Master this deck with 21 terms through effective study methods.
Generated from uploaded pdf
The main components of a Centralized DBMS include: 1. Data in storage, 2. Querying Data, 3. Query Optimization, 4. Indexing, 5. Transaction processing, 6. Application Development and Design, 7. Architecture.
The authors of 'Database System Concepts' are Abraham Silberschatz, Henry F. Korth, and S. Sudarshan.
The weightage distribution among assessment tools is as follows: Class Attendance and Performance - 10%, Quizzes/Assignments - 20%, Midterm - 30%, Final Exam - 40%, totaling 100%.
The Final Exam is scheduled as per the NSU academic calendar, with specific dates provided in the course outline.
In weeks 1-2, the course covers the course outline and an introduction to Distributed Databases, as well as Parallel and Distributed Database System Architecture.
A Distributed DBMS improves query performance by allowing parallel execution of queries across multiple nodes, which can significantly reduce response time compared to centralized systems.
Challenges in Distributed Database Design include determining how to distribute the database, managing replicated and non-replicated database distribution, and addressing directory management issues.
Query Processing in a Distributed DBMS involves converting user transactions into data manipulation instructions and optimizing these instructions to minimize costs related to data transmission and local processing.
Concurrency Control is crucial in a Distributed DBMS as it ensures synchronization of concurrent accesses, maintains consistency and isolation of transactions' effects, and manages deadlocks.
The main components of a Distributed DBMS include: 1. Distributed Data storage, 2. Distributed Query Processing, 3. Distributed Query Optimization, 4. Distributed Indexing, 5. Distributed Transaction Processing, 6. Application Development and Design in Distributed DBMS, 7. Distributed DBMS Architecture, 8. Advanced Distributed Model.
Reliability in a Distributed DBMS involves making the system resilient to failures, while Deadlock Management ensures that transactions do not get stuck waiting for each other. Both are essential for maintaining system integrity and performance.
Indexing in a DBMS is used to improve the speed of data retrieval operations on a database table by creating a data structure that allows for faster searches.
Data distribution in a Distributed DBMS can lead to improved performance and availability, but it also introduces complexities such as data consistency, synchronization, and potential latency issues.
Transaction Processing in a Distributed DBMS involves managing a sequence of operations that must be completed successfully to maintain data integrity, including ensuring atomicity, consistency, isolation, and durability (ACID properties).
Replicated database distribution involves creating copies of the same data across multiple locations to enhance availability and fault tolerance, while non-replicated distribution involves storing data in a single location, which may lead to performance bottlenecks.
Key features of a Blockchain Database include decentralization, immutability, transparency, and security, which allow for secure and verifiable transactions without the need for a central authority.
The NP-hard problem in Query Processing refers to the difficulty of optimizing query execution plans to minimize costs associated with data transmission and local processing, making it computationally challenging.
Application Development in a Distributed DBMS involves creating software applications that can effectively interact with distributed data sources, ensuring efficient data access, manipulation, and management across multiple nodes.
Potential issues with Deadlock Handling in a Distributed DBMS include the complexity of detecting deadlocks across distributed nodes, the need for effective resolution strategies, and the impact on overall system performance.
The architecture of a Distributed DBMS is designed to operate across multiple interconnected nodes, allowing for data distribution and parallel processing, whereas a Centralized DBMS relies on a single server for data management.
Query Optimization is important in a DBMS as it enhances the efficiency of query execution by selecting the most efficient execution plan, reducing resource consumption, and improving response times.