CAJ | 학술논문

提出了星型模型扁平化编码方法上的百分点聚集函数的并行算法.把星型模型中维表上与查询相关的维度层次信息编码到事实表里,该编码方法使得经过改写的聚集查询,在查询处理过程中无需进行事实表和维表之间的连接,数据可以均匀分布到机群上,利用并行处理提高查询性能.百分点计算不具有天然的并行性,提出了基于采样预测的并行迭代式算法,通过付出采样数据的网络传输开销,使得算法快速收敛,解决了大规模机群上的百分点聚集函数计算的性能问题.实验结果证实,该算法不仅能快速收敛,而且其网络传输开销也是可以接受的.
제출료성형모형편평화편마방법상적백분점취집함수적병행산법.파성형모형중유표상여사순상관적유도층차신식편마도사실표리,해편마방법사득경과개사적취집사순,재사순처리과정중무수진행사실표화유표지간적련접,수거가이균균분포도궤군상,이용병행처리제고사순성능.백분점계산불구유천연적병행성,제출료기우채양예측적병행질대식산법,통과부출채양수거적망락전수개소,사득산법쾌속수렴,해결료대규모궤군상적백분점취집함수계산적성능문제.실험결과증실,해산법불부능쾌속수렴,이차기망락전수개소야시가이접수적.
@@@@This paper proposes a parallel percentile computing algorithm over flattening encoded star schema. Firstly hierarchies of all dimensions pertaining to queries in a star schema are encoded into the fact table, the encoding scheme enables processing queries without joining between the fact table and dimension tables after query rewriting, thus data can be partitioned onto a large cluster evenly, and high performance is achieved through parallel processing. Percentile computing is not naturally parallelizable, this paper also puts forward an iterative parallel algorithm based on prediction. Through paying out the cost of data sampling, the algorithm converges quickly, and resolves the problem of computing percentile on a large scale cluster. Experiment results demonstrate that not only the algorithm converges quickly, but also the network overhead is acceptable.