DATABASE SYSTEM CONCEPTS (LOOSELEAF)
DATABASE SYSTEM CONCEPTS (LOOSELEAF)
7th Edition
ISBN: 9781260515046
Author: SILBERSCHATZ
Publisher: MCG
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Chapter 21, Problem 2PE

Explanation of Solution

a.

Load-balanced range partitioning function:

When partitioning data over n disks, range partitioning distributes the records by assigning adjacent attribute-value ranges-each disk.

  • Consider the user has a histogram where values are between 1 and 100

Explanation of Solution

b.

Algorithm:

The algorithm for computing a balanced range partition will be as follows:

  • Let the partitions be p1, p2, p3,….pp and the histogram range be h1, h2, h3,…hh, and the frequencies of the histogram range be n1, n2, n3,….nn.
  • Each partition consists of N/P records, so that N=i=1k(ni)
  • Construct the balanced partitioning sector,
    • Establish the value of  k1th  record, such that k1=N/P .
    • Establish the value of  k2th  record, such that k2=2 xN/P.
    • And so on till the value of kp-1th record, such that kp-1=(p-1)xN/P

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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
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