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1 Data Organization & ER Model Chapter 2 Instructor: Dr. Cynthia Xin Zhang.

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1 1 Data Organization & ER Model Chapter 2 Instructor: Dr. Cynthia Xin Zhang

2 2 Data design v When we build a new database … –Relational database design in DBMS v When we transform a existing database … –Data manipulation (merge, clean, format, etc.) –Information Retrieval

3 3 What Is a DBMS? v A very large, integrated collection of data. v Models real-world enterprise. – Entities (e.g., students, courses) – Relationships (e.g., Madonna is taking CSC 132) v A Database Management System (DBMS) is a software package designed to maintain and utilize databases.

4 4 Why Use a DBMS? v Data independence and efficient access. v Reduced application development time. v Data integrity and security. v Uniform data administration. v Concurrent access, recovery from crashes.

5 5 Why study Database implementation? v Good job market. –Web Developer –SQL Programmer (development DBA) –Database Administrator (production DBA) –Data Analyst v Graduate school –DBMS encompasses most of CS

6 6 Data Models v A data model is a collection of concepts for describing data. v A schema is a description of a particular collection of data, using the a given data model. v The relational model of data is the most widely used model today. –Main concept: relation, basically a table with rows and columns. –Every relation has a schema, which describes the columns, or fields.

7 7 Levels of Abstraction v Many views, single conceptual (logical) schema and physical schema. –Views describe how users see the data. –Conceptual schema defines logical structure –Physical schema describes the files and indexes used. * DDL (CREAT, ALTER, DROP); DML (SELECT, INTERT, UPDATE) ; * DCL (GRANT, REVOKE); TCL (COMMIT, SAVEPOINT, ROLLBACK). Physical Schema Conceptual Schema View 1View 2View 3

8 8 Example: University Database v Conceptual schema: – Students (sid: string, name: string, login: string, age: integer, gpa:real) – Courses (cid: string, cname:string, credits:integer) – Enrolled (sid:string, cid:string, grade:string) v Physical schema: –Relations stored as unordered files. –Index on first column of Students. v External Schema (View): – Course_info(cid:string,enrollment:integer)

9 9 Data Independence v Applications insulated from how data is structured and stored. v Logical data independence : Protection from changes in logical structure of data. v Physical data independence : Protection from changes in physical structure of data. * One of the most important benefits of using a DBMS!

10 10 Object Oriented Programming v Entity  Class v Property  Attribute v Cardinality  Multiplicity

11 11 Inside a Database Tables Relationship among tables Operations (queries)

12 12 Overview of db design v Requirement analysis –Data to be stored –Applications to be built –Operations (most frequent) subject to performance requirement v Conceptual db design –Description of the data (including constraints) –By high level model such as ER v Logical db design –Choose DBMS to implement –Convert conceptual db design into database schema Beyond ER design v Schema refinement ( normalization) v Physical db design –Analyze the workload –Indexing v Security design

13 13 Conceptual design v Issues to consider: (ER Model is used at this stage.) –What are the entities and relationships in the enterprise? –What information about these entities and relationships should we store in the database (i.e., attributes )? –What are the integrity constraints or business rules that hold? v Solution: –A database `schema’ in the ER Model can be represented pictorially ( ER diagrams ). –Can map an ER diagram into a relational schema.

14 14 University database v Entities: Students, professors, courses, textbook, classroom, transcript, s v Attributes: terms, ssn, birthdate, cell phone, account balance, parents, age, gender, gpa, major, classification, grade, name.

15 15 ER Model Basics v Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. v Entity Set : A collection of similar entities. E.g., all employees. –All entities in an entity set have the same set of attributes. –Each entity set has a key. –Each attribute has a domain. Employees ssn name lot What’s should be entities? Attributes? What’s the key? How many keys one object can have?

16 16 A Universal Data Model for All? Companies Employees Departments NameBusiness NameLocationssnBudget

17 17 Key v A key is a minimal set of attributes whose values uniquely identify an entity in the set. v Candidate key. v Primary key.

18 18 Entity, Entity Set, Attribute, and Schema ID or SSN NameUserID Age GPA John Smithjsmith Miki Jordan mjordan David Kimdkim Paul Lee plee

19 19 v Relationship : Association among 2 or more entities. E.g., Sam works in the Accounting Department. v Relationship Set : Collection of similar relationships. E.g., Many individuals works in many different departments. ER Model Basics (Contd.) lot dname budget did since name Works_In Departments Employees ssn

20 20 Entity vs. Entity Set Example: Student John Smith( , John Smith, 18, 3.5) Students in CSC , John Smith, 18, , Jie Zhang, 20, , Anil Jain, 21, 3.8

21 21 Example of Keys , John Smith, 18, , Jie Zhang, 20, , Anil Jain, 21, 3.8 Primary key Candidate key

22 22 Relationship vs. Relationship Set John Smith ITCS3160 ( , John Smith, 18, 3.5) (3160, ITCS, DBMS, J. Fan, 3, Kenn. 236) Relationship

23 23 Relationship vs. Relationship Set , John Smith, 18, , Jie Zhang, 20, , Anil Jain, 21, , ITCS, DBMS, J. Fan, 3, Kenn , ITCS, Visual DB, J. Fan, 3, Kenn. 236 Relationship set(“Enrolled in”) Students Courses

24 24 Students Name Login Id AgeGPA Courses Id NameCredit Enrolled_In Grade Relationship vs. Relationship Set Room Descriptive attribute

25 25 Example 1 v Build an ER-diagram for a university database: –Students u Have an Id, Name, Login, Age, GPA –Courses u Have an Id, Name, Credit Hours –Students enroll in courses u Receive a grade

26 26 Example 2 v Build an ER Diagram for a hospital database: –Patients u Name, Address, Phone #, Age –Drugs u Name, Manufacturer, Expiration Date –Patients are prescribed of drugs u Dosage, # Days

27 27 Constraints v Key constraints v Participation constraints

28 28 Many-to-Many 1-to-1 1-to ManyMany-to-1 Potential Relationship Types

29 29 Potential Relationship Types v Mary studies in the CS Dept. v Tom studies in the CS Dept. v Jack studies in the CS Dept. v … v The CS Dept has lots of students. v No student in the CS Dept works in other else Dept at the same time. Students CS Dept IN ??

30 30 Potential Relationship Types v Mary is taking the ITCS3160,ITCS2212. v Tom is taking the ITCS3160, ITCS2214. v Jack is taking the ITCS1102, ITCS2214. v … v 61 students are taking ITCS3160. v 120 students are taking ITCS2214. Students Courses take ??

31 31 lot dname budget did since name Works_In Departments Employees ssn Key Constraints v Consider Works_In: v An employee can work in many departments; v A dept can have many employees.

32 32 lot dname budget did since name Works_In Departments Employees ssn Key Constraints v Consider Works_In: v An employee can work in at most one department; v A dept can have many employees. Why "work-in" is not "key constraint"??

33 33 Key Constraints v In contrast, each dept has at most one manager, according to the key constraint on Manages. dname budgetdid since lot name ssn Manages Employees Departments Key Constraint (time constraint) At most one!!!

34 34 Participation Constraints v Does every department have a manager? –If so, this is a participation constraint : the participation of Departments in Manages is said to be total (vs. partial ). u Every did value in Departments table must appear in a row of the Manages table (with a non-null ssn value!) lot name dname budgetdid since name dname budgetdid since Manages since Departments Employees ssn Works_In Total w/key constraint Partial Total

35 35 lot name dname budgetdid since name dname budgetdid since Manages since Departments Employees ssn Works_In Total & key constraint Total What are the policies behind this ER model?

36 36 lot name dname budgetdid since name dname budgetdid since Manages since Departments Employees ssn Works_In Total w/key constraint Partial Total dname budgetdid since lot name ssn Manages Employees Departments Works_In Any Difference?

37 37 Weak Entities vs. Owner Entities v A weak entity can be identified uniquely only by considering the primary key of another ( owner ) entity. –Owner entity set and weak entity set must participate in a one-to-many relationship set (1 owner, many weak entities). –Weak entity set must have total participation in this identifying relationship set. lot name age pname Dependents Employees ssn Policy cost Weak Entity Identifying Relationship Primary Key for weak entity

38 38 dname budget did name Departments ssn lot Employees Works_In3 Duration from to Ternary Relationship lot dname budget did since name Works_In Departments Employees ssn Why?

39 39 ISA (`is a’) Hierarchies Contract_Emps name ssn Employees lot hourly_wages ISA Hourly_Emps contractid hours_worked v As in C++, or other PLs, attributes are inherited. v If we declare A ISA B, every A entity is also considered to be a B entity. v Overlap constraints : Can Joe be an Hourly_Emps as well as a Contract_Emps entity? ( Allowed/disallowed ) v Covering constraints : Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no) v Reasons for using ISA : –To add descriptive attributes specific to a subclass. –To identify entitities that participate in a relationship.

40 40 Aggregation v Used when we have to model a relationship involving (entitity sets and) a relationship set. – Aggregation allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships. –Monitors mapped to table like any other relationship set. budget did pid started_on pbudget dname until Departments Projects Sponsors Employees Monitors lot name ssn Aggregation

41 41 Real Database Design v Build an ER Diagram for the following information: –Walmart Stores u Store Id, Address, Phone # –Products u Product Id, Description, Price –Manufacturers u Name, Address, Phone # –Walmart Stores carry products u Amount in store –Manufacturers make products u Amount in factory/warehouses

42 42 Conceptual Design Using the ER Model v Design choices: –Should a concept be modeled as an entity or an attribute ? –Should a concept be modeled as an entity or a relationship ? –Identifying relationships: Binary or Ternary ? Aggregation ? Always follow the requirements.

43 43 Entity vs. Attribute v Should address be an attribute of Employees or an entity (connected to Employees by a relationship)? v Depends upon the use we want to make of address information, and the semantics of the data: u If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued). u If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modeled as an entity (since attribute values are atomic).

44 44 Entity vs. Attribute (Contd.) v Works_In2 does not allow an employee to work in a department for two or more periods. v Similar to the problem of wanting to record several addresses for an employee: we want to record several values of the descriptive attributes for each instance of this relationship. name Employees ssn lot Works_In2 from to dname budget did Departments dname budget did name Departments ssn lot Employees Works_In3 Duration from to

45 45 Entity vs. Relationship v First ER diagram OK if a manager gets a separate discretionary budget for each dept. –Redundancy of dbudget, which is stored for each dept managed by the manager. –Misleading: suggests dbudget tied to managed dept. v What if a manager gets a discretionary budget that covers all managed depts? Manages2 name dname budget did Employees Departments ssn lot dbudget since Employees since name dname budget did Departments ssn lot Manager Manages3 dbudget IsA

46 46 Binary vs. Ternary Relationships Requirements: v A policy not to be owned by more than one employee. v Every policy must be owned by some employee. v Dependents is a weak entity set. Each identified by pname +policyid age pname Dependents Covers name Employees ssn lot Policies policyid cost Bad design *

47 47 Binary vs. Ternary Relationships v If each policy is owned by just 1 employee: –Key constraint on Policies would mean policy can only cover 1 dependent! age pname Dependents Covers name Employees ssn lot Policies policyid cost Bad design Beneficiary age pname Dependents policyid cost Policies Purchaser name Employees ssn lot Better design *

48 48 Binary vs. Ternary Relationships (Contd.) v Previous example illustrated a case when binary relationships were better than one ternary relationship. v An example in the other direction: a ternary relation Teaches relates entity set Students, Professors and Courses, and has descriptive attributes term and year. No combination of binary relationships is an adequate substitute: –P teaches S, S takes C, P teaches C, do not necessarily imply that P indeed teaches S of C! –How do we record term and year?

49 49 Students ProfessorsCourses Teaches termyear ProfessorsCoursesStudents TeachesTakes termyeartermyear

50 50 Summary of Conceptual Design v Conceptual design follows requirements analysis, –Yields a high-level description of data to be stored v ER model popular for conceptual design –Constructs are expressive, close to the way people think about their applications. v Basic constructs : entities, relationships, and attributes (of entities and relationships). v Some additional constructs : weak entities, ISA hierarchies, and aggregation. v Note: There are many variations on ER model.

51 51 Summary of ER (Contd.) v Several kinds of integrity constraints can be expressed in the ER model: key constraints, participation constraints, and overlap/covering constraints for ISA hierarchies. Some foreign key constraints are also implicit in the definition of a relationship set. –Some constraints (notably, functional dependencies) cannot be expressed in the ER model. –Constraints play an important role in determining the best database design for an enterprise.

52 52 Summary of ER (Contd.) v ER design is subjective. There are often many ways to model a given scenario! Analyzing alternatives can be tricky, especially for a large enterprise. Common choices include: –Entity vs. attribute, entity vs. relationship, binary or n-ary relationship, whether or not to use ISA hierarchies, and whether or not to use aggregation. v Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful.

53 53 Example 1 Answer Students Name Login Id AgeGPA Courses Id NameCredit Enrolled_In Grade

54 54 Example 2 Answer Patients Name AddrPhoneAge Drugs Name ManufExp Prescribed Dosage #days


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