Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a dist...Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a distributed replica of a dataset exists.The aim is to partition the query payload(and its range) into subsets and distribute those to the replica nodes in a way that minimizes a client's response time.However,since query size and distribution characteristics of data(data dense/sparse regions) in varying ranges are not known a priori,performing efficient load balancing and parallel processing over the unpredictable workload is difficult.A technique based on the creation and manipulation of dynamic spatial indexes for query payload estimation in distributed queries was proposed.The effectiveness of this technique was demonstrated on queries for analysis of archived earthquake-generated seismic data records.展开更多
针对传统的数据库管理系统无法很好地学习谓词之间的交互以及无法准确地估计复杂查询的基数问题,提出了一种树形结构的长短期记忆神经网络(Tree Long Short Term Memory, TreeLSTM)模型建模查询,并使用该模型对新的查询基数进行估计.所...针对传统的数据库管理系统无法很好地学习谓词之间的交互以及无法准确地估计复杂查询的基数问题,提出了一种树形结构的长短期记忆神经网络(Tree Long Short Term Memory, TreeLSTM)模型建模查询,并使用该模型对新的查询基数进行估计.所提出的模型考虑了查询语句中包含的合取和析取运算,根据谓词之间的操作符类型将子表达式构建为树形结构,根据组合子表达式向量来表示连续向量空间中的任意逻辑表达式.TreeLSTM模型通过捕捉查询谓词之间的顺序依赖关系从而提升基数估计的性能和准确度,将TreeLSTM与基于直方图方法、基于学习的MSCN和TreeRNN方法进行了比较.实验结果表明:TreeLSTM的估算误差比直方图、MSCN、TreeRNN方法的误差分别降低了60.41%,33.33%和11.57%,该方法显著提高了基数估计器的性能.展开更多
文摘Replication is an approach often used to speed up the execution of queries submitted to a large dataset.A compile-time/run-time approach is presented for minimizing the response time of 2-dimensional range when a distributed replica of a dataset exists.The aim is to partition the query payload(and its range) into subsets and distribute those to the replica nodes in a way that minimizes a client's response time.However,since query size and distribution characteristics of data(data dense/sparse regions) in varying ranges are not known a priori,performing efficient load balancing and parallel processing over the unpredictable workload is difficult.A technique based on the creation and manipulation of dynamic spatial indexes for query payload estimation in distributed queries was proposed.The effectiveness of this technique was demonstrated on queries for analysis of archived earthquake-generated seismic data records.
文摘针对传统的数据库管理系统无法很好地学习谓词之间的交互以及无法准确地估计复杂查询的基数问题,提出了一种树形结构的长短期记忆神经网络(Tree Long Short Term Memory, TreeLSTM)模型建模查询,并使用该模型对新的查询基数进行估计.所提出的模型考虑了查询语句中包含的合取和析取运算,根据谓词之间的操作符类型将子表达式构建为树形结构,根据组合子表达式向量来表示连续向量空间中的任意逻辑表达式.TreeLSTM模型通过捕捉查询谓词之间的顺序依赖关系从而提升基数估计的性能和准确度,将TreeLSTM与基于直方图方法、基于学习的MSCN和TreeRNN方法进行了比较.实验结果表明:TreeLSTM的估算误差比直方图、MSCN、TreeRNN方法的误差分别降低了60.41%,33.33%和11.57%,该方法显著提高了基数估计器的性能.
