In An Environment Where Several Users Share Access To A S ✓ Solved

In an environment in which several users share access to a sing

1. (a) In an environment in which several users share access to a single database, can one user ever block another’s access for an unlimited period of time? (This situation is called indefinite postponement.) (b) Describe a scenario in which two users could cause the indefinite postponement of each other. (c) Describe a scenario in which a single user could cause the indefinite postponement of all users.

2. Using the two-step commit presented in the beginning of this chapter, describe how to avoid assigning one seat to two people, as in the airline example. That is, list precisely which steps the database manager should follow in assigning passengers to seats.

3. Suppose a database manager were to allow nesting of one transaction inside another. That is, after having updated part of one record, the DBMS would allow you to select another record, update it, and then perform further updates on the first record. What effect would nesting have on the integrity of a database? Suggest a mechanism by which nesting could be allowed.

4. Can a database contain two identical records without a negative effect on the integrity of the database? Why or why not?

5. Some operating systems perform buffered I/O. In this scheme, an output request is accepted from a user and the user is informed of the normal I/O completion. However, the actual physical write operation is performed later, at a time convenient to the operating system. Discuss the effect of buffered I/O on integrity in a DBMS.

6. A database transaction implements the command “set STATUS to ‘CURRENT’ in all records where BALANCE-OWED = 0.” (a) Describe how that transaction would be performed with the two-step commit described in this chapter. (b) Suppose the relations from which that command was formed are (CUSTOMER-ID,STATUS) and (CUSTOMER-ID,BALANCE-OWED). How would the transaction be performed? (c) Suppose the relations from which that command was formed are (CUSTOMER-ID,STATUS), (CREDIT-ID,CUSTOMER-ID), (CREDIT-ID, BALANCE-OWED). How would the transaction be performed?

7. Show that if longitudinal parity is used as an error detection code, values in a database can still be modified without detection. (Longitudinal parity is computed for the n th bit of each byte; that is, one parity bit is computed and retained for all bits in the 0th position, another parity bit for all bits in the 1st position, etc.)

8. Suppose query Q1 obtains the median m1 of a set S1 of values, and query Q2 obtains the median m2 of a subset S2 of S1. If m1

9. One approach suggested to ensure privacy is the small result rejection, in which the system rejects (returns no result from) any query, the result of which is derived from a small number, for example, five, of records. Show how to obtain sensitive data by using only queries derived from six records.

10. Cite a situation in which the sensitivity of an aggregate is greater than that of its constituent values. Cite a situation in which the sensitivity of an aggregate is less than that of its constituent values.

Paper For Above Instructions

In a multi-user environment where several users share access to a single database, the concept of indefinite postponement can arise. Indefinite postponement occurs when one user's actions prevent another user from accessing the database for an unlimited period. This is often linked to database locks and transaction management. Locking mechanisms are designed to ensure data consistency; however, they can inadvertently lead to scenarios where user A’s transaction waiting on a lock held by user B can indefinitely postpone user A if user B fails to complete their transaction.

For instance, consider two users: User A is attempting to update Record X, while User B is trying to update Record Y, which also references Record X. If User A locks Record X and User B attempts to access it while waiting for a lock, both users could end up blocking each other if the transactions are not designed to resolve potential deadlocks. This mutual blocking leads to indefinite postponement for both users.

Indefinite postponement can also occur at a broader level, where one user blocks all other users. For example, if a database administrator (DBA) initiates a lengthy transaction that requires exclusive access to certain system resources, and this transaction is not properly managed or terminated, it can prevent all users from accessing the affected database functions until that transaction completes, effectively causing indefinite postponement for the rest of the users.

To mitigate these risks, a two-step commit process can be implemented. This process ensures that a transaction is committed only when all parties involved are ready to proceed, thus preventing scenarios like double-booking in airline seat assignments. Step one involves preparing the transaction, where the system checks if the seat in question is available and reserves it temporarily. In step two, the transaction is committed by finalizing the reservation in the database. If any issues arise, the transaction can be aborted without affecting the integrity of the seat assignments.

Now, regarding the nesting of transactions, allowing one transaction to be nested within another can have ramifications for database integrity. It can complicate error handling since a failure in an inner transaction might necessitate rolling back the outer transaction. A viable mechanism to manage nesting is through the implementation of savepoints, allowing a user to specify certain points within a transaction that can be returned to upon rollback without having to abort the entire transaction.

In answering the question about duplicate records, having two identical records can indeed lead to integrity issues within a database. This situation typically violates the principles of uniqueness that many databases enforce through primary keys, and can also create confusion during data retrieval and processing. However, in specific scenarios, duplication might be acceptable (e.g., a logging system that records multiple instances of the same event).

Buffered I/O can significantly affect the integrity of a DBMS as well. In buffered I/O, a user's output request may be confirmed immediately, while the write operation occurs later. If a system failure occurs before the write completes, the information may be permanently lost or inconsistent, leading to data integrity issues. Adequate mechanisms such as journaling and write-through cache strategies can help address these risks by ensuring that data is safely stored.

When implementing a transaction, such as setting a status to 'CURRENT' for all records with zero 'BALANCE-OWED', a two-step commit process would be appropriate. First, the system would prepare by identifying all relevant records. Then, upon validation, it would update all identified records to reflect the new status. For example, if the relations involved are (CUSTOMER-ID, STATUS) and (CUSTOMER-ID, BALANCE-OWED), the transaction would first gather all customers with a zero balance before updating each customer's status accordingly.

Longitudinal parity as an error detection code has its limitations; it fails to detect modifications to data that result in the same parity bit, allowing database values to be changed without detection. This kind of vulnerability points to the necessity of stronger integrity checks beyond simple parity methods.

When it comes to analyzing medians within datasets, if Q1's median value is less than Q2's median, it suggests that subset S2 has elements positioned higher within the data distribution than S1. This insight can indicate potential biases or missing elements that require careful consideration in data management.

The small result rejection method, while potentially limiting sensitive data exposure, is not foolproof. By crafting queries that aggregate more than the threshold number of records, a malicious actor might still derive sensitive insights by cleverly constructing queries to obtain aggregate information that should have been protected.

Lastly, the discussion of aggregate sensitivity reveals nuanced scenarios. For instance, when considering income data, the aggregate mean might mask high values that, if isolated, present a more sensitive picture. Conversely, an aggregate of average customer satisfaction ratings may seem less sensitive than individual complaints that could indicate critical service failures.

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