教材：

[1]MySQL：(ISBN：9787302560067)党德鹏. 数据库应用、设计与实现(第二版). 北京: 清华大学出版社, 2023.9

[2]SQLServer：(ISBN：9787302629498)党德鹏. 数据库系统原理与实践. 北京: 清华大学出版社, 2023.7

[3]PostgreSQL：(ISBN：9787302464037)党德鹏. 数据库应用、设计与实现. 北京: 清华大学出版社, 2017.3

参考文献：

[1]Yan R., Jiang X., Wang W., Dang D. et al. Materials information extraction via automatically generated corpus. Sci Data 9, 401，2022，Nature子刊.

[2]Shaofei Wang, Depeng Dang∗, "A Generative Answer Aggregation Model for Sentence-level Crowdsourcing Task," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2022.3142821,2023,SCI，CCF A

[3]Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring，https://openreview.net/group?id=acmmm.org/ACMMM/2024/Conference#tab-accept-poster，ACMMM2024，CCF A

[4]Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution，https://openreview.net/group?id=acmmm.org/ACMMM/2024/Conference，ACMMM2024， CCF A

[5]Prompt-based Ingredient-Oriented All-in-One Image Restoration，https://ieeexplore.ieee.org/abstract/document/10526271，IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY，2024

[6]Weiren Wang,Xue Jiang,Shaohan Tian,Pei Liu,Depeng Dang,et al. Automated pipeline for superalloy data by text mining. npj Computational Materials 8,9(2022).https://doi.org/10.1038/s41524-021-00687-2.（Top；CiteScore: 11.00；Impact Factor:12.241；5-Year Impact Factor: 13.993）

[7]Shaofei Wang, Depeng Dang∗. Robust Cross-lingual Knowledge Base Question Answering via Knowledge Distillation[J]. Data Technologies and Applications,2021,SCI.

[8]Rongen Yan,Xue Jiang, Depeng Dang, Named Entity Recognition by Using XLNet-BiLSTM-CRF, Neural Processing Letters,2021,SCI.

[9]Depeng Dang, Chuangxia Chen, Haochen Li, Rongen Yan, Zixian Guo, Xingjian Wang, Deep knowledge-aware framework for web service recommendation, The Journal of Supercomputing,2021,SCI.

[10]Shaofei Wang, Depeng Dang∗, Incentive Mechanism for the Listing Item Task in Crowdsourcing, Information Sciences,Volume 512, Pages 80-95,ISSN 0020-0255, February 2020,SCI.（Top；CiteScore: 6.90；Impact Factor:5.524；5-Year Impact Factor: 5.305）

[11]Xinxin Wang, Depeng Dang ∗, Zixian Guo. Evaluating the crowd quality for subjective questions based on a Spark computing environment, Future Generation Computer Systems,(106):426-437,2020,SCI.

[12]Depeng Dang∗, Chuangxia Chen, Wenhui Yu and Huaxiao Hu. A semantic-aware collaborative filtering recommendation method for emergency plans in response to meteorological hazards. Intelligent Data Analysis,(24):705-721,2020,SCI.

[13]Dang Depeng*; Yu Wenhui; Chen, Chuangxia; Real-time detection and trend tracing of burst topics based on Negative Binomial Distribution on spark, Intelligent Data Analysis, 24(4) :925-940, 2020,SCI.

[14] Shaofei Wang, Depeng Dang, Zixian Guo, Chuangxia Chen, Wenhui Yu. Knowledge Enhanced Quality Estimation for Crowdsourcing. IEEE Access,I EEE Access PP(99):1-10, July 2019,SCI.

[15]Hu, Xin; Liu, Zhijie;Yao, Yingting;Wang, Nan;Dang, Depeng.Crowdsourcing Model Research for the Identification of Post-Earthquake Rescue Objects. Journal of Earthquake Engineering,23(5):863-881(IF:2.754),2019,SCI.

[16]Xin Hu, Depeng Dang. Crowdsourcing-based semantic relation recognition for natural language questions over RDF data. Enterprise Information Systems. 13(7-8): 935-958,2019,SCI.

[17]Dang Depeng, Xue Jiang, Nan Wang, Yingting Yao,Wenbin Yao. Concurrency Control of Real-Time Web Service Transactions. Journal of information science and engineering, 34(1):261-287,2018,SCI.

[18]Xin Hu, Depeng Dang, Yingting Yao, Luting Ye, Natural language aggregate query over RDF data, Information Sciences, Volumes 454–455, Pages 363-381,ISSN 0020-0255, 2018（Top，CiteScore: 6.90；Impact Factor: 5.524；5-Year Impact Factor: 5.305）,2018,SCI.

[19]D. Dang, Y. Liu, X. Zhang and S. Huang, "A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications," in IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 7, pp. 1879-1888, 1 July 2016.（CCF A）,2016,SCI.

[20]Shihang Huang, Ying Liu, Depeng Dang,Burst topic discovery and trend tracing based on Storm,Physica A: Statistical Mechanics and its Applications, Volume 416,2014,Pages 331-339,2014,SCI.（SCI CiteScore: 2.82；Impact Factor: 2.500；5-Year Impact Factor: 2.464

[21]党德鹏,高虎,杨竞帆,等.新时代数据库系统原理教材建设实践[J].计算机教育,2022,(11):200-204.DOI:10.16512/j.cnki.jsjjy.2022.11.021.

[22]党德鹏,陈曦,龙启铭,等.数据库系统原理课程思政建设实践[J].计算机教育,2022,(02):22-25.DOI:10.16512/j.cnki.jsjjy.2022.02.006.

[23]党德鹏,郭碧琳,过紫娴,等.数据库系统原理课程线上线下混合教学实践[J].计算机教育,2021,(05):174-178.DOI:10.16512/j.cnki.jsjjy.2021.05.042.

（1）实验中用到的软件从哪里获取？

  答：如下所列:

  PostgreSQL：https://www.postgresql.org/download/windows/

                  PostgreSQL11.5/10.10 （187MB，含图形化界面），Windows X86-64位

    MySQL：https://www.MySQL.com/downloads

    SQLServer：https://www.microsoft.com/zh-cn/sql-server/sql-server-downloads#

    Oracle：https://www.oracle.com/technetwork/database/enterprise-edition/downloads/index.html

           https://www.oracle.com/technetwork/cn/products/express-edition/downloads/index.html  

    Python:https://www.jetbrains.com/pycharm/ 

    Java：https://www.oracle.com/technetwork/java/javase/downloads/

    Eclipse jdk：https://www.eclipse.org/downloads/

    Dev-C++：https://sourceforge.net/projects/orwelldevcpp/

             https://bloodshed-dev-c.en.softonic.com/ 

    ODBC：https://odbc.postgresql.org/

    JDBC：https://jdbc.postgresql.org/download.html 

    Apache Tomcat：https://tomcat.apache.org/ 

（2）课程有哪些新颖之处？

  答：总结出课程有如下“七新”。

ž   “新知识”：纳入了数据管理技术最新发展，融入大数据技术最新进展和数据管理技术发展趋势。

ž   “新认识”：对传统数据库课程知识点即旧知识有新的、更深刻的认识。站在大数据管理的角度，拓展传统数据库关键技术方法，乃大数据时代数据库教学的尝试和实践；梳理了数据保护知识体系。

ž   “新结构”：重新梳理了课程知识体系结构。以安全、简单、高效地共享数据这个总目标为线索贯穿全书知识点；先讲语言，让学生通过上机使用数据库，对数据库有直观了解，再讲数据库设计，最后讲实现，由浅到深，由外到里，便于理解；研磨了与相关课程联系，实现无缝平滑衔接。

ž   “新案例”：以应急管理和网络考试系统为教学案例，实施案例驱动的教学模式。

ž   “新习题”：形成了完善的特色习题集，包括随堂练习、单元测试、期末测验等，有整套的题集。

ž   “新模式”：建立了增量式实验组织模式，习新温故，迭代前行；面向互联网提供整套的实验指导书、实验讲解ppt、实验报告要求及提纲、实验报告样板、实验报告评分标准细则、学生实验碰到的各种问题及讨论答疑，可有效实施增强混合学习。配合网上学习，针对实验操作、课程重点和难点适时穿插视频交互式直播进行讲解和答疑。

ž   “新思政”：围绕“社会主义核心价值观”、“家国情怀”、“工匠精神”等数据，融入课程教学与实验操作过程，培养学生高尚情操。

（3）课程调度是怎样的? 

（4）学习 “数据库系统原理”，课程考核具体包括哪些内容？

答：在线讨论与课堂交流10%，发帖和回帖的数量达到5条为满分，不足5条时按比例计算，水贴不记入总数；单元测验70%，题型包括选择、判断；期末测验20%，题型为选择、判断。课程前六周会按节奏逐步发布单元测验，第六周发布期末考试，所有单元测验和期末考试的截止时间为2025年6月22日23:30，经平台审核后7月2日前后发布课程正式成绩并开放证书申请。单元测验可以尝试多次取成绩最高者计入课程成绩；期末考试只有一次作答机会请慎重选择时机参与。需要提前获取课程成绩的学员，可以在第六周后提前学习课程全部内容并完成单元测验和考试，然后联系助教或教师，获得含主讲教师签字的课程成绩截图；引用该课程开展SPOC教学的老师可以随时联系主讲教师或助教获取教学资源、商榷教学实施。

（5）对学好“数据库系统原理”课程有哪些建议？

  答：阅读教材预习，观看视频学习，完成随堂练习，实践实验习题，完成单元测验并复习，随时积极参与讨论。

（6）需要时如何联系助教或老师？

  答：请发邮件，two_bits@163.com；skysnow2017@gmail.com。