Database Concepts (8th Edition)
8th Edition
ISBN: 9780134601533
Author: David M. Kroenke, David J. Auer, Scott L. Vandenberg, Robert C. Yoder
Publisher: PEARSON
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Chapter 2, Problem 2.37RQ
Program Plan Intro
Modification Problem:
A modification problem contains a circumstance where a value already exists in a
Functional dependencies:
The functional dependency is a relationship that exists at the time of one attribute distinctively determines another.
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Open the file SC_EX19_EOM2-1_FirstLastNamexlsx, available for download from the SAM website.
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Chapter 2 Solutions
Database Concepts (8th Edition)
Ch. 2 - Prob. 2.1RQCh. 2 - Define the term entity, and give an example of an...Ch. 2 - Prob. 2.3RQCh. 2 - Prob. 2.4RQCh. 2 - Prob. 2.5RQCh. 2 - Prob. 2.6RQCh. 2 - Prob. 2.7RQCh. 2 - Prob. 2.8RQCh. 2 - Prob. 2.9RQCh. 2 - Define the term unique key and give an example.
Ch. 2 - Prob. 2.11RQCh. 2 - Prob. 2.12RQCh. 2 - Define the terms candidate key and primary key....Ch. 2 - Describe four uses of a primary key.Ch. 2 - Prob. 2.15RQCh. 2 - Prob. 2.16RQCh. 2 - Prob. 2.17RQCh. 2 - Explain the term foreign key, and give an example.Ch. 2 - Prob. 2.19RQCh. 2 - Prob. 2.20RQCh. 2 - Explain three possible interpretations of a null...Ch. 2 - Give an example of a null value (other than one...Ch. 2 - Prob. 2.23RQCh. 2 - Prob. 2.24RQCh. 2 - Prob. 2.25RQCh. 2 - Prob. 2.26RQCh. 2 - Prob. 2.27RQCh. 2 - Define the term primary key in terms of functional...Ch. 2 - Prob. 2.31RQCh. 2 - Examine the data in the Veterinary Office...Ch. 2 - Prob. 2.33RQCh. 2 - Describe a modification problem that occurs when...Ch. 2 - Prob. 2.35RQCh. 2 - Prob. 2.36RQCh. 2 - Prob. 2.37RQCh. 2 - Apply the normalization process to the Veterinary...Ch. 2 - Prob. 2.39ECh. 2 - Apply the normalization process to the Veterinary...
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- Please provide me with the output image of both of them . below are the diagrams code make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder…arrow_forwardWhy I need ?arrow_forwardHere are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…arrow_forward
- Please provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…arrow_forwardI'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…arrow_forwardThe knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity. For reference, I will attach a second image…arrow_forward
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