Johns Hopkins University
Distributed Query Optimization and Security
Johns Hopkins University

Distributed Query Optimization and Security

David Silberberg

位教师:David Silberberg

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2 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Learners will gain skills in implementing database security through views, dynamic authorization, and semantic integrity rules.

  • Students will understand query optimization techniques, cost evaluation, and the creation of optimized query plans to enhance database performance.

  • Learners will master distributed query optimization, using cost models, MapReduce, and HDFS for efficient data storage, compression, and processing.

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作业

9 项作业

授课语言:英语(English)

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积累特定领域的专业知识

本课程是 Large-Scale Database Systems 专项课程 专项课程的一部分
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该课程共有4个模块

This course delves into advanced topics in query processing and data security within distributed databases. Students will learn about semantic data control, including the use of views and authorization techniques, to secure data access. The course also covers distributed query processing, and optimization methods. By understanding these techniques, learners will develop skills to create optimized, secure distributed databases capable of handling complex data queries. This module also introduces another paradigm of large-scale data systems supported by Hadoop, MapReduce and HDFS.

涵盖的内容

2篇阅读材料

This course provides an in-depth exploration of database security, focusing on defining and implementing views for secure data access. Students will learn how to apply dynamic authorization techniques through cascading grant and revoke policies, and specify semantic integrity rules essential for maintaining security and consistency within distributed database systems.

涵盖的内容

3个视频4篇阅读材料3个作业

This course delves into the critical aspects of query optimization in databases, focusing on the motivation behind optimizing queries, evaluating the cost-effectiveness of various query plans, and understanding the steps involved in the optimization process. Students will learn to calculate optimization costs and decompose SQL queries into efficient, optimized query trees, enhancing overall database performance.

涵盖的内容

3个视频4篇阅读材料3个作业

This module delves into distributed query optimization techniques, introducing key methodologies to improve performance in distributed systems using cost models and heuristics for near-optimal query plans. Learners will gain practical skills in query optimization, leveraging the semi-join algorithm. Learners will also gain experience in another popular large-scale data approach embodied in MapReduce supported by the Hadoop Distributed File System (HDFS) to efficiently process large datasets. Through hands-on exercises, students will practice writing HDFS code for data storage and manipulation, focusing on data compression and decompression, and employ advanced MapReduce patterns for optimal data processing..

涵盖的内容

6个视频6篇阅读材料3个作业

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位教师

David Silberberg
Johns Hopkins University
3 门课程705 名学生

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