摘要:
全书分为11章,内容包括:数据库简介、关系数据库系统、数据库语言、创建与管理数据库、模式与表的管理、关系代数、查询、数据管理、视图、索引、数据库安全性。本书不仅介绍数据库的来源与历史、基本定义与体系结构、数学概念与理论,还介绍实际操作语法说明、案例与注意事项;既有理论基础,又有实际操作演示,并安排了实验教程。本书中的案例操作演示内容都已在SQL Server 2019版本上测试通过。
Wan, Chen Li, Wenzhong Ding, Wangxiang Zhang, Zhijie Lu, Qingning Qian, Lin Xu, Ji Lu, Jixiang Cao, Rongrong Ye, Baoliu Lu, Sanglu
Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R ChinaData Fdn Co Nanjing 210023 Peoples R ChinaNARI Grp Corp State Key Lab Smart Grid Protect & Control Nanjing 211106 Peoples R China
摘要:
Predicting future performance curve and mining the top-K influential KPIs are two important tasks for Database Management System (DBMS) operations. In this paper, we propose a multi-task sequence learning approach to address the two tasks in a uniform framework. The proposed approach adopts a Long Short-Term Memory (LSTM) based deep neural network model that uses multilevel discrete wavelets transform and LSTM-based Seq2Seq forecaster to capture the features in both time and frequency domains from high dimensional time series, and achieves multi-step performance prediction and top-K KPI mining concurrently. The performance of the proposed multi-task sequence learning approach is evaluated based on two real-world DBMS datasets, which shows that the proposed approach achieves the lowest mean absolute error and root mean squared error in predicting performance scores, and significantly outperforms the state-of-the-art algorithms in both learning tasks. (c) 2021 Elsevier Inc. All rights reserved.