One problem area with the DDBMSs is that of distributed database design.  (a) Discuss the issues that have to be addressed with distributed database design.

Distributed Database Management Systems (DDBMSs) are complex systems that manage data distributed across multiple platforms. Designing a distributed database involves several critical issues that need to be addressed:

1. Data Fragmentation: This involves determining how to break down the database into smaller, manageable fragments that are then distributed across different locations. The challenge is to do this in a way that optimizes performance without compromising data integrity.

2. Data Replication: This involves deciding which data to replicate on multiple sites. Replication can improve availability and performance but also increases storage costs and the complexity of keeping replicas synchronized.

3. Data Allocation: This involves deciding where to locate the data fragments. The goal is to place data close to where it is most frequently accessed to minimize data transfer costs.

4. Transparency: The system should hide the fact that the data is distributed and present it as a single, unified database to the users. This is known as distribution transparency.

5. Query Processing: The system needs to be able to process queries efficiently, taking into account the distribution of data. This involves deciding which data to access, in what order, and how to combine the data from different locations.

6. Transaction Management: The system needs to ensure that transactions are executed correctly and consistently, even if they involve data from multiple locations. This includes dealing with issues such as concurrency control and recovery from failures.

7. Security: The system needs to ensure that the data is secure, both in transit and at rest. This includes dealing with issues such as encryption, authentication, and authorization.

8. Performance: The system needs to provide good performance, both in terms of response time and throughput. This involves dealing with issues such as network latency, load balancing, and caching.

9. Scalability: The system needs to be able to scale up to handle increasing amounts of data and users. This involves dealing with issues such as adding new nodes, partitioning data, and balancing load.

10. Heterogeneity: The system needs to be able to handle different types of data and databases, as well as different types of networks and hardware. This involves dealing with issues such as data integration, data transformation, and interoperability.