对象关系技术对多维数据计算的支持

在多维数据压缩的基础上，提出了多维数据的CUBE计算方法。首先给出了无限内存算法，其次给出了一个不完全立方体算法，最后给出了完整立方体的计算方法。并与其他CUBE算法作了比较。     

一种增量更新FreeCube的方法

数据立方体是联机分析处理的一个重要应用。如何对数据立方体(CUBE)进行更新目前研究相对较少。给出了CUBE的一种新颖的存储结构——FreeCube的定义，该结构大大降低了CUBE存储的空间，分析了它的相关性质，提出了增量更新FreeCube的理论，并给出了具体的算法，以实例说明了该算法的正确性，总结了下一步的工作方向。     

实时主动数据仓库中多维数据实视图的选择

通过基于主动决策引擎日志的数据挖掘来找到分析规则的CUBE使用模式,从而为多维数据实视图选择算法提供重要依据;在此基础上设计了3A概率模型,并给出考虑CUBE受访概率分布的视图选择贪婪算法PGreedy(probability greedy),以及结合视图挽留原则的视图动态调整算法.实验结果表明,在实时主动数据仓库环境下,PGreedy算法比BPUS(benefit per unit space)算法具有更好的性能.     

基于混合存储结构的数据立方体生成算法

通过把数据立方体中的维分为划分维和非划分维，视图中的数据被分成两部分，分别存储在关系和多维数组中。针对这种混合存储结构，我们设计了一个数据立方体生成算法，它结合了流水线聚集方法和多维数组聚集方法的优点，大大减少了流水线的条数和所需要的存储空间，加快了计算速度。并用一个实际数据集进行了实验，结果表明该算法适用于计算高维的数据立方体。     

一种高效的多维关联规则挖掘算法研究

对经典关联规则挖掘算法进行深入研究的基础上,结合数据立方体的结构特点和OLAP技术,给出了一种高效的多维关联规则挖掘算法,并对不同数据立方体下的算法的性能进行了分析比较.     

一种高效的多维关联规则挖掘算法研究

对经典关联规则挖掘算法进行深入研究的基础上，结合数据立方体的结构特点和OLAP技术，给出了一种高效的多维关联规则挖掘算法，并对不同数据立方体下的算法的性能进行了分析比较。     

一种并行处理多维连接和聚集操作的有效方法

随着并行计算算法的完善和廉价、功能强大的多处理机系统的成熟，使得采用多处理机系统来并行处理多维数据仓库的连接和聚集操作成为当前有效提高OLAP查询处理性能的首选技术．为此，提出一种降低连接和聚集操作开销的并行算法PJAMDDC(parallel join and aggregation for multi-dimensional data cube)．算法充分考虑了多维数据立方体的存储机制和多处理机分布系统的结构特点，在原有聚集计算多维数据立方体的搜索点阵逻辑结构的基础上，采用多维数据仓库的层次联合代理(hierarchy combined surrogate)和对立方体的搜索点阵进行加权的方法，使得立方体数据在多个处理机间的分配达到最佳的状态，从而在分割多维数据的同时，提高了并行处理多维连接和聚集操作的效率．算法实验评估表明，PJAMDDC算法并行处理多维数据仓库的连接和聚集操作是有效的．     

基于数据挖掘的移动通信客户消费行为分析

从多维立方体数据模型出发，本文提出了跨多个多维立方体的数据挖掘聚类算子模型，并将基于多维立方体事实物理维度的分类聚类算法，应用于移动通信客户消费行为分析中，提供了消费行为分析的实例和方法。     

多维关联在汽车销售中的应用

为挖掘隐藏在大量汽车销售数据背后的知识,给出了一种基于数据立方体的多维关联挖掘算法.在Visual C 7.0中实现了该算法并将其集成到SQL Server的分析服务中,用该算法对汽车销售数据立方体进行了关联分析,结果显示其正确和可行的.     

基于数据立方体分块的多维关联规则挖掘

介绍了在数据立方体上对于不同可信度的数据进行分块的方法,阐述了基于数据立方体分块的多维关联规则挖掘的算法.     

A topology-preserving optimization algorithm for polycube mapping

We present an effective optimization framework to compute polycube mapping. Composed of a set of small cubes, a polycube well approximates the geometry of the free-form model yet possesses great regularity; therefore, it can serve as a nice parametric domain for free-form shape modeling and analysis. Generally, the more cubes are used to construct the polycube, the better the shape can be approximated and parameterized with less distortion. However, corner points of a polycube domain are singularities of this parametric representation, so a polycube domain having too many corners is undesirable. We develop an iterative algorithm to seek for the optimal polycube domain and mapping, with the constraint on using a restricted number of cubes (therefore restricted number of corner points). We also use our polycube mapping framework to compute an optimal common polycube domain for multiple objects simultaneously for lowly distorted consistent parameterization.     

The Multi-Tree Cubing algorithm for computing iceberg cubes

The computation of data cubes is one of the most expensive operations in on-line analytical processing (OLAP). To improve efficiency, an iceberg cube represents only the cells whose aggregate values are above a given threshold (minimum support). Top-down and bottom-up approaches are used to compute the iceberg cube for a data set, but both have performance limitations. In this paper, a new algorithm, called Multi-Tree Cubing (MTC), is proposed for computing an iceberg cube. The Multi-Tree Cubing algorithm is an integrated top-down and bottom-up approach. Overall control is handled in a top-down manner, so MTC features shared computation. By processing the orderings in the opposite order from the Top-Down Computation algorithm, the MTC algorithm is able to prune attributes. The Bottom Up Computation (BUC) algorithm and its variations also perform pruning by relying on the processing of intermediate partitions. The MTC algorithm, however, prunes without processing such partitions. The MTC algorithm is based on a specialized type of prefix tree data structure, called an Attribute–Partition tree (AP-tree), consisting of attribute and partition nodes. The AP-tree facilitates fast, in-memory sorting and APRIORI-like pruning. We report on five series of experiments, which confirm that MTC is consistently as fast or faster than BUC, while finding the same iceberg cubes.     

一种计算部分数据立方的算法

预计算一个完整的数据立方可以获得最快的查询响应速度,但是对于一个大规模的数据立方,所需的存储空间非常大,因此通常只能预先计算数据立方中的部分聚集。文章提出了计算部分数据立方的算法PCC(PartialComputationofCube),它的特点是采用自底向上的划分方法,能根据需要计算的聚集确定维的划分路径,并裁减不必要的聚集和划分。实验表明,和利用完整数据立方的计算方法BUC来计算部分数据立方的方法比,PCC算法的效率更高。     

New Algorithm for Computing Cube on Very Large Compressed Data Sets

Data compression is an effective technique to improve the performance of data warehouses. Since cube operation represents the core of online analytical processing in data warehouses, it is a major challenge to develop efficient algorithms for computing cube on compressed data warehouses. To our knowledge, very few cube computation techniques have been proposed for compressed data warehouses to date in the literature. This paper presents a novel algorithm to compute cubes on compressed data warehouses. The algorithm operates directly on compressed data sets without the need of first decompressing them. The algorithm is applicable to a large class of mapping complete data compression methods. The complexity of the algorithm is analyzed in detail. The analytical and experimental results show that the algorithm is more efficient than all other existing cube algorithms. In addition, a heuristic algorithm to generate an optimal plan for computing cube is also proposed     

一种新的封闭立方体查询算法

提出了一种新的封闭立方体查询算法,缩小了查询时需搜索的记录的范围,提高了查询效率。给出了相关的理论分析和证明,并给出相关的封闭掩码集生成算法。实验结果和理论分析证明了新算法是有效的,在75%的情况下能将需查询范围包含的记录数减少到传统方法的92%左右,提高了对封闭立方体的查询效率。     

蛇形益智魔方的三维路径求解算法

蛇形益智魔方是智力魔方的一种,由27个两两相连的小正方体组成。文章通过计算机图形学的概念建立空间三维坐标模型,对模型中关键点进行分析归类,对空间中每个方块体可能的坐标点进行了递归公式推导,最后利用三维空间坐标的遍历算法来寻找蛇形魔方所有可能的解,指出了模型的使用前景。     

基于Hadoop的封闭直方图立方

封闭数据立方是一种有效的无损压缩技术,它去掉了数据立方中的冗余信息,从而有效降低了数据立方的存储空间、加快了计算速度,而且几乎不影响查询性能.Hadoop的MapReduce并行计算模型为数据立方的计算提供了技术支持,Hadoop的分布式文件系统HDFS为数据立方的存储提供了保障.为了节省存储空间、加快查询速度,在传统数据立方的基础上提出封闭直方图立方,它在封闭数据立方的基础上通过编码技术进一步节省了存储空间,通过建立索引加快了查询速度.Hadoop并行计算平台不论从扩展性还是均衡性都为封闭直方图立方提供了保证.实验证明:封闭直方图立方对数据立方进行了有效压缩,具有较高的查询性能,根据Hadoop的特点通过增加节点个数明显加快了计算速度.     

蛇形益智魔方的三维路径求解算法

蛇形益智魔方是智力魔方的一种,由27个两两相连的小正方体组成。文章通过计算机图形学的概念建立空间三维坐标模型,对模型中关键点进行分析归类,对空间中每个方块体可能的坐标点进行了递归公式推导,最后利用三维空间坐标的遍历算法采寻找蛇形魔方所有可能的解,指出了模型的使用前景。     

Generation of sign Walsh spectra from disjoint cubes of Boolean functions

A new algorithm is given that converts a reduced representation of Boolean functions in the form of disjoint cubes to sign Walsh spectra. Since the known algorithms that generate sign Walsh spectra always start from the truth table of Boolean functions, the method presented computes faster with a smaller computer memory. The method is especially efficient for such Boolean functions that are described by only few disjoint cubes.     

A type-merging algorithm for extracting an isosurface from volumetric data

A new approach for reducing the number of triangles representing an isosurface in volumetric data is presented. The basic idea is to classify the configurations of the marching cubes approach into types. Surface patches traversing neighboring cubes of the same type can be merged into patches, which can be approximated with fewer and larger triangles. Experimental results show that the number of triangles is about 50% of that obtained with the marching cubes algorithm, with comparable image quality. The execution time is somewhat longer than that of the marching cubes algorithm.