(a) Schedule S1 does not contain any phenomena or violations of locking rules.S1 executes all its transactions correctly in a serial fashion, with no two conflicting operations happening simultaneously.
It has read locks on Z, Y and X in transaction order T1, T3 and T2, respectively. It then goes on to commit its transactions in the reverse order of their locking. Since all locks are released in the correct order and no two transactions access the same data simultaneously, the schedule is free of phenomena and complies with locking laws. The highest isolation level that this schedule can be executed on is Repeatable read. This schedule satisfies the Serializable isolation level.(b) Schedule S2 has a Dirty Read Phenomenon on Y by T1. S2 includes a violation of the dirty read phenomenon because T1 reads a value written by T3 before it has been committed. The phenomenon arises because T3 modifies Y, then T1 reads and prints the updated value of Y.
However, the modifications performed by T3 are not permanent, and the transaction has not yet been committed. Since T1 reads the value of Y that has not been committed yet, a Dirty Read phenomenon occurs. The highest isolation level that this schedule can be executed on is Read committed. This schedule satisfies the Read Uncommitted isolation level.(c) Schedule S3 has a Lost Update Phenomenon on X by T2. S3 violates the Lost Update phenomenon since T1 and T2 both read and write to X, but T1's changes are overwritten by T2's changes, resulting in a lost update. The phenomenon arises because T2 modifies X's value after T1 has read and updated it but before T1 commits its transaction. When T1's changes are overwritten by T2's changes, T1's modifications are effectively lost. The highest isolation level that this schedule can be executed on is Repeatable Read. This schedule satisfies the Read Committed isolation level.
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Part 2: Short answer questions. There are 5 questions each worth 2 marks. The total mark for Part 2 is 10 marks. n databases, derived attributes are often not represented. Give two reasons why you would include derived attributes in a database? Enter your answer here
Enhanced data analysis: Derived attributes assist in providing valuable insights about the data that would not have been feasible with only the core attributes.
Computational efficiency: Including a derived attribute in the database enhances computational efficiency.
In databases, derived attributes are often not represented. The two reasons why we would include derived attributes in a database are as follows:
Enhanced data analysis: Derived attributes assist in providing valuable insights about the data that would not have been feasible with only the core attributes.
They assist in making the data more meaningful by revealing hidden patterns, relationships, and trends in the data. They're also used to calculate metrics like profits and loss, revenue, and so on.
Computational efficiency: Including a derived attribute in the database enhances computational efficiency.
Consider, for example, a client database that contains clients' birth dates and their ages. Instead of performing computations to determine clients' ages each time, a calculated age derived attribute can be included in the database to improve query performance and save computational resources.
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In the Project Opportunity Assessment, the first question is the aim of all questions.
True/False
A problem statement is an unstructured set of statements that describes the purpose of an effort in terms of what problem it’s trying to solve.
True/False
In the Project Opportunity Assessment, the first question is the aim of all questions. The statement is False. A problem statement is an unstructured set of statements that describes the purpose of an effort in terms of what problem it’s trying to solve. The statement is True.
The Project Opportunity Assessment is the initial phase of project planning and evaluation. It is also known as an opportunity assessment. This stage helps organizations to determine whether the project is worthwhile or not and whether it aligns with their objectives and goals.
This stage considers the project's practicality and viability in terms of cost, timeline, and resource allocation. To achieve this, project managers ask several questions to determine the project's feasibility, market demand, and potential benefits. The first question in this process is not the aim of all questions. Instead, this phase has several questions that are crucial to the project's success.A problem statement is a clear and concise statement that explains the issue that needs to be addressed in the project. It provides a background for the project, highlights the gap in existing knowledge, and explains the significance of the issue.
The problem statement is used to guide the project's scope, objectives, and goals. It helps to identify the stakeholders and their expectations. It also serves as a tool for communication between the project team and stakeholders. Therefore, the statement "A problem statement is an unstructured set of statements that describes the purpose of an effort in terms of what problem it’s trying to solve" is True.
The Project Opportunity Assessment is an essential stage in project management. It helps organizations to assess the feasibility of the project and determine its potential benefits. The stage involves asking several questions to ensure the project aligns with the organization's objectives and goals. The first question is not the aim of all questions, but rather one of several critical questions.
Additionally, the problem statement is an essential tool in project management. It helps to guide the project's scope, objectives, and goals. It is also used to identify stakeholders and communicate with them. Thus, a problem statement is an unstructured set of statements that describes the purpose of an effort in terms of what problem it’s trying to solve.
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If you know that the weight on the driving wheels of a tractor is 93,800 lb and that it is moving
on firm earth with a coefficient of traction of 0.55, what is the usable power if the tractor is
operating in Abha (elevation of approx. 7,000 ft above sea level):
a. 51,590 lb
b. 55,440 lb
c. 40,760 lb
d. 45,400 lb
The usable power of the tractor cannot be determined without knowing the speed. The answer is option d) 45,400 lb
Explanation: In order to find out the usable power, we first calculate the traction force. The traction force is the maximum force that the tractor can exert. It is equal to the product of the normal force and the coefficient of traction. The normal force is equal to the weight on the driving wheels, which is given to be 93,800 lb.The coefficient of traction is 0.55. So, the traction force is: 93,800 × 0.55 = 51,590 lbNow, we use the formula for usable power:Usable power = Traction force × Speed ÷ 375The speed is not given, so we cannot calculate the usable power. The usable power depends on both the traction force and the speed. A higher speed will require a higher usable power. A lower speed will require a lower usable power.
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Show that the communalities in a factor analysis model are unaffected by the transformation A = AM Ex. 5.3 Give a formula for the proportion of variance explained by the jth factor estimated by the principal factor approach.
In factor analysis, communalities measure the amount of variance of the variables that are accounted for by the common factors. These communalities are typically estimated by the initial solution of factor analysis. A transformation of variables does not change the communalities in the factor analysis model.
Explanation: A = AM transformation In factor analysis, the variables can be transformed by an orthogonal transformation. This transformation does not change the communalities of the factor analysis model. The transformation can be written as A = AM where A is a transformed variable matrix, M is an orthogonal transformation matrix, and A is the original variable matrix.
Proportion of variance explained The proportion of variance explained by the jth factor estimated by the principal factor approach is given by the formula.
Var(j)/ (total variance) where Var(j) is the variance of the jth factor, and (total variance) is the total variance of the variables.
There are several methods to estimate the proportion of variance explained by the j th factor.
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