Parallel Distributive Join Algorithm on the Intel Paragon

In this paper, we analyze the performance of the parallel Distributive Join algorithm that we proposed in Chung and Yang 1995. We implemented the algorithm on an Intel Paragon machine and analyzed the effect of the number of processors and the join selectivity on the performance of the algorithm. We also compared the performance of the Distributive Join (DJ) algorithm with that of the Hybrid-Hash(HH) join algorithm. Our results show that the DJ performs comparably with the HH over the entire range of number of processors used and different join selectivities. A big advantage of the parallel DJ algorithm over the HH join algorithm is that it can easily support non-equijoin operations. The results can also be used to estimate the performance of file I/O intensive applications to be implemented on the Intel Paragon machine.     

基于直方图的并行结构连接算法

连接操作是最昂贵且常用的数据库操作．在传统数据库系统中，主要的连接操作是等值连接操作，因此，传统的并行连接算法主要集中于并行等值连接操作．另外，随着XML在Web应用中变得越来越重要，XML已经成为Internet上一种新的数据交换标准．对XML数据的连接操作不同于传统数据库中的等值连接操作，它属于结构连接操作．以前适合等值连接操作的并行连接算法并不能有效地解决结构连接问题．因此，第1次提出了并行结构连接问题，并且通过应用直方图的思想于并行连接中，从而提出两种基本的并行XML结构连接算法、等高直方图连接算法和等宽直方图连接算法．实验表明这两种算法具有较好的性能．     

抗数据偏斜的高效并行join运算算法研究

通过分析ABJ 算法和Hybrid hash join算法,并对两个算法进行了结合和改进,提出了一种能克服各种数据偏斜的并行二元连接运算算法,可在不同的数据偏斜情况下启动不同的模块,克服数据偏斜造成的负载不平衡现象。     

抗数据偏斜的高效并行join运算算法研究

通过分析ABJ＋算法和Hybrid hash join算法,并对两个算法进行了结合和改进,提出了一种能克服各种数据偏斜的并行二元连接运算算法,可在不同的数据偏斜情况下启动不同的模块,克服数据偏斜造成的负载不平衡现象。     

Data Partitioning for Parallel Spatial Join Processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.     

基于并行B+-树的并行Join算法的设计、分析与实现

Ｂ＾＋－树是一种有效的数据库存储结构，被普遍应用于各种关系数据库系统。把Ｂ＾＋－树并行化，使之用于并行数据库系统显然是一项很有意义的重要工作。本文研究了适用于并行数据库的并行Ｂ＾＋－树存储结构，提出两类基于并行Ｂ＾＋－树工并行Ｊｏｉｎ算法。理论和实验结果表明，这些算法效率高基其它并行Ｊｏｉｎ算法。     

Structural Join and Staircase Join Algorithms of Sibling Relationship

The processing of XML queries can result in evaluation of various structural relationships. Efficient algorithms for evaluating ancestor-descendant and parent-child relationships have been proposed. Whereas the problems of evaluating preceding-sibling-following-sibling and preceding-following relationships are still open. In this paper, we studied the structural join and staircase join for sibling relationship. First, the idea of how to filter out and minimize unnecessary reads of elements using parent＇s structural information is introduced, which can be used to accelerate structural joins of parent-child and preceding-sibling-following-sibling relationships. Second, two efficient structural join algorithms of sibling relationship are proposed. These algorithms lead to optimal join performance： nodes that do not participate in the join can be judged beforehand and then skipped using B^＋-tree index. Besides, each element list joined is scanned sequentially once at most. Furthermore, output of join results is sorted in document order. We also discussed the staircase join algorithm for sibling axes. Studies show that, staircase join for sibling axes is close to the structural join for sibling axes and shares the same characteristic of high efficiency. Our experimental results not only demonstrate the effectiveness of our optimizing techniques for sibling axes, but also validate the efficiency of our algorithms. As far as we know, this is the first work addressing this problem specially.     

基于DSVM的并行Hash连接算法及其性能评价

提出了一个基于分布式共享虚拟存储器技术的并行Ｈａｓｈ连接算法,然后设计了一个并行连接算法的测试评价基准,并评价和分析了该算法在均匀情况下３个不同负载的性能比较和Ｚｉｐｆ顺斜数据分布情况下两种度策略的算法性能。同时与其它并行连接算法进行性能比较与分析。     

基于CPU-GPU异构体系结构的并行字符串相似性连接方法

相似性连接技术在数据清洗、数据集成等领域中具有重要意义,近年来引起了学术界的广泛关注.随着数据量的不断增大、数据处理实时性的要求逐渐提高以及处理器性能提升瓶颈的出现,传统的串行相似性连接方法已经不能满足当前大数据处理的需求.近些年,GPU作为协处理器在机器学习等领域取得了良好的加速效果,因此基于GPU的并行算法开始成为解决各类性能问题的有效解决方案.为此,提出了基于CPU-GPU异构体系的并行相似性连接方法.首先,方法使用GPU构建倒排索引,索引采用SoA(struct of arrays)结构,从而解决了传统索引结构在并行模式下读写效率低的问题.其次,针对串行算法的性能问题,提出基于过滤验证框架的并行双重长度过滤算法,其中利用前缀过滤和构建好的倒排索引提升过滤效果.方法中相似度精确计算验证过程使用CPU计算执行,从而充分利用CPU-GPU的异构计算资源.最后,在多个数据集上进行实验验证性能.通过与串行相似性连接算法进行对比,实验结果表明所提出方法相对于已有方法具有更好的过滤效果和更低的索引生成代价,并在相似性连接上具有更好的性能和良好的加速比.     

MapReduce连接查询的I/O代价研究

数据的指数级增长给数据管理和分析带来了严峻的挑战.连接查询是数据分析中一种常用运算,而MapReduce是一种用于大规模数据集并行处理的编程模型,研究基于MapReduce的连接查询代价评估和查询优化,有着学术意义和应用价值.MapReduce连接查询算法的性能主要取决于I/O代价(包括本地和网络I/O),而I/O代价与数据集以及连接运算的特征参数相关,通过对二元连接的I/O代价评估可以优化多元连接执行计划.基于此,首先提出了二元连接查询的I/O代价模型;随后,对现有二元连接算法进行形式化定义和简单扩展,归纳出6种基于MapReduce连接查询算法,并通过算法白盒分析定义它们的I/O代价函数;最后,提出一种多元连接最优执行计划的选择算法.通过实验表明I/O代价模型的正确性且能够准确地反映算法的性能优劣.