Database System Concepts
7th Edition
ISBN: 9789332901384
Author: Henry F. Korth & S. Sudarshan Abraham Silberschatz
Publisher: MCGRAW-HILL HIGHER EDUCATION
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Chapter 18, Problem 10PE
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Practical applications of two-phase locking protocol
- Two-phase locking protocol is used for simple applications where a single granularity is accepted...
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Practical applications of two-phase locking with multiple granularity locking protocol
- It is used for applications where some applications access individual records and others access whole relations or substantial parts...
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Practical applications of tree protocol
- It is used for all applications that tend to access data items in an order consistent with a particular partial order...
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Practical applications of timestamp ordering protocol
- It is used if the application demands a concurrent execution that is equivalent to a particular serial ordering.
- Conflicts are handled by roll-back of transactions rather than waiting and schedules are also not recoverable...
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Practical applications of validation protocol
- If the probability of two conflict of two concurrently executing transaction is low, then this protocol can be used...
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Practical applications of multiversion timestamp ordering protocol
- It is used it timestamp ordering is appropriate...
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Practical applications of multiversion multiversion two phase locking protocol
- It allows read-only transactions to always commit without waiting.
- Update transactions follow two phase locking protocol and allows recoverable schedules with conflicts solved by waiting rather than roll-back...
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Can you help me solve this problem using Master's Theorem:Solve the recurrence relation f(n) = 3af(n/a) + (n + a)2 with f(1) = 1 and a > 1 byfinding an expression for f(n) in big-Oh notation.
here is example 7.6## Example 7.6 Suppose the sample population is χ 2 (2), which is non-normal but with same variance 4. ▶ Repeat the simulation, but replacing the N(0, 4) samples with χ 2 (2) samples. ▶ Calculate the empirical confidence level.(Empirical confidence level) n <- 20 alpha <- 0.05 UCL <- replicate(1000, expr = { x <- rchisq(n,df=2) (n-1)*var(x)/qchisq(alpha,df=n-1) }) sum(UCL >4) mean(UCL > 4) ## t.test function n <- 20 x <- rnorm(n,mean=2) result <- t.test(x,mu=1) result$statistic result$parameter result$p.value result$conf.int result$estimate
using r language
Chapter 18 Solutions
Database System Concepts
Ch. 18 - Prob. 1PECh. 18 - Prob. 2PECh. 18 - Prob. 3PECh. 18 - Prob. 4PECh. 18 - Prob. 5PECh. 18 - Prob. 6PECh. 18 - Prob. 7PECh. 18 - Prob. 8PECh. 18 - Prob. 9PECh. 18 - Prob. 10PE
Ch. 18 - Prob. 11PECh. 18 - Prob. 12PECh. 18 - Prob. 13PECh. 18 - Prob. 14PECh. 18 - Prob. 17ECh. 18 - Prob. 18ECh. 18 - Prob. 19ECh. 18 - Prob. 20ECh. 18 - Prob. 21ECh. 18 - Prob. 22ECh. 18 - Prob. 23ECh. 18 - Prob. 24ECh. 18 - Prob. 25ECh. 18 - Prob. 26ECh. 18 - Prob. 27ECh. 18 - Prob. 28ECh. 18 - Prob. 29ECh. 18 - Prob. 30ECh. 18 - Prob. 31ECh. 18 - Prob. 32ECh. 18 - Prob. 33ECh. 18 - Prob. 34ECh. 18 - Prob. 35ECh. 18 - Prob. 36E
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