MIS3150 Data and Information Management Lecture 2 - Data and Process Modeling

1. MIS3150 Data and Information ManagementLecture 2 - Data and Process Modeling Arijit Sengupta

2. Structure of this semester MIS3150 1. Design 2. Querying 3. Advanced Topics 0. Intro 4. Applications Database Fundamentals Conceptual Modeling Query Languages Java DB Applications – JDBC Transaction Management Relational Model Advanced SQL Data Mining Normalization Newbie Users Designers Developers Professionals

3. Today’s Buzzwords • Data Modeling • Process Modeling • Data Flow Diagrams • Entity-Relationship Models • Cardinality and Participation Constraints • Weak Entities • Generalization Hierarchies

4. So, where are we? Proposal Requirements Analysis Normalization Modeling Schema design Design Tables Indexes Queries Optimization Implementation Testing Installation

5. Objectives of this lecture • Describe the process inherent in a system • Present a system process in a concise diagrammatic form • Describe the system data in terms of conceptual objects and relationships between them • Translate such conceptual descriptions into actual tables

6. Benefits of Conceptual Design • Projects without a strong conceptual design are more likely to fail • Design is one of the most important aspects of project and business process quality management standards: • ISO 9000 • CMM • Designs are typically network structured, not flat like databases • Literature in Relational Model shows Benefits of Conceptual Design in user performance

7. Database Modeling • Process Models • Overview of process components • Inputs and outputs of different processes • Data sources and destinations • Mode of data flow between processes • Data Models • Model only the data, no process • Different components of the data • Relationships between primary data components

8. Models, method, and media • A model • describes business or organization • separates operation from technology • Good modeling requires good methodologies • encompass data, process, decisions • richly expressive and provide for levels of analysis • simple representation • Modeling medium • both formal and visual

9. Data Flow medium • Notation: • Source: box • Process (transform): box with rounded corners • File (store): box open on right • Destination: box • Flow: arrow • Structure: • “Explosion” of processes (recursion on structure)

10. Data Flow Diagrams

11. DFD rules • Start with a very basic overview of complete process, showing only the most important processes, sources, destinations, and files • Recursively “explode” each of the processes (note: processes only!): • preserve inputs and outputs • preserve file accesses • new processes, files and sources/destinations can be created, but cannot be used from previous levels if not directly used in the previous level

12. Overview of Data Modeling • Conceptual design: (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? • What are the integrity constraints or business rules that hold? • A database `schema’ in the ER Model can be represented pictorially (ER diagrams). • Can map an ER diagram into a relational schema.

13. name ssn dob Employees ER Model Basics • Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. • Entity Set: A collection of similar entities. E.g., all employees. • All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!) • Each entity set has a key. • Each attribute has a domain.

14. Alternative Entity Representations Employee ------------------------ SSN Name Dob SSN Name Dob Employee Employee SSN Name Dob

15. since name dname ssn budget salary did Works_In Employees Departments name ssn salary Employees super-visor subor-dinate Reports_To ER Model Basics (Contd.) • Relationship: Association among two or more entities. E.g., Attishoo works in Pharmacy department. • Relationship Set: Collection of similar relationships. • An n-ary relationship set R relates n entity sets E1 ... En; each relationship in R involves entities e1 E1, ..., en En • Same entity set could participate in different relationship sets, or in different “roles” in same set.

16. Model this An auto repair shop provides services to vehicles brought in by customers. A customer may own multiple vehicles that they bring in for service. Each service request is assigned to a technician. A service consists of different jobs that are assigned fees. A service may need parts as well. The customer is given an invoice with details on all the fees and parts costs. • What should be modeled? • Which items should be modeled as entities? • Which items should be modeled as relationships? • Which items should be modeled as attributes?

17. A thumb rule to modeling • Major nouns become entities • Minor nouns become attributes • Verbs connecting major nouns become relationships

18. Major nouns in our passage?

19. Minor nouns in our passage?

20. Verbs in our passage?

21. ER model for our exercise

22. Business Rules • A department must have one and only one manager • A manager may manage multiple departments • An employee works in only one department • A department (of course) has many employees

23. Participation Constraints • 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). • Every did value in Departments table must appear in a row of the Manages table (with a non-null ssn value!) since since name name dname dname salary ssn did did budget budget 1,1 0,M Manages Employee Department 1,1 1,M Works_In since

24. Structural Constraints • Participation • Do all entity instances participate in at least one relationship instance? • Cardinality • How many relationship instances can an entity instance participate in? (min,max) (min,max) Participation Cardinality 0 -- Partial 1 -- one 1 -- Total (Mandatory) M -- more than one

25. Understanding P/C constraints 0:M 1:1 Employee manages Department 1:1 works_in 1:M John Accounting Mary Susan Sales Jack Peter Development Sally

26. Many-Many relationships Student Course takes 0:M 0:M John MIS415 Mary Susan MIS215 Jack Peter MIS345 Sally MIS490

27. Alternative Approaches • Arity approach • Crow’s foot approach (as in book) • Minmax approach • For this class, use ONLY the Participation-Cardinality approach – this is what will be used in assignments and exams

28. Back to our Auto Service Example • What are the participation/cardinality constraints of the relationships? • Owns - • Assigned to - • Consists of - • Needs part – • ?

29. Weak Entities • 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 (one owner, many weak entities). • Weak entity set must have total participation in this identifying relationship set. name cost pname salary age ssn Policy Dependents Employees 0:M 1:1

30. Point to ponder • Is there a weak entity in the auto service example?

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

32. Stop and think • Is there an IS-A hierarchy in the auto service example? • What would it do to the design?

33. Conceptual Design Using the ER Model • 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? • Constraints in the ER Model: • A lot of data semantics can (and should) be captured. • But some constraints cannot be captured in ER diagrams.

34. Entity vs. Attribute • Should addressbe an attribute of Employees or an entity (connected to Employees by a relationship)? • Depends upon the use we want to make of address information, and the semantics of the data: • If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued). • 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).

35. Converting model to design • Many-to-many relationships • Each entity becomes a table • The relationship becomes a table • PKs of entities becomes FKs in the relationship • Student( ) • Course( ) • Takes( ) Courseno Coursename Credits StudentID Name Class Major takes 0:M Course Student 0:M semester

36. Model to design (contd.) • 1-Many relationships • Entities become tables • Copy PK of multi-participant to single participant • Copy attributes of relationship to single participant (why?) Partno Type Make ComputerID Make Model Year 0:1 includes 1:M Part Computer installdate

37. Model to design (contd.) • 1-1 relationships • Entities can be merged, or • copy PK of any entity to the other • Generalization • Copy PK of parent entity to child entity as FK, as well as PK • Weak entities • Copy PK of controlling entity to weak entity as FK as well as part of PK

38. Lets convert our autoservice

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

40. Summary of ER (Contd.) • Several kinds of integrity constraints can be expressed in the ER model: key constraints, participationconstraints, 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.

41. Summary of ER (Contd.) • 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 • Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful.

42. Class Exercise Design an ER Model for a hospital system, with the following case description. Add other assumptions as needed. The hospital database stores data about patients, their admission and discharge from hospital’s departments and their treatments. For each patient, we know the name, address, sex, social security number. For each department we know the department’s name, its location, the name of the doctor who heads it, the number of beds available, and the number of beds occupied. A doctor may work in several departments, but may only be the head in one department. Each patient goes through multiple treatments during hospitalization; for each treatment we store its name, duration and the possible reactions to it that the patient may have. A treatment may have one or more follow-up treatments. Items to ponder: What other constraints can we apply on this model?