influences onthe OLAP query efficiency, the materialization way of data cube isstudied. On the other hand, OLAP approximate query approach is capableto realize the tradeoff between the query time and the query accuracy,which is beneficial to remarkably improve the OLAP query efficiency, soit is also considered to be a part of main content of this dissertation.Meanwhile, in order to improve the efficiency of OLAP query, therecommendation of OLAP query dimensions is another way from theother perspective. This approach focuses on aid decision making, whichprovides the users the dimensions closely related to the query target so asto shorten the query time. To study on the approaches which can improvethe efficiency of OLAP query mentioned above, the primary work of thisdissertation includes:
(1) The idea of data mining is introduced into the research onhow to improve the OLAP query efficiency in this dissertation. TheApriori theory is applied in building data cube which is the base of OLAPquery. User-interest is proposed to be the constraint condition of choosingthe frequent data cuboids. According to the frequently used queries, aniceberg data cube construction algorithm is designed to construct the datacube, meanwhile the method to incrementally update the iceberg cube isproposed. It enables the system to respond the OLAP query withoutrealtime computing by partial materialization. On the other hand, sincethe approach is based on the real log of queries, the OLAP queryefficiency is further improved due to the strong support to the queriesrequested by users while the data storage space is drastically reduced.
(2) Model construction for OLAP approximate query is also aneffective way to improve the query efficiency. During the research,Copula theory is extended to the new filed to build a statistical model on continuous dimensions for range queries. The model is used to extract thedata synopsis, which stores the related samples and the information ofparameters. The model drastically saves the data storage space whileimproves the OLAP query efficiency guaranteed with acceptableaccuracy rate. In order to improve the accuracy of OLAP approximatequery model, some methods are carried out to solve the problem. First, inorder to fit the marginal distribution of each dimension more precise,nonparametric kernel density estimation is applied instead of parametricmodels with known distribution, by which the applicability of the modelis extended to most type of data. Second, after considering the existingcorrelation between dimensions, Copula is used to fit the jointdistribution, which is aimed at extracting the dependency structure to fitthe data distribution more accurately. On the other hand, it is easy toimplement OLAP operations like drill-down or roll-up on continuousdimensions without setting up the dimension levels in advance, which makes the OLAP query procedure flexible.
(3) The high-dimensional data environment is considered in thisdissertation.“C-Vine” Pair Copula is adopted, which makes a further stepon applying Copula to OLAP approximate query. It fits the structure ofOLAP data set, and the difference of correlation between differentobservation dimensions and measure dimension is taken into account, bywhich the dependency structure is constructed according to the features ofsamples. The accuracy of the query results derived by the model is furtherimproved, especially in high-dimensional data environment.
(4) From the perspective of introducing data mining to OLAPquery, feature selection is applied to OLAP query dimensionrecommendation in high-dimensional OLAP data set. The OLAP data setincludes massive data and there is correlation with different intensitybetween different dimensions. Consequently, the OLAP query efficiencyis influenced and the decision efficiency and accuracy are interfered. In order to remove the redundant dimensions according to the query target, adimension selection algorithm is designed to support the recommendationof OLAP query dimensions. This algorithm is capable to remove thedimensions not related to the decision target based on the information ofdecision attribute classification, as well as to recognize the dimension setincluding the correlated dimensions. The purpose of this work is toprovide users the referential query dimension set, so as to improve thequery and decision efficiency.
本文以实现多维OLAP高效查询为目标，对提高OLAP查询效率的若干关键方法进行了研究。为了充分发挥数据分析的功能，本文借鉴了联机分析挖掘（OLAM）的概念，将数据挖掘、统计分析方法运用到研究过程中，设计了一个集成了数据挖掘、统计分析方法的OLAP查询框架，以提高OLAP查询效率。在这个框架中，对提高OLAP查询效率的三项关键技术进行了研究：由于数据立方体是进行OLAP查询的数据基础，其构建方法直接影响OLAP查询的效率，因此本文对其物化方法进行了研究；OLAP近似查询方法能够在查询时间与查询精度之间实现很好的折衷，有利于OLAP查询效率的显著提升，因此也成为本文研究的主要内容之一；OLAP查询维度推荐也是本文的研究内容之一，它以辅助决策为出发点，为用户提供与查询目标密切相关的维度，以缩短OLAP查询时间。具体来说，本文针对上述OLAP高效查询方法，进行了以下研究：
(1)本文将数据挖掘的思想引入到提高OLAP查询效率的研究过程中。将关联规则挖掘技术中经典Apriori理论的思想运用于OLAP查询的数据基础——数据立方体的构建过程中，并提出用户兴趣度的概念，以此作为约束条件，以用户使用系统进行查询的实际情况为依据，设计了数据立方体部分物化的冰山立方体构建算法，以及增量式更新冰山立方体的方法。该方法通过对数据方体进行有选择的物化，使得系统在处理用户查询时不需通过即时计算。同时，由于考虑了用户查询的实际情况，该方法在大大节省数据存储空间的同时，使得此数据立方体支持用户查询的程度保持在较高水平，进一步提高了OLAP查询的效率。
(2)本文首次将统计方法中的Copula理论引入到OLAP近似查询建模过程中，扩大了Copula理论的应用领域，针对连续维度建立了OLAP范围查询模型。本文所建立的模型提取了大量数据中的概要信息，只需要存储相关样本及参数信息，大大节省了数据的存储空间，且在保证查询准确率的前提下大大提高了OLAP查询效率。为了提升OLAP近似查询模型的精确度，本文采取了一系列措施。首先，在对各个维度样本数据进行边缘分布拟合时，为了使拟合效果更准确，本文采用了非参数核密度估计方法代替分布已知的参数方法对样本数据进行拟合，将模型的实用性推广到大部分数据；其次，本文充分考虑了各维度之间可能存在相关性的情况，使用Copula函数对联合分布进行拟合，提取维度间的相依结构，使分布拟合的结果更精确。同时，模型支持在连续维度上直接进行OLAP查询，当进行钻取操作时不需要事先设定维度的层次，大大增加了OLAP查询的灵活性。
(3)针对维度较高的OLAP数据集，本文将适用于OLAP数据立方体的基于“C藤”结构的Pair Copula方法引入到OLAP近似查询的建模过程中，在使用Copula函数的基础上进一步考虑了不同观察维度与度量维度之间相关性的差异，根据样本数据的特征自由选取和构造其相关结构，使得模型拟合结果的精确度得到进一步提升，并使得模型适用于高维数据环境。
(4)在将数据挖掘思想引入OLAP查询的研究过程中，本文还针对高维OLAP数据集，将变量选择方法运用到OLAP查询维度推荐上。由于高维OLAP数据集具有所含信息量大、不同维度间具有不同程度的相关性等特点，因此一定程度上影响了用户OLAP查询的效率，从而干扰了用户决策的效率和准确性。针对数据中存在对查询目标而言的冗余维度，本文设计了一种支持OLAP查询维度推荐的维度选择算法。该算法根据用户提供的决策属性分类信息来有针对性地去除与决策目标不相关的维度，并且同时找出具有线性相关性的维度集合，在有效识别观察维度之间的相关关系的同时提取与用户查询目标关联最紧密的维度集合，从而大大提高用户的查询与决策效率。
As one of the key techniques of business intelligence (BI), on-lineanalytical processing (OLAP) is a very important way for knowledgeacquisition and decision support. Meanwhile, due to the increase of datavolumn and data dimensionality caused by the improvement ofinformatization, and the high-efficiency required by decision support, theresearch on efficient OLAP query is developing. Aiming at decisionsupport, it becomes an important research topic to study how to improvethe multi-dimensional OLAP query efficiency in massive high-dimensional data sets so as to shorten the query time.
The purpose of this dissertation is to realize the high-efficiency ofmulti-dimensional OLAP query by studying the key approaches forimproving the OLAP query efficiency. In order to bring the full play ofdata analysis, the concept of on-line analytical mining (OLAM) is usedfor reference in this dissertation. Data mining techniques and statistical analysis approaches are integrated to form an OLAP query frame. Thenthe key techniques are studied in the framework. Since data cube is thebase of OLAP query of which the construction way directly influences onthe OLAP query efficiency, the materialization way of data cube isstudied. On the other hand, OLAP approximate query approach is capableto realize the tradeoff between the query time and the query accuracy,which is beneficial to remarkably improve the OLAP query efficiency, soit is also considered to be a part of main content of this dissertation.Meanwhile, in order to improve the efficiency of OLAP query, therecommendation of OLAP query dimensions is another way from theother perspective. This approach focuses on aid decision making, whichprovides the users the dimensions closely related to the query target so asto shorten the query time. To study on the approaches which can improvethe efficiency of OLAP query mentioned above, the primary work of thisdissertation includes:
(1) The idea of data mining is introduced into the research onhow to improve the OLAP query efficiency in this dissertation. TheApriori theory is applied in building data cube which is the base of OLAPquery. User-interest is proposed to be the constraint condition of choosingthe frequent data cuboids. According to the frequently used queries, aniceberg data cube construction algorithm is designed to construct the datacube, meanwhile the method to incrementally update the iceberg cube isproposed. It enables the system to respond the OLAP query withoutrealtime computing by partial materialization. On the other hand, sincethe approach is based on the real log of queries, the OLAP queryefficiency is further improved due to the strong support to the queriesrequested by users while the data storage space is drastically reduced.
(2) Model construction for OLAP approximate query is also aneffective way to improve the query efficiency. During the research,Copula theory is extended to the new filed to build a statistical model on continuous dimensions for range queries. The model is used to extract thedata synopsis, which stores the related samples and the information ofparameters. The model drastically saves the data storage space whileimproves the OLAP query efficiency guaranteed with acceptableaccuracy rate. In order to improve the accuracy of OLAP approximatequery model, some methods are carried out to solve the problem. First, inorder to fit the marginal distribution of each dimension more precise,nonparametric kernel density estimation is applied instead of parametricmodels with known distribution, by which the applicability of the modelis extended to most type of data. Second, after considering the existingcorrelation between dimensions, Copula is used to fit the jointdistribution, which is aimed at extracting the dependency structure to fitthe data distribution more accurately. On the other hand, it is easy toimplement OLAP operations like drill-down or roll-up on continuousdimensions without setting up the dimension levels in advance, which makes the OLAP query procedure flexible.
(3) The high-dimensional data environment is considered in thisdissertation.“C-Vine” Pair Copula is adopted, which makes a further stepon applying Copula to OLAP approximate query. It fits the structure ofOLAP data set, and the difference of correlation between differentobservation dimensions and measure dimension is taken into account, bywhich the dependency structure is constructed according to the features ofsamples. The accuracy of the query results derived by the model is furtherimproved, especially in high-dimensional data environment.
(4) From the perspective of introducing data mining to OLAPquery, feature selection is applied to OLAP query dimensionrecommendation in high-dimensional OLAP data set. The OLAP data setincludes massive data and there is correlation with different intensitybetween different dimensions. Consequently, the OLAP query efficiencyis influenced and the decision efficiency and accuracy are interfered. In order to remove the redundant dimensions according to the query target, adimension selection algorithm is designed to support the recommendationof OLAP query dimensions. This algorithm is capable to remove thedimensions not related to the decision target based on the information ofdecision attribute classification, as well as to recognize the dimension setincluding the correlated dimensions. The purpose of this work is toprovide users the referential query dimension set, so as to improve thequery and decision efficiency.
引文
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