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华中科技大学2009级大机械类课程《数据库技术》考试试卷（B卷）2011年9月姓名学号院系班级说明：1. 本试卷共四道大题，满分100分；2. 闭卷考试，考试时间为150分钟。
题号一二三四总分12345678分数一、选择题每题只有一个正确答案。
（10×2）1．若关系中的某一属性组的值能唯一地标识一个元组，则称该属性组为________。
A.候选码B.关系C.基本属性D.外码2．在人工管理阶段，数据是_____。
A.有结构的B.无结构的C.整体无结构，记录有结构D.整体结构化的3．在文件系统阶段，数据_____。
A.无独立性B.独立性差C.具有物理独立性D.具有逻辑独立性4．数据库系统阶段，数据_____。
A.具有物理独立性，没有逻辑独立性B.具有物理独立性和逻辑独立性C.独立性差D.没有物理独立性，有逻辑独立性5．数据管理技术经历了人工管理，_____和_____。
(1)DBMS (2)文件系统 (3)网状系统 (4)数据库系统 (5)关系系统A.(3)和(5)B.(2)和(3)C.(1)和(4)D.(2)和(4)6．数据的完整性包括实体完整性和________。
A.实体完整性B.参照完整性C.函数依赖完整性D.全局完整性7.在SQL语言中数据插入的操作是通过________语句实现的。
A.CREATEB.REVOKEC.GRANTD.INSERT8．1NF________规范为2NF。
A.消除非主属性对码的部分函数依赖B.消除非主属性对码的传递函数依赖C.消除主属性对码的部分和传递函数依赖D.消除非平凡且非函数依赖的多值依赖9．2NF________规范为3NF。
A.消除非主属性对码的部分函数依赖B.消除非主属性对码的传递函数依赖C.消除主属性对码的部分和传递函数依赖D.消除非平凡且非函数依赖的多值依赖10．一个m:n联系转换为一个关系模式后，其关系的码为________。
A.m端和n端实体的码的组合 B.m端实体的码 C.n端实体的码 D.实体的码二、简答题。
2022年华中科技大学计算机科学与技术专业《数据库原理》科目期末试卷B（有答案）一、填空题1、DBMS的完整性控制机制应具备三个功能：定义功能，即______；检查功能，即______；最后若发现用户的操作请求使数据违背了完整性约束条件，则采取一定的动作来保证数据的完整性。
2、主题在数据仓库中由一系列实现。
一个主题之下表的划分可按______、______数据所属时间段进行划分，主题在数据仓库中可用______方式进行存储，如果主题存储量大，为了提高处理效率可采用______方式进行存储。
3、在设计局部E-R图时，由于各个子系统分别有不同的应用，而且往往是由不同的设计人员设计，所以各个局部E-R图之间难免有不一致的地方，称为冲突。
这些冲突主要有______、______和______3类。
4、使某个事务永远处于等待状态，得不到执行的现象称为______。
有两个或两个以上的事务处于等待状态，每个事务都在等待其中另一个事务解除封锁，它才能继续下去，结果任何一个事务都无法执行，这种现象称为______。
5、在RDBMS中，通过某种代价模型计算各种查询的执行代价。
在集中式数据库中，查询的执行开销主要包括______和______代价。
在多用户数据库中，还应考虑查询的内存代价开销。
6、设某数据库中有商品表（商品号，商品名，商品类别，价格）。
现要创建一个视图，该视图包含全部商品类别及每类商品的平均价格。
请补全如下语句： CREATE VIEW V1（商品类别，平均价格）AS SELECT商品类别，_____FROM商品表GROUP BY商品类别；7、在SQL语言中，为了数据库的安全性，设置了对数据的存取进行控制的语句，对用户授权使用____________语句，收回所授的权限使用____________语句。
8、在一个关系R中，若每个数据项都是不可再分割的，那么R一定属于______。
9、设在SQL Server 2000环境下，对“销售数据库”进行的备份操作序列如下图所示。
2022年华中科技大学数据科学与大数据技术专业《计算机网络》科目期末试卷B（有答案）一、选择题1、以下说法错误的是（）。
I..广播式网络一般只包含3层，即物理层、数据链路层和网络层II.Intermet的核心协议是TCP/IPIII.在lnternet中，网络层的服务访问点是端口号A. I、II、IIIB. IIIC. I、ⅢD. I、II2、在OS1参考模型中，下列功能需由应用层的相邻层实现的是（）。
A.对话管理B.数据格式转换C.路由选择D.可靠数据传输3、动态路山选择和静态路由选择的主要区别是（）。
A.动态路由选择需要维护整个网络的拓扑结构信息，而静态路由选择只需要维护有限的拓扑结构信息B.动态路由选择需要使用路由选择协议去发现和维护路由信息，而静态路由选择只需要手动配置路由信息C.动态路由选择的可扩展性要大大优于静态路由选择，因为在网络拓扑结构发生了变化时，路由选择不需要手动配置去通知路由器D.动态路由选择使用路由表，而静态路由选择不使用路由表4、在IP首部的字段中，与分片和重组无关的是（）。
注：假设现在已经分片完成。
A.总长度B.标识C.标志D.片偏移5、下列关于令牌环网络的描述中，错误的是（）。
A.令牌环网络存在冲突B.同一时刻，环上只有一个数据在传输C.网上所有结点共享网络带宽D.数据从一个结点到另一个结点的时间可以计算6、以太网交换机是按照（）进行转发的。
A.MAC地址B.IP地址C.协议类型D.端口号7、在TCP中，采用（）来区分不同的应用进程。
A.端口号B.IP地址C.协议类型D.MAC地址8、TCP的通信双方，有一方发送了带有FIN标志位的数据段后表示（）。
A.将断开通信双方的TCP连接B.单方面释放连接，表示本方已经无数据发送，但是可以接收对方的数据C.终止数据发送，双方都不能发送数据D.连接被重新建立9、UDP报文头部不包括（）。
A.目的地址B.源UDP端口C.目的UDP端口D.报文长度10、域名与（）是一一对应的。
可编辑修改精选全文完整版数据库题库及答案数据库技术是计算机科学中重要的一部分，广泛应用于各个领域。
为了更好地学习和理解数据库知识，题库是一个非常有用的资源。
本文提供一些常见的数据库题目及其答案，希望对读者的学习和实践有所帮助。
一、选择题1. 在关系数据库中，用于描述记录之间的关系的是：a) 表格b) 行c) 列d) 键答案：a) 表格2. 数据库管理系统(DBMS)的主要功能包括：a) 数据的存储和管理b) 数据的查询和分析c) 数据的备份和恢复d) 数据的安全性控制答案：a) 数据的存储和管理、b) 数据的查询和分析、c) 数据的备份和恢复、d) 数据的安全性控制3. 关系数据库中，用于唯一标识一条记录的属性被称为：a) 主键b) 外键c) 候选键d) 索引答案：a) 主键4. 数据库的范式用于描述数据库的结构是否符合某种规范。
以下哪一项不是数据库的范式：a) 一范式b) 二范式c) 三范式d) 四范式答案：d) 四范式5. 在SQL语句中，用于插入新记录的关键字是：a) SELECTb) UPDATEc) INSERTd) DELETE答案：c) INSERT二、填空题1. 在关系数据库中，每一行都代表一个________。
答案：记录2. 数据库中具有相同属性的记录集合被称为____________。
答案：表3. 在关系数据库中，用于检索某一部分记录的语句是________。
答案：SELECT4. 数据库设计中常用的三个范式依次是一范式、二范式和__________。
答案：三范式5. 数据库表中，用于唯一标识一条记录的属性被称为________。
答案：主键三、应用题1. 请列举至少三种数据库管理系统(DBMS)的类型，并简要描述它们的特点。
答案：关系型数据库管理系统(RDBMS)：采用表格的形式组织数据，具有高度结构化和强一致性的特点。
常见的关系型数据库管理系统有MySQL、Oracle、SQL Server等。
2022年华中科技大学软件工程专业《计算机网络》科目期末试卷B（有答案）一、选择题1、计算机网络可分为通信子网和资源子网。
下列属于通信子网的是（）。
I.网桥 II.交换机 III.计算机软件 IV.路由器A. I、II、ⅣB. II、III.、ⅣC. I、Ⅲ、ⅣD. I、Ⅱ、Ⅲ2、图所示网络中，假设所有域名服务器均采用选代查询方式进行域名解析。
当H4访问规范域名某个的网站时，域名服务器201.1.1.1在完成该域名解析过程中，可能发出DNS查询的最少和最多次数分别是（）。
A.0，3B.1，3C.0，4D.1，43、一个主机移动到了另一个局域网中，如果一个分组到达了它原来所在的局域网中，分组会被转发给（）。
A.移动IP的本地代理B.移动IP的外部代理C.主机D.丢弃4、IPv6的地址长度为（）位。
A.32B.64C.128D.2565、对于使用交换机连接起来的10Mbit/s的共享式以太网，若有10个用户，则每个用户能够占有的带宽为（）。
A.1 Mbit/sB.2Mbit/sC.10Mbit/sD.100Mbit/s6、CSMA协议可以利用多种监听算法来减小发送冲突的概率，下列关于各种监听算法的描述中，正确的是（）。
A.非坚持型监听算法有利于减少网络空闲时间B.1-坚持型监听算法有利于减少冲突的概率C.P-坚持型监听算法无法减少网络的空闲时间D.1-坚持型监听算法能够及时抢占信道7、下列网络应用中，（）不适合使用UDP。
A.客户/服务器领域B.远程调用C.实时多媒体应用D.远程登录8、假设某应用程序每秒产生一个60B的数据块，每个数据块被封装在一个TCP报文中，然后再封装到一个IP数据报中，那么最后每个数据报所含有的应用数据所占的百分比是（）（注意：TCP报文和IP数据报的首部没有附加字段）。
A.20%B.40%C.60%D.80%9、下列关于因特网中的主机和路由器的说法，错误的是（）。
A.主机通常需要实现IPB.路由器必须实现TCPC.主机通常需要实现TCPD.路由器必须实现IP10、图所示的曼彻斯特编码表示的比特串为（）A.011001B.100110C.111110D.01111011、下面有关DNS的说法中正确的是（）。
数据库课程实验操作指导（修订版）华中科技大学计算机科学与技术学院数据库系统课程教学组二OO七年三月目 录一．概述1．SQL Server 2000 组成2．SQL Server 2000的安装3．SQL Server 2000 的工具二．DDL的使用方法1．数据库建立2．基本表的建立3．视图的建立三．DML的使用方法1．INSERT命令2．DELETE 命令3．UPDATE命令四．SELECT命令使用方法五．DCL的使用方法1．SQL Server 登录管理2．用户管理3．授权用户（GRANT、REVOKE）六．游标的使用1．游标的定义2．游标的操作七．数据库的备份和恢复八．实验练习1．基本表的创建、数据插入2．数据查询3．数据修改、删除4．视图的操作5．库函数，授权控制6．数据库备份、恢复九．数据库课程设计基本要求一． 概述1．SQL Server 2000 组成基于SQL和客户/服务器体系结构的关系数据库管理系统服务器软件包，是由Microsoft 公司推出的SQL Server数据库管理系统的最新版本。
从图1 SQL Server的体系结构示意图中看出，SQL Server 2000由4部分组成，在实验中，我们要求掌握基于SQL Server 2000的服务器的使用，也就是数据库管理员Array DBA的主要操作。
（注：本文所有内容均在SQLServer 2000上实现，读者也可在SQL Server 2005上得到类似结果。
）2．SQL Server 2000的安装SQL Server 2000的常见版本有：企业版、标准版、个人版、开发人员版等。
对软硬件的最低需求为：CPU Pentium 166MHz，内存64MB，硬盘180 MB。
SQL Server 2000企业版和标准版只能在windows2000 Server版和Professional版操作系统下运行。
下面介绍SQL Server 2000 企业版在本地机上的安装过程。
计算方法华中科技大学数学系教材张诚坚, 高健, 何南忠. 计算方法. 北京：高等教育出版社,1999年参考书¾李庆扬, 易大义, 王能超. 现代数值分析, 北京：高等教育出版社¾Richard L. Burden & J. Douglas Faires .Numerical Analysis(Seventh Edition), 北京：高等教育出版社, 2001¾徐士良．C常用算法程序集(第二版)．北京：清华大学出版社，1996期末考试试题期末考试的试卷有填空题和解答题。
解答题共7个题，分数约占70％。
期末考试主要考核：基本概念；基本原理；基本运算。
必须带简易计算器。
总成绩=平时成绩*20%+期末成绩*80%§1绪论第1节数值算法概论第2节预备知识与误差第1节数值算法概论1. 引言数值计算已经是计算机处理实际问题的一种关键手段。
它使各科学领域从定性分析阶段走向定量分析阶段，从粗糙走向精密。
2. 计算机数值方法的研究对象与特点计算问题x I n∫+ =15dxxx n 11nx I dx =∫011615 , ln5n n n n I I I I −==−1615 , ln I I I I ==−误差的传播与积累丽的北京就刮起台风来了？！3 数值算法计算方法的主要任务:1.将计算机上不能执行的运算化为在计算机上可执行的运算2.针对所求解的数值问题研究在计算机上可执行的且有效的计算公式3.因为可能采用了近似等价运算,故要进行误差分析,即数值问题的性态及数值方法的稳定性数值算法是指有步骤地完成解数值问题的过程.数值算法有四个特点:1.目的明确算法必须有明确的目的,其条件和结论均应有清楚的规定2.定义精确对算法的每一步都必须有精确的定义3.算法可执行算法中的每一步操作都是可执行的4.步骤有限算法必须在有限步内能够完成解题过程例如给出等差数列1,2,3,…,10000的求和算法算法构造如下:N取记数器置零=S.1=,0⇒+,.21+N⇒SNNS.3<N10000若2,，否则转.4输出SN,一、误差的种类及来源1模型误差在建立数学模型过程中,要将复杂的现象抽象归结为数学模型,往往要忽略一些次要因素的影响,而对问题作一些简化,因此和实际问题有一定的区别.2观测误差在建模和具体运算过程中所用的数据往往是通过观察和测量得到的,由于精度的限制,这些数据一般是近似的,即有误差.3截断误差由于计算机只能完成有限次算术运算和逻辑运算,因此要将有些需用极限或无穷过程进行的运算有限化,对无穷过程进行截断,这就带来误差.第2节预备知识与误差在数值计算过程中还会遇到无穷小数,因误差与有效数字有效数字用科学计数法，记(其中）若（即的截取按四舍五入规则），则称为有n 位有效数字，精确到。
课程实验报告课程名称：数据库系统概论专业班级：学号：姓名：指导教师：报告日期：计算机科学与技术学院目录目录 (3)一、实验目的 (5)二、实验原理 (5)1.SQL Server 2008查询分析器 (5)2.DDL使用方法 (5)3.DML使用方法 (6)4.DCL 的使用方法 (7)5.数据库的备份和恢复 (8)三、实验内容 (9)实验1: 基本表的创建、数据插入 (9)实验2：数据查询 (10)实验3：数据修改、删除 (10)实验4：视图的操作 (10)实验5：库函数，授权控制 (10)实验6：数据库的备份、恢复 (11)四、实验过程 (11)实验1: 基本表的创建、数据插入 (11)实验2: 数据查询 (14)实验3: 数据修改、删除 (16)实验4：视图的操作 (18)实验5：库函数，授权控制 (19)实验6：数据库的备份、恢复 (20)五、心得体会 (21)一、实验目的掌握SQL Server 2008的工具使用掌握DDL的使用方法掌握DML的使用方法掌握SELECT命令使用方法掌握DCL的使用方法掌握数据库的备份和恢复二、实验原理1.SQL Server 2008查询分析器查询分析器是一个重要工具，实验中的所有SQL语言命令均需在查询分析器中输入、编辑运行。
2.DDL使用方法1）数据库创建在查询分析器中执行下列语句即可在默认的设备上创建新的数据库ems。
CREATE DATABASE database_name2）基本表的建立创建基本表的命令为：CREATE TABLE table_name，在该命令中定义主码和外码时，可以使用列约束（Column Constraint）或表约束（Table Constraint）子句。
创建基本表时，应先选择包含表的数据库。
3）视图的建立视图是组成数据库体系结构——三级模式两级映像结构中的外模式的基本单元，SQL-Server的视图定义命令为：CREATE VIEW view-name AS SELECT statement视图是用于定义终端用户数据来源的。
数据库系统原理_华中科技大学中国大学mooc课后章节答案期末考试题库2023年1.数据库三级模式中，用户与数据库系统的接口是( )答案:外模式2.数据库领域三大经典数据模型是（）答案:网状模型、层次模型及关系模型3.关系代数的五种基本运算是（）答案:并、差、笛卡尔积、选择、投影4.在关系代数中，自然联接是由（）组合而成的答案:投影、选择和笛卡尔积5.设有如下关系：职工关系EMP( E# ,ENAME,AGE,SEX)，E#表示职工号，ENAME表示职工名，AGE表示职工年龄，SEX表示职工性别。
工作关系WORKS( E#,C#,SALARY) SALARY表示职工工资。
公司关系COMP(C#,CNAME,CITY) C#表示公司号，CNAME 表示公司名。
设工号为E6的职工在多个公司工作。
查询：至少在E6职工兼职的所有公司工作的职工工号。
下面关系代数表达式正确的是（）答案:6.下列关系运算中花费时间可能最长的运算是（）答案:笛卡尔积7.下列关于SQL语言的说法正确的是（）答案:SQL是一种非过程化语言，无需了解存取路径8.以下关于模式与视图的关系，描述不正确的是（）答案:如果建表时不定义表所属的模式，该表将不属于任何模式9.进行自然联结运算的两个关系必须具有（）答案:公共属性组10.下列选项中与其它三个不属于同一种数据库保护机制的是（）答案:级联删除11.授权定义经过编译后存储在（）中答案:数据库12.若要允许将角色转授给其他用户，则相应的SQL授权语句中应包含的短语是（）答案:WITH ADMIN OPTION13.数据库的强制存取控制机制禁止高许可证级别的用户更新低密级的数据对象是为了（）答案:防止敏感信息的泄露14.下列选项属于数据完整性范畴的是（）答案:数据相容性15.实体完整性的违约处理为（）答案:拒绝执行16.关系的某个属性若有UNIQUE约束，则表示（）答案:该属性的非空值不允许重复17.假设在某关系数据库中，选课表的外码“课号”参照课程关系的主码“课号”，且在创建该外码时包含了ON UPDATE CASCADE子句，则该子句意味着（）答案:修改某门课程的课号会连带修改相关的选课记录中的课号18.假设在Student表上创建了一个AFTER UPDATE的行级触发器，若该表有1000条记录，执行语句：UPDATE Student SET Sno=Sno+10000; 则将执行触发动作次数为（）答案:100019.已知关系模式R的属性全集U={X，Y，Z}，且XY和YZ为R的候选码，则以下说法错误的是（）答案:X→Z一定不成立20.已知关系模式R(XYZ)的函数依赖集F={Y→Z,Y→X,X→YZ }，则在下列选项中，该关系满足的范式最高可达到（）答案:BCNF21.以下关系模式中属于BCNF的是（）答案:R(X,Y,Z) F={XY→Z}22.已知关系模式R(ABCD)的函数依赖集F={A→BC,C→B,C→D}，则下列选项中，不被F逻辑蕴涵的是（）答案:BC→AD23.答案:ABCD24.已知关系模式R(ABCD)的函数依赖集F={AB→C,BC→D,BD→A}，则在以下选项中，R的候选码是（）答案:BC25.已知关系模式R(ABCD)的函数依赖集F={A→BC,B→CD,C→AD}，则在下列选项中，属于F的最小函数依赖集的是（）答案:{A→B, B→C, C→A, C→D}26.已知关系模式R(U，F)，其中U={A，B，C，D，E，F}，F={AB→C，D→A，CD→E}，现要将R分解为若干个具有依赖保持性和无损连接性的3NF，以下选项正确的是（）答案:{ABC, AD, BD, CDE}27.在数据库设计中，关系规范化这一步骤属于（）答案:逻辑设计阶段28.将以下E-R图转换成关系模式并进行适当的消解后，生成的关系模式中外码个数是（）答案:2个29.现要设计一个高考志愿填报数据库，假设有如下语义：每个高校开设若干专业，不同高校可开设相同专业，每个考生可平行填报多个报考志愿，每个志愿需明确说明要报考哪个高校的哪个专业。
数据库大作业-教务管理系统—华中科技大学(总34页)本页仅作为文档封面，使用时可以删除This document is for reference only-rar21year.March第1章绪言 ..................................................................................................... 错误!未定义书签。
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2022年华中科技大学数据科学与大数据技术专业《计算机系统结构》科目期末试卷B(有答案）一、选择题1、最能确保提高虚拟存贮器访主存的命中率的改进途径是( )A.增大辅存容量B.采用FIFO替换算法并增大页面C.改用LRU替换算法并增大页面D.改用LRU替换算法并增大页面数2、计算机系统多级层次中，从下层到上层，各级相对顺序正确的应当是（）。
A.汇编语言机器级，操作系统机器级，高级语言机器级B.微程序机器级，传统机器语言机器级，汇编语言机器级C.传统机器语言机器级，高级语言机器级，汇编语言机器级D.汇编语言机器级，应用语言机器级，高级语言机器级3、下列关于虚拟存贮器的说法，比较正确的应当是( )A.访主存命中率随页面大小增大而提高B.访主存命中率随主存容量增加而提高C.更换替换算法能提高命中率D.在主存命中率低时，改用堆栈型替换算法，并增大主存容量，可提高命中率4、"从中间开始"设计的"中间"目前多数是在( )。
A.传统机器语言级与操作系统机器级之间B.传统机器语言级与微程序机器级之间C.微程序机器级与汇编语言机器级之间D.操作系统机器级与汇编语言机器级之间5、非线性流水线是指( )A.一次运算中使用流水线中的多个功能段B.一次运算中要多次使用流水线中的某些功能段C.流水线中某些功能段在各次运算中的作用不同D.流水线的各个功能段在各种运算中有不同的组合6、块冲突概率最高的Cache地址映象方式是( )A.段相联B.组相联C.直接D.全相联7、对系统程序员不透明的应当是（）A.CACHE 存储器B.系列机各档不同的数据通路宽度C.指令缓冲寄存器D.虚拟存储器8、"一次重叠"中消除"指令相关"最好的方法是( )。
A.不准修改指令B.设相关专用通路C.推后分析下条指令D.推后执行下条指令9、除了分布处理、MPP和机群系统外，并行处理计算机按其基本结构特征可分为流水线计算机，阵列处理机，多处理机和（）四种不同的结构。
Harvard Brain Tissue Resource CenterNational Brain DatabankNeuroscience Gene Expression RepositoryResearch on Standards and PlatformsWorking Technical ReportAugust 11, 2003Project LeadNitin Sawhney, Ph.D.Technical DevelopmentTom Hickerson, Shai Sachs, Dmitriy AndreyevAbstractThe Harvard Brain Tissue Resource Center (or The Brainbank) at the McLean Hospital is one of three federally funded centers for the collection and distribution of human brain specimens for research, and the only designated acquisition center. The Brainbank seeks to establish a publicly accessible repository (The National Brain Databank) to collect and disseminate results of postmortem studies of neurological and psychiatric disorders. The National Brain Databank will primarily provide neuropathology information including gene expression data, which will be accessed and queried using a web-based interface. The project will utilize key microarray metadata standards such as MAIME and MAGE-ML and best practices employed by existing gene expression repositories like NIH’s Gene Expression Omnibus (GEO) and ArrayExpress at the European Bioinformatics Institute.The National Brain Databank initiative requires a long term perspective to develop an appropriate application platform with a scaleable and robust database while incorporating suitable microarray standards and ontologies. In this technical paper, we survey the overall lifecycle of research at the Brainbank with respect to the microarray experiments. We also review the main gene expression repositories and analytic tools as well as the emerging MAIME and MAGE-ML standards being adopted by the research community.We propose a system architecture that allows integration of existing Affymetrix-based microarray data using the MAGE object model and interfaces, while retaining the data in its raw form. We believe the proposed repository will benefit from an architecture using the Java J2EE application framework and the Oracle 9i relational database running on a secure and high-performance Linux-based server. This architecture enables an open, scaleable and extensible approach towards development and deployment of the repository in conjunction with existing software tools and standards in academic settings. We believe that the basic framework outlined in this technical report should serve as a robust foundation for the evolving gene expression repository at the Brainbank.Table of ContentsKey Recommendations (3)1Introduction: Objectives of the National Brain Databank (4)2Lifecycle of Research at the Brainbank (5)2.1Acquisition and Curation of Brain Tissue Samples (5)2.2Gene Expression Experiments using Microarrays (5)2.3Analysis of Expression Data: Software Tools and Data Standards (7)2.4Current Computing Infrastructure and Databases at the Brainbank (8)2.5Basic Requirements for National Brain Databank (9)3Public Gene Expression Repositories (12)4Microarray Standards and Ontologies (13)4.1Motivation for Microarray Standards (13)4.2What is an Ontology? (14)4.3Understanding the Role of MIAME (14)4.4Understanding MAGE-OM and MAGE-ML (15)4.5Software Support for MAGE (16)4.5.1Affymetrix GDAC Exporter (16)4.5.2MGED’s MAGE-stk (16)4.5.3Commercial Software: Rosetta Resolver (16)4.6Data Formats used by Gene Expression Repositories (16)4.6.1SOFT Format at GEO (16)4.6.2MAGE Standards at ArrayExpress (17)4.6.3GeneXML at GeneX (17)4.7Historical Evolution of MAGE Standards (17)4.8Proposed Use of MIAME/MAGE and Related Technologies (18)4.8.1National Brain Databank Database Structure (18)4.8.2Importing Experimental Data (18)4.8.3Curating the Brainbank Data (19)4.8.4Searching the Data (20)4.8.5Browsing the Data (20)4.8.6Exporting the Data (20)5National Brain Databank: Proposed Model and Approach (21)5.1Summary of Preliminary Requirements (21)5.2Proposed Application Model and System Architecture (22)5.3Designing the Application Platform: Adopting Java J2EE (24)5.3.1What is J2EE? (24)5.3.2Case Study: PhenoDB Project at Massachusetts General Hospital (25)5.3.3Available Java Tools and Comparison with Other Languages (26)5.4Adopting a UNIX Operating Environment for the National Brain Databank Server (28)5.5Adopting a Relational Database: Comparison of Database Platforms (29)6Summary of Ongoing Requirements Analysis (32)7Conclusions (33)References (34)Appendix: Comparison of Databases and Security IssuesKey RecommendationsØTo support the large volume of heterogeneous data generated from microarray experiments at the Brainbank, the system must provide a range a mechanisms for indexing, annotating and linking thedatasets with clinical and diagnostic data on the brain tissue samples. Hence, use of standardizedapproaches such as the MAIME ontology is important along with a robust and scaleable database.ØTo ensure standardized submission, export and exchange with other gene expression repositories, the system should support the MAGE-OM object model and the XML-based MAGE-ML data exchangestandards. These standards are increasingly being adopted by many databases and software tools.ØWhile the MAGE standards are becoming popular, many existing databases and analytic tools are only now beginning to adapt to these standards. Hence, for the foreseeable future the Brainbank mustcontinue to provide gene expression data in their native formats to enable analysis by current software.The system must export data using MAGE while providing access to raw data files stored in the server.ØTo maintain the high standards for archiving and disseminating data to the neuroscience community, the Brainbank must carefully curate data submitted from internal experiments and external investigators.Hence software tools and workflows should be provided to annotate, validate, cross-reference, and map data to the internal representation. These data submission and curation mechanisms should be MAIME compliant and can be adapted form existing software tools.ØSimilar to existing gene expression repositories, the National Brain Databank must provide adequate tools for querying the diagnostic and gene expression data along a number of searchable parameters.This requires that the experimental data be submitted using MAIME compliant processes as well asindexing the raw data and clinical reports to extract relevant keywords and terms for extensive queries.ØTo allow data to be usable it must be referenced to standardized Gene sequences in GenBank and linked to relevant publications in online resources such as PubMed. The system must support mechanisms to cross-link and reference these online sources using a combination of manual and automated methods.ØSince the brain samples collected and gene expression data generated are based on patient profiles and the online repository is designed to be a publicly accessible resource, data must be selectivelydisseminated to comply with HIPAA guidelines. Hence, the system should support user authenticationmechanisms, a range of user roles and privileges for certain datasets and files, while enforcing adequate security measures in a robust and secure database.ØExtracting and archiving gene expression data in the online repository requires acquiring data from specialized software like Affymetrix using export tools like GDAC and other utilities for converting content to MAIME and MAGE-ML-based formats. The system must support extensible interfaces and APIs toallow integration with such tools. It is important to use nonproprietary platforms, open standards andmethodologies in the design of the system architecture.ØThe deployment architecture for the National Brain Databank must ensure long term scalability, robustness, performance, extensibility and interoperability with other systems and platforms. We proposea system architecture using the Java J2EE application framework and the Oracle 9i relational databaserunning on a secure and high-performance Linux-based server. We believe this architecture provides the most secure and extensible foundation in the long term for deploying a public gene expression repository.1 Introduction: Objectives of the National Brain DatabankThe Harvard Brain Tissue Resource Center1 (or The Brainbank) directed by Dr. Francine M. Benes at the McLean Hospital is one of three federally funded centers for the collection and distribution of human brain specimens for research, and the only designated acquisition center. The center’s brain tissue provides a critical resource for scientists worldwide to assist in their investigations into the functioning of the nervous system and the evolution of many psychiatric diseases.The Brainbank seeks to establish a publicly accessible repository (The National Brain Databank) to collect and disseminate results of postmortem studies of neurological and psychiatric disorders. For this project, Akaza Research2 has been contracted to conduct research, design and development of the public gene expression repository for the Brainbank’s National Brain Databank. Akaza Research is an informatics consulting firm based in Cambridge, MA that provides its academic and nonprofit clients with open and customized solutions to facilitate public research in the life sciences. The National Brain Databank will primarily provide neuropathology information including gene expression data along with anonymous demographics, which will be accessed and queried using a web-based interface. While general information will be publicly available, authorized researchers will have access to detailed results and export data into relevant standardized formats. As the system evolves, distributed researchers will have the ability to upload their own results using a specified metadata format, pending a process of approval and curation from administrators at the National Brain Databank.The project will utilize key microarray metadata standards such as MAIME and MAGE-ML3 and best practices employed by existing gene expression repositories like NIH’s Gene Expression Omnibus (GEO)4 and ArrayExpress at the European Bioinformatics Institute. Akaza is conducting requirements analysis to identity the core specifications of the system over several phases of software releases that address the near-term needs and long term vision of the National Brain Databank. This research and analysis effort conducted in conjunction with the Brainbank will be distilled into technical papers (such as this one) and formal specifications. Based on feedback from the Brainbank, Akaza will commence on the design and development of the system’s first release which will include a project website, implementing the new database schema and data migration from existing MS Access and MS SQL Server databases at the Brainbank, as well as the deployment of the core Java J2EE based web-application framework for the online repository.A key aspect of the National Brain Databank project includes specification and design of appropriate metadata formats and related import/export mechanisms. In addition, several workflow processes will be implemented to provide administrators with mechanisms for selective authorization of users, data import/depositing andcuration/administration of the repository. The Brainbank eventually intends to support the neuroscience research community by expanding the scope of neuropathology information available to include SNP and proteomics data, while providing additional online tools for advanced search and cross-indexing, and supporting the ability to exchange relevant data with other online repositories. As the system is deployed, Akaza will continue to conduct ongoing evaluation, documentation, training and testing with lead users and administrators for iterative refinement of the system to ensure a useful and robust repository for the neuroscience research community.This working technical paper, based on preliminary requirements gathering and background research, summarizes the key goals of the National Brain Databank, the process of research at the Brainbank, existing gene expression repositories and metadata standards as well as relevant software tools and databases. The paper proposes a high-level implementation approach for the National Brain Databank’s online gene expression repository including the conceptual database model and application framework, rationale for adopting Java J2EE, Oracle and Linux as the basis for the system and outlines the ongoing requirements analysis work. Based on review and feedback from Brainbank, key decisions and tradeoffs indicated here will be resolved to finalize the key requirements and specifications towards development of the first system release of the National Brain Databank.123/4/geo/2 Lifecycle of Research at the BrainbankThe Harvard Brain Tissue Resource Center (the Brainbank) was established at McLean Hospital as a centralized, federally funded resource for the collection and distribution of human brain specimens for research. As a designated “NIH National Resource”, the Brainbank provides a vital public service by collecting and disseminating postmortem brain tissue samples to the neuroscience research community (at no charge). These brain tissues are typically related to neurological disorders including Huntington's, Parkinson's and Alzheimer's, psychiatric disorders like schizophrenia or manic-depression (bipolar disorder), as well as normal control specimens which are essential for comparative work. Collectively, these specimens are used for a wide variety of applications, including receptor binding, immunocytochemistry, in situ hybridization, virus detection, polymerase chain reaction (PCR), DNA sequencing, mRNA isolation, and a broad range of neurochemical assays.2.1 Acquisition and Curation of Brain Tissue SamplesHaving been established for over 20 years, the Brainbank has created a strong reputation as a NIH National Resource for brain tissue collection, archiving and dissemination to aid neuroscience research. To maintain this high standard, the Brainbank takes very meticulous care in receiving, documenting, caring for, and collecting background data for its cases. Samples are examined by neuropathologists and extensive case histories and family interviews are performed wherever possible, given privacy and practical limitations.There are currently over 5800 brains stored in the Brainbank. Previously, brain tissue samples for Huntington's, Parkinson's, and Alzheimer's disease were collected, whereas now the Brainbank additionally collects samples from patients with psychiatric disorders such as schizophrenia or manic-depression as well as normal control tissue. The Brainbank also houses private collections of brain tissue samples for the Tourette Syndrome Association (TSA), which are managed by the organization. Over the years, the Brainbank has compiled a representative brain tissue sample for research called the "McLean 66" cohort5 (with samples from about 66-67 brains) includes roughly equal numbers of Schizophrenic, Bipolar (hardest to obtain), and control cases. Gene expression data is now being derived from this set and will be included in the online repository initially.The Brainbank’s website currently provides password-based access to an anonymized catalog of brain tissue samples, with demographic information, diagnosis information, some neuropathological and clinical information, and related images. Investigators can browse and query the database and request additional demographic information as well as the actual samples from the Brainbank. Requests for samples are handled by an independent committee that provides a recommendation to the Brainbank, before it can supply these tissue samples to the investigators. Currently the Brainbank supplies nearly 100 investigators with about 4000 samples every year.2.2 Gene Expression Experiments using MicroarraysIn addition to providing brain tissue samples with the relevant patient demographic information, the Brainbank is currently extracting gene expression levels from thousands of DNA samples of its tissue specimens. Over the last 2 years the Brainbank has expanded its capability to extract gene expression data using newly acquired microarray technologies6 primarily from Affymetrix, including GeneChip® microarrays. Previously all gene expression experiments were contracted out to external labs; however the results were neither consistent nor of high quality. Hence the decision was made to bring this capability in-house.Affymetrix offers high-density microarrays for human, mouse and rat genomes. These arrays are clustered into sets of GeneChips containing probe pairs for up to 12,000 transcripts. For example, the Human U133 Genome Set of more than 39,000 transcripts is divided over two GeneChips labeled A (composed of known genes) and B (composed of express sequence tag or EST7 with unknown function). Affymetrix matching uses 25 base pair (bp) probes8 affixed to known regions on a DNA chip, which has between 8,900 and 33,000 probes. The Microarray scanner uses lasers to detect DNA stained with fluorescence, to help analyze binding of complementary5 This previously originated as the “McLean 60” cohort sample, which has since been slightly expanded to include additional brain samples.6 Tutorial on microarrays: /About/primer/microarrays.html7 Express sequence tag (EST) is a single-pass sequence from either end of a cDNA clone; approximately 200 to 600 base pairs in length.8 A labeled, single-stranded DNA or RNA molecule of specific base sequence, that is used to detect the complementary base sequence by hybridization. At Affymetrix, probe refers to unlabeled oligonucleotides synthesized on a GeneChip probe array.cDNA/RNA sequences from the tissue sample. Each chip costs about $600 in materials (including reagents) to prepare and run, and takes about a week to prepare (as part of a batch).Researchers at the Brainbank create 5-7 gene expression profiles for each case, corresponding to the various brain regions that need to be studied. A gene expression experiment is rarely repeated for the same tissue sample to create another profile, unless the first one yields poor quality data. For example, over-washing and straining of the tissue samples in preparation for gene expression experiments can yield uniformly white images which are not useful for analysis. Hybridization quality is verified using background calculations in the report files generated, particularly examining the 3’/5’ signals at housekeeping genes (values of 2 are considered good). Before the Brainbank had acquired in-house capacity to conduct microarray experiments, it had provided the RNA solutions for the McLean 60 cohort study to a commercial firm, Psychiatric Genomics9 in Maryland to allow them to replicate these experiments using their own approach and unique procedures. This data may be provided to the Brainbank in the future, and hence it must be archived as a replicated data set accordingly. Experimental replicates may be assigned the same or different accession number.Gene expression experiments can generate nearly 72 MB of data for a just a single typical array according to Affymetrix10, hence the storage and management of such data becomes a crucial task. Each sample hybridized to an Affymetrix GeneChip generates five Absolute Analysis files:1. EXP: The experimental file (in ASCII text) stores laboratory information, for each array including theexperiment name, sample, and the type of GeneChip array being used.2. DAT: The data file contains the raw image of the scanned GeneChip array, corresponding to the rawhybridization data. These data are not processed, and no scaling factors or normalization is embedded.(40-70 MB)3. CEL: The cell intensity file assigns X, Y coordinates to each cell on the array and calculates the averageintensity of each cell. This file can be used to re-analyze data with different expression algorithmparameters. This file provides a normalized image. (the ASCII/Excel file is around 10-12 MB)4. CHP: The chip file is generated using information in the CEL file to determine the presence or absence ofeach transcript and its relative expression level. (Binary file around 7 MB for Rats and 14MB for Humans)5. RPT: The report file (in ASCII text) provides quick access to the quality control information for eachhybridization, including noise, scale factor, target values, percent present, absent or marginal andaverage signal values, and housekeeping controls such as Sig(3'/5').The report file is often examined first after running an experiment to ensure the quality of results and then the image files are used to check for any artifacts. Affymetrix software uses the EXP file together with the DAT file to process the raw data in order to generate data analysis files. The chip file is primarily used for statistical analysis. Although the EXP, DAT, CEL, CEL, CHP and RPT files can only be read using Affymetrix software, the quantitative and qualitative expression values in each CHP file can be exported as text (tab delimited) files. The DAT and CHP image files can be saved in TIFF format and later converted to JPEG for easy viewing. Optionally a mask file can also be generated to provide additional information on the microarray chip quality.The Affymetrix Absolute Analysis text files contain a row for each transcript represented in the microarray and columns of the raw expression data for that transcript (indicating mRNA expression levels). The Affymetrix platform contains multiple pairs of perfect match and mismatch oligonucleotides11 for each transcript examined. The software uses the pattern and intensity of hybridization to these oligos to calculate a relative expression value for each transcript (referred to as ‘Signal’ in version 5.0 Microarray Suite and ‘Average Difference’ in previous910/technology/data_analysis/11 A short length of single-stranded nucleotides; used as probes on GeneChip® arrays.software versions).12 The algorithms also determine whether each transcript was detected during the hybridization. This qualitative information is reported as a “Present”, “Absent” or “Marginal”.Each Affymetrix microarray contains thousands of different oligonucleotide probes. The sequences of these probes are available at the Affymetrix NetAffx13 website. It provides background/annotation info on the Affymetrix probes (based on probe ID) and also maps relationships between Affymetrix microarray chip probe IDs with that of repositories like GenBank. Currently, GenBank and dChip do not read Affymetrix IDs. Generating and using these IDs from the NetAffx website is somewhat confusing as they are not always corresponding and the relationships between them can often be many to 1, 1 to many, or many to many.Affymetrix software includes MicroSuite for cataloging microarray data, the MicroDB database, and Data Mining tools which perform statistical tests and run on MicroDB. The Affymetrix Analysis Data Model14(AADM) is the relational database schema provided along with a set of Application Programming Interfaces (API) implemented as views to provide access to data stored in Affymetrix-based local gene expression databases. While the raw microarray gene expression data may be stored in an internal database, the results are valuable for the neuroscience researchers if the data is shared along with the relevant experimental metadata and demographic details. Hence it is important to consider standards and ontologies for sharing microarray data among databases and analytic tools used by the research community.2.3 Analysis of Expression Data: Software Tools and Data StandardsA number of software tools are used for analysis of gene expression data generated by Microarray experiments. In addition to Affymetrix’s own Data Mining Tool (DMT) and a number of proprietary commercial tools, several freely available tools are used within the research community including dChip, BioConductor and GeneCluster. Affymetrix provides the Data Mining Tool (DMT) v3.015 to allow filtering and sorting of expression results from microarray experiments, perform cluster and matrix analysis as well as annotate genes (manually or from the NetAffx website). DMT software runs on Windows NT and allows multiple queries to be performed in multiple GeneChip experiments simultaneously. To load data, one must register and select the MicroDB database to query and view the CHP files generated by Affymetrix. These can then be filtered to perform relevant analysis. Despite having been developed for Affymetrix users, the software interface does not appear to be intuitive, and many of these features have now been incorporated in publicly available analysis tools.16The DNA Chip Analyzer (or dChip)17 is the most commonly used microarray analysis software, particularly utilized at the Brainbank. It was developed by Dr. Cheng Li (2003) at the Harvard School of Public Health and is freely available from Harvard. dChip requires the CDF chip file and the CEL files for conducting analysis. The software can normalize the data, export expression values, filter genes, and perform hierarchical clustering or compare genes between groups of samples. The authors of dChip encourage researchers to make their gene expression results available publicly for analysis by others:18“We encourage researchers who generate Affymetrix data to also put the CEL or DAT files available with the paper. This will enhance the efforts of improving on the low-level analysis of Affymetrix microarray such as feature extraction, normalization and expression indexes, as well as ease the data-sharing and cross-reference among researchers since CEL level files can be pooled to analyze in a more controlled manner.CEL files have text format and contain summarized probe-level (PM, MM) data of Affymetrix array. dChip software uses the raw CEL files. If CEL files are stored in a central database system (containing the raw CEL files or directory links to CEL files), such a function would be convenient (as implemented in theWhitehead Xchip database): users query the database through web interface for their experiments, and request the raw CEL files to be stored temporarily on a ftp site for downloading.“12/documents/tech/Tech%20Note%20-%20Data%20Deliverables.pdf13/analysis/index.affx14/support/developer/15/products/software/specific/dmt.affx16 Manual on Affymetrix Data Mining Tool compiled by Bob Burke at the Brainbank, Summer 2003.17/complab/dchip/18/complab/dchip/public%20data.htmBioConductor19 is collaborative open source software developed by researchers at the Dana Farber Cancer Institute and the Harvard Medical School/Harvard School of Public Health. It provides a range of tools for statistical and graphical methods for analysis of genomic data and facilitates integration of biological metadata from PubMed and LocusLink. It is based on the “R” statistical programming language. The system handles Affymetrix data by allowing users to provide CEL files, as well as phenotypic and MAIME information through graphical widgets for data entry.GeneCluster 2.020 is a Java-based software tool developed by Whitehead Institute/MIT Center for Genome Research (WICGR). GeneCluster allows data analysis using supervised classification such as K nearest neighbor, gene selection and permutation tests. GeneCluster supports 2 data formats – the WICGR RES file format (*.res) and the GCT (Gene Cluster Text) file format (*.gct). The main difference between the two file formats is the RES file format contains labels for each gene's absent (A) versus present (P) calls as generated by Affymetrix's GeneChip software (which are currently ignored by GeneCluster). Data files for use in GeneCluster can be created automatically by a special tool such as WICGR's Res File Creation Tool or manually by standard tools such as Microsoft Excel and text editors.To support data exchange with a range of analytic tools, the online repository for the National Brain Databank must provide the CHIP (for Affymetrix DMT), CDF and most importantly the CEL files in raw form for downloading. In addition, any report and experiment files may also be desired by some researchers to gain confidence in the experiments, while experimental metadata in accordance with MAIME will be useful for analysis as well. All files generated by Affymetrix can be placed in a secure directory within the server and referenced in the sample metadata, such that they can be easily accessed if the online user has appropriate privileges. In the future many analytic tools will begin to support MAIME metadata and microarray data import/export in MAGE-ML formats, such as GeneSpring21 and GenePix22.2.4 Current Computing Infrastructure and Databases at the BrainbankThe Brainbank currently houses its databases in 2 main servers (Brain Servers 1 and 2) while a third server is being deployed for the National Brain Databank and an additional machine will be provided for development. Clinical Server (or Brainserver-1) hosts the primary brain tissue and clinical data. As it contains the initial unanonymized patient data (Brains DB), it maintains restricted access in compliance with HIPAA guidelines. The server configuration is a HP Proliant ML370 G2 with 1 GHz processor, 256 MB RAM and 37.8 GB storage, RAID5 w/ (6) 9.1 GB removable hard drives. It runs on Windows NT 4.00.1381 with ML SQL Server 7.00.839 and MS Access databases. Clinical demographic and diagnostic data for brain samples are archived on these databases. It also includes brain tissue information and freezer inventory as well as neuropathology reports. This server is isolated from other machines on the network to maintain security of sensitive data.Public Web Server (or Brainserver-2) hosts the publicly accessible website for the Brainbank23 and the Harvard Image Database v1.0024 which allows restricted access to query the anonymized data on brain tissue samples. The server configuration is a HP Proliant ML370 G3 with 2.4 GHz processor, 1.5 GB RAM and 90.2 GB storage, RAID5 w/ (6) 18.2 GB removable hard drives. It runs on Windows NT 4.00.1381, IIS Server with ML SQL Server 7.00.839 and Webhunter v4.0 databases. The Webhunter is a database product developed by ADS Image, Inc.25 which is used for querying and indexing brain tissue images stored in SQL Server (previously in Access). The database (Anonymous Brains) contains anonymized brain tissue and clinical data, which is bulk imported manually using SQL Server scripts from the databases in the clinical server.National Brain Databank (Brainserver-3 or National-DB) will host the public gene expression repository for the Brainbank. Some data from other Brainbank databases will be imported into the database running on this server. The server configuration is a HP Proliant ML370 G3 with dual 2.4 GHz processors, 1.5 GB RAM and 90.2 GB19/20/cancer/software/genecluster2/gc2.html21/cgi/SiG.cgi/Products/GeneSpring/index.smf22/GN_GenePixSoftware.html2324/BrainDB/default.htm25。
数据库基础期末⏹主观题用黑色字迹的签字笔作答（切勿使用其它颜色的签字笔）。
⏹答题过程中，要保持答卷纸或答题卡清洁。
⏹考试结束信号发出后，要立即停笔并起立。
一、单项选择题（每小题2 分，共 40分）1、一下对于Oracle文件系统描述错误的是（）？A、*nix下Oracle的可执行文件在$Oracle_HOME/bin/Oracle,$Oracle_HOME/bin也应该包含在路径环境变量内B、Windows下Oracle的可执行文件在%Oracle_HOME%\bin\Oracle.exe,其他C、硬件加密D、固件加密【答案】B2、客户/服务器体系结构的关键在于（）A、计算的分布B、功能的分布C、CPU的分布D、数据的分布【答案】B3、以下（）是linux 环境下MySQL默认的配置文件。
A、fB、fC、fD、f【答案】A 4、关系模式的候选码可以有（）A、0个B、1个C、1个或多个D、多个【答案】C5、设关系R和S的属性个数分别为n和m,那么RS操作结果的属性个数为（）。
A、nxmB、max(n,m)C、n+mD、n-m【答案】C6、数据库一般使用（）以上的关系。
A、1NFB、3NFC、BCNFD、4NF【答案】B7、SQL语句的一次查询结果是（）。
A、数据项B、记录C、元组D、表【答案】D8、在DBS中，DBMS和OS之间关系是（）。
A、并发运行B、相互调用C、OS调用DBMSD、DBMS调用OS【答案】D9、Oracle数据库中，当实例处于NOMOUNT状态，可以访问以下（）数据字典和动态性能视图。
A、DBA_TABLESB、V$DATAFILE精选整理C、V$INSTANCED、V$DATABASE【答案】C10、在Oracle中，当控制一个显式游标时，下列（）命令包含INTO子句。
A、OpenB、CloseC、FetchD、CURSOR【答案】C11、查询x的字符集需要使用______函数？A、convert(x)B、collation(x)C、charset(x)D、set(x)【答案】C【解析】本题得分：0分12、声明一个类型为int类型的变量i,并将其赋值为10。
