MIS710 Module 1a Data and Process Modeling

1. MIS710 Module 1aData and Process Modeling Arijit Sengupta

2. Structure of this semester MIS710 1. Design 2. Querying 4. Advanced Topics 0. Intro 3. 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. Motivation - why model? • If you cannot model, you cannot comprehend, and if you cannot comprehend, you cannot control • Dual goal: • Analysis and conceptualization • Presentation

9. 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 • same term as painting medium, e.g., oil, pastel • both formal and visual

10. 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)

11. Data Flow Diagrams

12. 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

13. 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.

14. name ssn lot 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.

15. name ssn lot Employees since name dname super-visor subor-dinate ssn budget lot did Reports_To Works_In Employees Departments 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. 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 ssn did did budget budget lot 1,M 0,M Departments Employees Manages 1,1 1,M Works_In since

17. 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

18. 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 age ssn lot Policy Dependents Employees

19. 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

20. 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.

21. 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).

22. 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

23. 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

24. 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 • Weak entities • Copy PK of controlling entity to weak entity

25. 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.

26. 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.

27. 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.