Database Systems: Design, Implementation, & Management
13th Edition
ISBN: 9781337627900
Author: Carlos Coronel, Steven Morris
Publisher: Cengage Learning
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Chapter 5, Problem 8RQ
Program Plan Intro
Attribute:
- Attribute is a property of an entity. It describes each entity in a
database and the name of the column in a table is representing an attribute. - Fields in a database table are also referred to as attributes of a database object.
Subtype:
- Subtype is one type of entity which inherits the attributes from other entity.
- The subtype relationship is a grouping of entities type in single entity that shares the common attributes or relationships which are different from other grouping.
- It is related to an entity supertype, where the supertype entity has common characteristics and the subtype entity has the unique characteristics of each entity subtype.
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Chapter 5 Solutions
Database Systems: Design, Implementation, & Management
Ch. 5 - Prob. 1RQCh. 5 - What kinds of data would you store in an entity...Ch. 5 - Prob. 3RQCh. 5 - What is a subtype discriminator? Give an example...Ch. 5 - Prob. 5RQCh. 5 - What is a disjoint subtype? Give an example.Ch. 5 - What is the difference between partial...Ch. 5 - Prob. 8RQCh. 5 - According to the data model, is it required that...Ch. 5 - Prob. 10RQ
Ch. 5 - Prob. 11RQCh. 5 - Prob. 12RQCh. 5 - Prob. 13RQCh. 5 - Prob. 14RQCh. 5 - When implementing a 1:1 relationship, where should...Ch. 5 - What is time-variant data, and how would you deal...Ch. 5 - Prob. 17RQCh. 5 - Prob. 1PCh. 5 - Prob. 2PCh. 5 - Prob. 3PCh. 5 - Prob. 4PCh. 5 - Prob. 5PCh. 5 - Prob. 6PCh. 5 - Prob. 7PCh. 5 - Prob. 8CCh. 5 - Prob. 9CCh. 5 - Prob. 10C
Knowledge Booster
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- In the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .arrow_forwardPlease 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_forward
- Here 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_forwardPlease 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_forward
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