EPACK DATABASE SYSTEMS WITH LOOSELEAF
13th Edition
ISBN: 9780357475171
Author: Coronel
Publisher: CENGAGE L
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Chapter 10, Problem 2P
Explanation of Solution
Concurrency control:
The purpose of concurrency control is to ensure the serializability of transaction in a multiuser
Common problems with concurrent transaction execution:
Concurrency transaction is one or more transactions are processed on same time. During the concurrency transaction execution the following problems can occur in the transaction. They are:
- Lost updates
- Uncommitted data
- Inconsistent retrievals
Lost updates:
A data can be lost when the two different transactions want to update same field or row within the database at the same time.
- For example: if the transactions “T1” and “T2” want to update the same element and one of the updates is lost or overwritten by other transactions.
Uncommitted data:
The data of the transactions are not committed when the transactions are executed concurrently...
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I need help in construct a matlab code to find the voltage, the currents, and the watts based on that circuit.
Objective
Implement Bottom-Up Iterative MergeSort and analyze its efficiency compared to
recursive MergeSort. Unlike the recursive approach, which involves multiple function
calls and stack overhead, the bottom-up version sorts iteratively by merging small
subarrays first, reducing recursion depth and improving performance.
Task
1. Implement Bottom-Up Iterative MergeSort
о Start with single-element subarrays and iteratively merge them into larger
sorted sections.
Use a loop-based merging process instead of recursion.
○ Implement an efficient in-place merging strategy if possible.
2. Performance Analysis
Compare execution time with recursive MergeSort on random, nearly
sorted, and reversed datasets.
○ Measure and plot time complexity vs. input size.
O
Submission
Explain why the iterative version reduces function call overhead and when
it performs better.
•
Code implementation with comments.
•
A short report (1-2 pages) comparing performance.
•
Graph of execution time vs. input size for…
Given a shared data set, we allow multiple readers to read at the same time, and only one single writer can access the shared data at the same time. In the lecture slides, a solution is given. However, the problem is that the write cannot write forever, if there are always at least one reader.
How to ensure that the writer can eventually write? Propose your solution by using semaphores and implemented in Python
from threading import Thread, Semaphore
from time import sleep
from sys import stdout
class Reader(Thread):
def__init__(self, name):
self.n=name; Thread.__init__(self)
defrun(self):
globalnr, nw, dr, dw
whileTrue:
# ⟨await nw == 0 then nr += 1⟩
e.acquire()
ifnw>0:
#if nw > 0 or dw > 0 :
dr+=1; e.release(); r.acquire()
nr+=1
ifdr>0: dr-=1; r.release()
else: e.release()
# read data
stdout.write(self.n+' reading\n')
sleep(1)
# ⟨nr -= 1⟩
e.acquire()
nr-=1
ifnr==0anddw>0:
dw-=1 ; w.release()
else: e.release()
class Writer(Thread):
def__init__(self, name):…
Chapter 10 Solutions
EPACK DATABASE SYSTEMS WITH LOOSELEAF
Ch. 10 - Prob. 1RQCh. 10 - Prob. 2RQCh. 10 - Prob. 3RQCh. 10 - Prob. 4RQCh. 10 - Prob. 5RQCh. 10 - Prob. 6RQCh. 10 - Prob. 7RQCh. 10 - Prob. 8RQCh. 10 - Prob. 9RQCh. 10 - Prob. 10RQ
Ch. 10 - Prob. 11RQCh. 10 - Prob. 12RQCh. 10 - Prob. 13RQCh. 10 - Prob. 14RQCh. 10 - Prob. 15RQCh. 10 - Prob. 16RQCh. 10 - Prob. 17RQCh. 10 - Prob. 1PCh. 10 - Prob. 2PCh. 10 - Prob. 3PCh. 10 - Prob. 4PCh. 10 - Prob. 5PCh. 10 - Prob. 6PCh. 10 - Prob. 7PCh. 10 - Prob. 8PCh. 10 - Prob. 9PCh. 10 - Prob. 10PCh. 10 - Prob. 11P
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