Introduction. Database Management Systems

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1 Introduction Database Management Systems

2 Database Management System (DBMS) Collection of interrelated data and set of programs to access the data Convenient and efficient processing of data Database Application Software Farkas CSCE 824 2

3 Evolution of Database Systems Early days: customized applications built on top of file systems Drawbacks of using file systems to store data: Data redundancy and inconsistency Difficulty in accessing data Atomicity of updates Concurrency control Security Data isolation multiple files and formats Integrity problems Farkas CSCE 824 3

4 Abstraction View level: different perspectives application programs hide irrelevant data Logical level: data models Logical representation of data Different approaches: relational, hierarchical, network, object oriented, semi-structured, etc. Data independence principle Physical level: how data is stored Farkas CSCE 824 4

5 Data Models A collection of tools for describing Data Relationships among data items Semantics of stored data Database constraints Entity-Relational Model UML Etc. Farkas CSCE 824 5

6 Database Management Systems Smaller and smaller systems Past: large and expensive DBMS Present: DBMS in most personal computers More and more data stored Past: few MB Present: terabyte (10 12 bytes), petabyte (10 15 bytes) Functionality: from physical to view level Optimization Farkas CSCE 824 6

7 Data Definition Language (DDL) Defines the database schema and constraints DDL compiler data dictionary Metadata data about data Farkas CSCE 824 7

8 Data Manipulation Language (DML) Accessing and manipulating the data Query Languages Procedural user specifies what data is required and how to get those data Nonprocedural user specifies what data is required without specifying how to get those data Farkas CSCE 824 8

9 MODELING DATA SEMANTICS Farkas CSCE 824 9

10 Entity Sets and Relationship Sets Database: collection of entities and relationship among entities Entity: object that exists and distinguishable from other objects Entity set: collection of similar objects Attribute: property of an entity and relationship sets Farkas CSCE

11 Attributes Domain: set of permitted values for each attributes Attribute types: Simple vs. composite Single-valued vs. multi-valued Derived Farkas CSCE

12 Example E/R Diagram Name Breed Name License # Age Dog Boards Kennel Address Weight Owns Pays Phone Owner Name Phone Farkas CSCE

13 Degree of Relationship Sets Number of entity sets participating in a relationship set Binary relationship set: two entity sets (most common) Multiway relationship set: connects more than two entity sets E.g., An owner frequents certain kennels for certain dogs Binary relationship can t represent these requirements Need 3-way relationship Farkas CSCE

14 Example 3-Way Diagram Name Breed Name License # Age Dog Kennel Address Weight Must Board Phone Owner Name Phone Farkas CSCE

15 Mapping Cardinality Number of entities to which another entity can be associated via a relationship set One-one Many-one (One-many) Many-many Farkas CSCE

16 Roles Entity set may appear more then once in a relationship Label the edges between the relationships and the entity set with names called roles. E.g., relationships among the dogs: Dog ancestor descendant Related to Farkas CSCE

17 Subclasses in E/R Special case, fewer entities, more properties E.g., show dog is a dog, but not all dogs are show dogs. It also have properties, type of competition, rank, etc. Assume subclasses form a tree (no multiple inheritance) ISA relationship Farkas CSCE

18 Example Subclass Age Name Weight Dog Breed Special case, fewer entities, more properties ISA Subclasses form a tree (no multiple inheritance) Show-Dog Type Rank Farkas CSCE

19 Keys Set of attributes for one entity set such that no two entities in the set has the same value for all the attributes of the key Each entity set must have a key Farkas CSCE

20 Keys Super key: set of one or more attributes whose value iniquely determine each entity Candidate key: minimal super key Primary key: a selected candidate key Farkas CSCE

21 Example Multi-Attribute Key for Owner Owner There are more than One person with the Same name, but they Live at different Places. Name Address Phone Farkas CSCE

22 Converting ER Model into Relations Farkas CSCE

23 Example Relation Dog Name Age Weight Breed (lb) Pepper German Shepherd Joker 4 83 Mix Bruno null 51 Boxer Farkas CSCE

24 Relational Data Model Set of relation names: R Set of attribute names: A Relation schema: S=(r,{a 1,,a n }) r relation name in R {a 1,,a n } subset of A e.g., (Dog,{Name, Age, Weight, Breed}) Farkas CSCE

25 Relational Data Model Tuple over a relation scheme S is a mapping t: {a 1,,a n } dom(a 1 a n ) e.g., t(dog-name)=pepper t(dog-age)=10 t(dog-weight)= 75 t(dog-breed)=german Shepherd Farkas CSCE

26 Relational Data Model Relation over schema S is a set of tuples over the scheme Database: set of relations Farkas CSCE

27 Query Languages Relational Algebra Set operations SQL Bag operations Farkas CSCE

28 Relational Algebra Select (σ) Project (Π) Set difference (-) Union ( ) Rename (Px(r)) Set intersection ( ) Natural join ( ) Farkas CSCE

29 Structured Query Language SQL Typical SQL query form: SELECT A 1, A 2,..., A n FROM r 1, r 2,..., r m WHERE C A i s represent attributes to be returned r i s represent relations C is a condition Farkas CSCE

30 Next Class Overview of Information Security (from CSCE 522 slides) Farkas CSCE

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