EBK EXPERIENCING MIS,
8th Edition
ISBN: 9780134792729
Author: BOYLE
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
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Chapter 5, Problem 4ARQ
Explanation of Solution
A database contains all the necessary information that is required for processing the business.
- The database is not useful for the business user since the contents that are present in them are structured in an unorganized way.
- The data stored in the database is present in the raw form and hence database is not ideal for the users.
Four elements of database application:
The four elements of database applications that are listed below:
- Forms
- Reports
- Queries
- Application Programs.
Purpose of the elements of database application:
The purpose of the each element is described below:
Forms:
The data present in the database can be viewed, inserted, updated and deleted using the elements.
Let us consider the example:
- Consider the student mark that needs to updates in the database.
- The form can be used to enter the marks scored by the user and the mark entered by the user can be viewed by the user through forms.
- Consider the mark that is entered by the user needs to be corrected and it can be updated through forms.
- Any student record that is not required can be deleted through forms.
Reports:
The data that are stored in the database are represented in the structural format by performing sorting, grouping, filtering and through other data manipulation process.
Let us consider the example:
A student record whose score can be viewed from highest to lowest order by using the sorting operation.
Queries:
The data values can be retrieved based on the search made by the user.
Let us consider the example:
A student whose score is greater than “90” can be viewed using the query given by the user.
Application Programs:
Application programs are used to provide security to the data, maintaining the consistency of the data and to process special purpose.
Let us consider the example:
The student data can be corrected only by the teacher who enters it the data is secured in such a way no student cannot modify the data present in the database...
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Note : please avoid using AI answer the question by carefully reading it and provide a clear and concise solutionHere is a clear background and explanation of the full method, including what each part is doing and why.
Background & Motivation
Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption.
Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled.
Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data.
What We Are Doing: Overview
We solve this using a Teacher–Student knowledge distillation framework:
We train a Teacher model on a clean and complete dataset where both inputs and labels are available.
We then use that Teacher to teach two separate Student models:
Student A learns…
Here is a clear background and explanation of the full method, including what each part is doing and why.
Background & Motivation
Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption.
Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled.
Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data.
What We Are Doing: Overview
We solve this using a Teacher–Student knowledge distillation framework:
We train a Teacher model on a clean and complete dataset where both inputs and labels are available.
We then use that Teacher to teach two separate Student models:
Student A learns from incomplete input (some sensor values missing).
Student B learns from incomplete labels (RUL labels missing…
here is a diagram code :
graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
Chapter 5 Solutions
EBK EXPERIENCING MIS,
Ch. 5.3 - Prob. 1SWCh. 5.3 - Prob. 2SWCh. 5.3 - Prob. 3SWCh. 5.3 - Prob. 4SWCh. 5 - Prob. 1EGDQCh. 5 - Prob. 2EGDQCh. 5 - Prob. 3EGDQCh. 5 - Prob. 4EGDQCh. 5 - Prob. 5EGDQCh. 5 - Prob. 6EGDQ
Ch. 5 - Prob. 7EGDQCh. 5 - Consider the adage Never ask a question for which...Ch. 5 - Prob. 1ARQCh. 5 - Prob. 2ARQCh. 5 - Prob. 3ARQCh. 5 - Prob. 4ARQCh. 5 - Prob. 5ARQCh. 5 - Prob. 6ARQCh. 5 - Prob. 1UYKCh. 5 - Prob. 2UYKCh. 5 - Prob. 3UYKCh. 5 - Study Figure 5-17 to understand the entities and...Ch. 5 - Working with your team, develop a list of seven...Ch. 5 - Modify the E-R model in Figure 5-17 to include a...Ch. 5 - Discuss the advantages and disadvantages of the...Ch. 5 - Transform the data model in Figure 5-17 into a...Ch. 5 - Prob. 9CECh. 5 - Fill your database with sample data. Because you...Ch. 5 - Prob. 11CECh. 5 - Prob. 12CSCh. 5 - Prob. 13CSCh. 5 - Prob. 14CSCh. 5 - Prob. 15CSCh. 5 - Prob. 16CSCh. 5 - Prob. 17CSCh. 5 - Prob. 18CSCh. 5 - Prob. 19MMLCh. 5 - Prob. 20MLM
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