- Database: Shared collection of logically related data and a description of it, designed to meet the information needs of an organization.

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أساسيات قواعد بيانات 220) DataBase fundamentals (IS Lecture 1: Ch1 -Principles of DataBases- File-Based Systems: Collection of application programs that perform services for the end users. (e.

Incompatible file formats Solution? DATABASE Approach. - Data: know facts that can be recorded and have an implicit meaning. Data itself has no meaning without meatdata which describes data.

- Database: Shared collection of logically related data and a description of it, designed to meet the information needs of an organization.

1 أساسيات قواعد بيانات 220) DataBase fundamentals (IS Lecture 1: Ch1 -Principles of DataBases- File-Based Systems: Collection of application programs that perform services for the end users. (e.g: reports). - Each program defines and manages its own data. - Limitations (Disadvantages): 1. Separation and isolation of data 2. Duplication of Data 3. Data dependence 4. Incompatible file formats Solution? DATABASE Approach. - Data: know facts that can be recorded and have an implicit meaning. Data itself has no meaning without meatdata which describes data. - Information: the data you process in a manner that make it meaningful. So, Data is what you store in database. Information is what you retrieve from database. - Database: Shared collection of logically related data and a description of it, designed to meet the information needs of an organization. - Metadata (system catalog/data dictionary): the description of data, used to enable program-data independence. - Entity: a distinct object( person, place, thing ) in the organization that is to be represented in the database. - Attribute: is a property that describes some aspect of the object that we wish to record. - Relationship: is an association between entities. Database Management System (DBMS): a software system that enables users to create, maintain, and query the database. examples of DBMSs: Oracle, MS Access, MySQL Database Application: collection of data and programs that interacts with the database by issuing an appropriate request(typically an SQL statement) to the DBMS. Database System: The DBMS software together with the data itself. Sometimes, the applications are also included. (DB + DBMS + Application program) To manage large amounts of data Efficiently, reliably, securely and conveniently. Main Characteristics of the Database approach 1. Self-describing nature of a database system 2. Insulation between programs and data 3. Data Abstraction 4. Support of multiple views of the data 5. Sharing of data and multiuser transaction processing Transaction: executing program or process that includes one or more database accesses, such as reading or updating of DB record. View: Subset of the database. Allows each user to have his/her own view of database 1

Components of DBMS environment: 1. Hardware 2. Software 3. Data 4. Procedures 5.

- Database Designers: responsible for defining the content, the structure, the constraints, and functions or transactions against the database.

- Database administrations - End-users > Categories: Naive(queries/modified data) - Sophisticated.

- Restricting unauthorized access of data - Providing backup and recovery services - Providing multiple interfaces to different classes of users - Enforcing integrity constraints on the database

2 Components of DBMS environment: 1. Hardware 2. Software 3. Data 4. Procedures 5. People 1/17/1439 AH Role in the Database Environment - System Analyst: Determine the user requirements and develop the system specification. - Database Designers: responsible for defining the content, the structure, the constraints, and functions or transactions against the database. - Application Programmer - Implement programs meet the end user needs. - Test, debug, document, and maintain transactions. - Database administrations - End-users > Categories: Naive(queries/modified data) - Sophisticated. Advantages of using the Database Approach - controlling redundancy in data storage and in development and maintenance efforts. - Sharing of data among multiple users. - Restricting unauthorized access of data - Providing backup and recovery services - Providing multiple interfaces to different classes of users - Enforcing integrity constraints on the database Disadvantages of using the Database Approach - Complexity - Cost of DBMS - Additional hardware costs - Performance - Higher impact of a failure Lecture 2: Ch1 -DataBase Environment- Data abstraction: generally refers to the suppression of details of data organization and storage, and the highlighting of the essential features for an improved understanding of data. - it enables different users to perceive data at their preferred level of detail. Schema(intention): description of database. < like metadata. + Change rarely. - displays schema is called: Schema diagram. - We call each obj in the schema a schema construct. Instance(database state/extension): the actual database at any point of time. - Changes rapidly - When we initially load data into database, it said to move into initial state of database. - Each write operation changes the current state to new state. Goal of Three-level Architecture is to separate the user apps from the physical database. 2

1. The External or view Level - includes a number of external schemas or user views. (the ways users perceive data) - describes the part of database the is relevant to a particular user. 2.

- It concentrates on describing entities, data types, relationships, user operations, and constraints. 3.

Mapping: the processes of transforming requests and results between levels. Reasons for separation: 1. Each user should able to access the data, but have a different customized view of data. 2.

3 1. The External or view Level - includes a number of external schemas or user views. (the ways users perceive data) - describes the part of database the is relevant to a particular user. 2. Conceptual Level - It has a conceptual schema(logical structure of entire database) - Describes what data is stored in database and relationships among the data. - It concentrates on describing entities, data types, relationships, user operations, and constraints. 3. Internal Level - It has an internal schema(the way DBMS and OS perceive data) - Physical representation of the database on computer. - How the data stored in the database. Mapping: the processes of transforming requests and results between levels. Reasons for separation: 1. Each user should able to access the data, but have a different customized view of data. 2. DBA should be able to change the DB storage structure without affecting user s view. 3. The internal structure of database should be unaffected by changes to the physical aspects of storage, such as change to new storage device. Data Independence: the ability to modify a schema definition in one level without affecting a schema definition in the next higher level. - Three-level architecture provides Data Independence, which means that upper level are unaffected by changes to lower level. Kinds of data independence: - Logical Data independence - Refers to immunity of external schemas to changes in conceptual schema. - Conceptual schema changes -addition/removal of entities- should not require changes to external schema or rewrites of application programs. - Physical Data independence - Refers to immunity of conceptual schema to changes in the internal schema. - Internal schema changes -using different file organizations, storage structures/ devices- should not require change to conceptual or external schemas. Data Model: collection of concepts that can be used to describe the structure of a database. - Purpose > to represent data in an understandable way. Database Design: - Conceptual database design > to build the conceptual representation of the database, which includes identification of the important entities, relationships and attributes. - Logical database design > to translate the conceptual representation to the logical structure of the database, which includes designing the relations. - Physical database design > to decide how the logical The stages of database system development lifecycle 3

structure is to be physically implemented (as base relations) in the target database management system (DBMS).

- the conceptual data model includes ER and a data dictionary.

considerations. - Physical data design: process of producing a description of the implantation of the database on secondary storage. < tailored to a specific DBMS.

- Allowing child records to have multiple parents (M:N). 2. Hierarchical data model > each parent record can have many children, but each record has only one parent (1:M).

4 structure is to be physically implemented (as base relations) in the target database management system (DBMS). 1/17/1439 AH - Conceptual data design: the process of constructing a model of the data in an enterprise, independent of all physical considerations. - the conceptual data model includes ER and a data dictionary. - Logical data design: process off constructing a model of the data used in an enterprise based on a specific data model -relational-, but independent of particular DBMS and other physical considerations. - Physical data design: process of producing a description of the implantation of the database on secondary storage. < tailored to a specific DBMS. Classification or models of DBMSs: - First Generation: 1. Network data model > allowing a record to participate in multiple parent/ child relationships. - Allowing child records to have multiple parents (M:N). 2. Hierarchical data model > each parent record can have many children, but each record has only one parent (1:M). -Tree-like structure- - Disadvantages: 1. Required complex programs for even simple queries. 2. Minimal data independence. 3. No widely accepted theoretical foundations. - Second Generation: 1. Relational data model > computer database in which all data is stored in relations(tables with rows and columns). each table is composed of records(tuples) and each record is identified by a field(attribute with unique value). - Advantages: - Modifying data will be efficient. - Data retrieval will be efficient too. - Self-documenting. - Changes to the database schema easy to make. - Third Generation: - Object-oriented data model > Response to increasing complexity of DB applications. Lecture 3: Ch12 -Data modeling using ER Model- ER(Entity-Relationship) model: is a popular high-level conceptual data model. - Based on real world objects called entities and relationships between them. - it can be directly translate to relations (tables). ERD(Entity-Relationship Diagram): is a graphical model for representing conceptual data model. using three elements: Entities, attributes, and relationships. 1. Entity: real thing with an independent existence. could be: - physical existence (car, student) - conceptual existence (company, course). 2. Attribute: property describing an entity. eg: student name, ID, age 4

Types of Attributes: Simple: single atomic value with an independent existence. Single-valued Stored 1/17/1439 AH Composite: multiple components.

-Can t be NULL- - there could be more than one; if so, we select one as primary key and the other an alternate key. Primary key: the selected key that uniquely identify and entity.

5 Types of Attributes: Simple: single atomic value with an independent existence. Single-valued Stored 1/17/1439 AH Composite: multiple components. Multi-valued Derived: represents a derivable value from a related attribute. Keys of Entity: Candidate key: is minimal set of attributes whose values uniquely identify and entity. -Can t be NULL- - there could be more than one; if so, we select one as primary key and the other an alternate key. Primary key: the selected key that uniquely identify and entity. - indicated with underline- we choose PK based on: - Attribute length - Number of attributes required - Certainty of uniqueness. PK could be: - simple > single attribute - composite > multiple attributes. 3. Relationship: association between entities. -represented by diamonds- - it could have attributes. Degree of relationships: - Unary(recursive) between one entity - Binary between two entities - N-ary more than two. e.g.: 3 entities > ternary. Role: purpose that each participating entity type plays in a relationship. Types of constraints on relationships: Cardinality ratio (number of relationship an entity can participate in) Participation (all or only some entities participate in a relationship) 1:1 Mandatory(total): requires (every, should, must) represented by double lines 1:M - M:1 Optional(Partial): doesn t required (not every, may, could) represented by single line M:N Multiplicity: number(range) of possible entities that may relate to a single association through a particular relationship. (min, max) - * = many - - Strong entity: have a key attribute. (independent) - Weak entity: no key attribute. (depends on some other entity) - -represented by double line- - weak entity in weak relationships is always mandatory. 5

Lecture 4: Ch3 -Mapping ERD to Relations- Logical (relational) model represents database as relations(tables). - Tuple: row of relation. - Degree of relation: number of attributes in it.

6 Lecture 4: Ch3 -Mapping ERD to Relations- Logical (relational) model represents database as relations(tables). - Tuple: row of relation. - Degree of relation: number of attributes in it. - Cardinality: number of tuples in it. - Relational schema: describes a relation(r). Denoted by: R(A1,A2,,An). Relational Keys: - Candidate Key (CK) - Primary Key(PK) - Foreign Key(FK): attribute(s) in a relation that matches the CK of some relation. (relationships between two relations). - Relations should have as few NULL values as possible, if there are frequents NULL values, we can separate relations with PK. - NULL values: - Unknown value - Unavailable value - Not applicable attribute\ Data redundancy: data field appear more than once in the database. - Disadvantages: weak maintaining of DB + waste of memory. Mapping next page > 6

Mapping strong entity type Mapping Multivalued attributes Mapping weak entities 1 relation with attributes + PK composite > only components Derived > omitted 2 relations: 1.

7 Mapping strong entity type Mapping Multivalued attributes Mapping weak entities 1 relation with attributes + PK composite > only components Derived > omitted 2 relations: 1. all attributes except multivalued + PK 2. PK of 1st relation as FK + multivalued attribute 1/17/1439 AH 2 relations: 1. all attributes of 1st entity + PK 2. all attributes of weak entity + Key + PK of 1st relation as FK Mapping Binary relationships M:N Mapping Binary relationships 1:M Mapping Binary relationships 1:1 3 relations: 1. all attributes of 1st entity + PK 2. all attributes of 2nd entity + PK 3. PKs of entities as FKs + Relationship attribute 2 relations: 1. all attributes of (one side) entity + PK 2. all attributes of (many side) entity + PK of (one side) as FK + relationship attribute - Mandatory/optional on both sides: 2 relations: 1. all attributes of 1st entity + PK 2. all attributes of 2nd entity + PK of 1st as FK + relationship attribute ^ opposite is fine - Mandatory on one side 2 relations: 1. all attributes of optional side entity 2. all attributes of mandatory side entity + PK of optional side as FK + relationship attribute Mapping Unary relationships 1:M Mapping Unary relationships M:N Mapping Unary relationships 1:1 1 relation: 1. PK + copy of PK as FK + relationship attribute 2 relations: 1. all attributes of entity + PK 2. PK as FK + copy of PK as FK + relationship attribute - Mandatory on both sides: < 1:M rules - Optional on both sides: < M:N rules - Mandatory on one side: either of two methods 1:M/M:N Mapping N-ary relationships n relations: (n = number of entities) 1. all attributes of entity + PK 2. PKs as FKs + relationship attribute \ 7

Lecture 5: Ch14-15 -FDs and Normalization- Functional dependencies: describes the relationship between attributes in a relation.

-series of tests- - Purpose: Guarantees no redundancy due to FDs. Guarantees no update anomalies. Normal forms: UNF(unnormalized form): relation that contains one or more repeating groups.

2 relation: 1. all attributes except multivalued. 2.

8 Lecture 5: Ch FDs and Normalization- Functional dependencies: describes the relationship between attributes in a relation. - Purpose: specify the set of integrity constraints that must hold on a relation. Normalization: bottom-up approach to DB design that begins by examining the relationships between attributes. -series of tests- - Purpose: Guarantees no redundancy due to FDs. Guarantees no update anomalies. Normal forms: UNF(unnormalized form): relation that contains one or more repeating groups. 1NF 2NF 3NF - All attributes depends on the key - intersection of each row & col contains one & only one value. - Disallows: 1. Multivalued attributes 2. nested relations (repeating group) UNF > 1NF? 2 relation: 1. all attributes except multivalued. 2. PK of 1st relation + multivalued -both becomes PK- - All attributes depends on the whole key (Full functional dependency) - Disallows: partial dependency 1NF > 2NF? - 1NF - remove partial dependency by moving in a new relation with copy of determinants. - All attributes depends on nothing but the key - Disallows: Transitive functional dependency 1NF > 2NF? - 1NF - 2NF - place transitive dependent attributes in new relation with copy of determinants. 8

Lecture 6: Ch6 -SQL overview, oracle date type, DDL and constraint-

Data Definition Language(DDL): allows DBAs/users to describe and

operations on data held in the database (insertion, modification,

user to tell system how to manipulate data (OracleSQL) -

create privileges to allow users access to the database.

relational database management systems such as: Oracle.

learn + case insensitive - non-procedural language Table creation

9 Lecture 6: Ch6 -SQL overview, oracle date type, DDL and constraint- Database Languages: 1. Data Definition Language(DDL): allows DBAs/users to describe and name entities, attributes and relationships. (define a schema or modify it) 2. Data Manipulation Language(DML): provides basic data manipulation operations on data held in the database (insertion, modification, retrieval, deletion) Two types of DML: - Procedural DML > allows user to tell system how to manipulate data (OracleSQL) - Non-Procedural DML > allows user to state what data is needed (SQL query) 3. Data Control Language(DCL): component of database language used to create privileges to allow users access to the database. SQL overview: - stands for Structured Query Language - standard for relational database management systems such as: Oracle. - All SQL statements are instructions to the database - Easy to learn + case insensitive - non-procedural language Table creation NOT NULL constraint *column level only* UNIQUE constraint DEFAULT constraint PRIMARY KEY constraint FOREIGN KEY constraint CHECK constraint DDL: Referential integrity constraint: ON DELETE/UPDATE CASCADE ON DELETE/UPDATE SET NULL ON DELETE/UPDATE SET DEFAULT ^ follows foreign key constraint. 9

Lecture 7: Ch6 -SQL overview, oracle date type, DDL and constraint-

column delete column modify column add constraint remove constraint

Ch4,5-Relational Algebra, Data Manipulation in SQL, Simple SQL

records) to produce a relation as result.

Join - Intersection - Set difference - Division.

meet specified condition to result in a new relation.

attributes list out of R to result in a new relation. DML cont.

10 Lecture 7: Ch6 -SQL overview, oracle date type, DDL and constraint- ALTER table (add/delete/modify columns, add/remove constraint) add column delete column modify column add constraint remove constraint enable/disable constraint drop table view table Lecture 8: Ch4,5-Relational Algebra, Data Manipulation in SQL, Simple SQL queries- Relational Algebra: operations on relations(sets of records) to produce a relation as result. output from one operation can become input to another operation (nested relational algebra). 1. Unary operations: (works on one relation) < -examples only on unary- I. Selection II. Projection 1. Binary operations:(pair of relations) Cartesian product - Union - Join - Intersection - Set difference - Division. Selection Select[cond](R) selects tuples out of relation R that meet specified condition to result in a new relation. Projection Project[attr-list](R) selects columns specified in the attributes list out of R to result in a new relation. DML cont. in next page> INSERT INTO -add rows- SELECT (column)/(*) -only for outputs, no change in the actual data- UPDATE -update records in table- DELETE -delete records with rollback- TRUNCATE -no rollback and empties memory space- 10

WHERE clause (condition) - Comparison (=,<,>,<>,<=,>=), AND/OR/NOT - Range (BETWEEN/NOT BETWEEN) - Set

NULL/ IS NOT NULL 1/17/1439 AH DISTINCT (removes duplicate values) ORDER BY (ascending/descending order)

SQL queries- - Table and Column names can be prefixed with owner s name.

product; - Column names can be prefixed with table name: SELECT product.

11 WHERE clause (condition) - Comparison (=,<,>,<>,<=,>=), AND/OR/NOT - Range (BETWEEN/NOT BETWEEN) - Set membership (IN/NOT IN) - Pattern match (LIKE/NOT LIKE) > % 0 or more characters, _ single character - IS NULL/ IS NOT NULL 1/17/1439 AH DISTINCT (removes duplicate values) ORDER BY (ascending/descending order) Lecture 9 is after 10 -sorry for the inconvenient- Lecture 10: Ch5 -Data Manipulation in SQL, Advances SQL queries- - Table and Column names can be prefixed with owner s name. for example: table product owned by John: SELECT * FROM John.product; - Column names can be prefixed with table name: SELECT product.prodno FROM product; Alias: temporarily rename a table or column heading. Join: if two or more tables are needed at the same time to find all required data (joined together). - it computes new table from joined ones. 11

there are no matches in the right table. C.

there are no matches in the left table. D.

two table -more examples in slides- - Outer Joins in

-where nulls may be added it s placed in the join

12 1/17/1439 AH - Types of JOIN: A. Inner join: return rows when there is at least one match in both tables. B. Left join: return all rows from the left table even if there are no matches in the right table. C. Right join: return all rows from the right table even if there are no matches in the left table. D. Full outer join: retains rows that are unmatched in both the tables. Note: all outer joins, the displayed unmatched column filled with NULLs. - Examples of JOIN >> Simple join (two tables) more than two table -more examples in slides- - Outer Joins in Oracle SQL: put (+) on the potentially deficient side -where nulls may be added it s placed in the join condition next to the table that is allowed to have NULL values. Note: table may be outer joined with only one other table. - More examples: Nested Queries: 1. query results are tables, which can also be queried. - inner query referred to as subquery. 2. If query result is a single value, it can be treated as a value, and be compared with other values. 12

- EXIST returns to there exist at least one

subquery, it s false if subquery returns an

and eliminates any duplicate rows from the

13 Queries using EXISTS/ NOT EXISTS: - designed only for subqueries. - EXIST returns to there exist at least one row in the result table returned by the subquery, it s false if subquery returns an empty result table. 1/17/1439 AH Union: used to combine result-set of two or more SELECT statements and eliminates any duplicate rows from the result. - SELECT statement with UNION must: 1. have the same number of columns. 2. columns must have same data type. 3. columns in each SELECT statement must be in the same order. - to allow duplicate values, use UNION ALL. Minus: returns only unique rows returned by the first query not the second. Intersect: returns only those rows returned by the two queries. 13

Lecture 9: Ch5 -Data Manipulation in SQL, Advances SQL queries- Aggregate

total of numeric column Avg : return average of numeric column

can use distinct to omit duplicated values.

- if WHERE is used with GROUP BY, WHERE is applied first, then groups are

HAVING: - designed to be used with GROUP BY to restrict groups.

table if you have two tables that has same structure you can use it.

14 Lecture 9: Ch5 -Data Manipulation in SQL, Advances SQL queries- Aggregate functions: Count: return number of values(without nulls) Sum: return total of numeric column Avg : return average of numeric column Min(smallest value), Max(larges value) - we can use * for all table - we can use distinct to omit duplicated values. Advanced queries -GROUP BY-: - used in conjunction with aggregate functions to group the result-set. - use group by clause to get sub-totals. - if WHERE is used with GROUP BY, WHERE is applied first, then groups are formed from rows satisfying condition. HAVING: - designed to be used with GROUP BY to restrict groups. Inserting data using queries: - you can insert a result of at query into table if you have two tables that has same structure you can use it. SQL data loader: - for a table containing a large data set, INSERT command is not efficient. - oracle provides a data loader utility SQLLoader. - data can be loaded from any text file inserted to database. - but table must be created first. 14

Lecture 11: Ch5 -Transaction control language, data control language- Transaction control Language(TLC): used to control transactional processing in database. - Commands: A.

15 Lecture 11: Ch5 -Transaction control language, data control language- Transaction control Language(TLC): used to control transactional processing in database. - Commands: A. COMMIT: to save changes permanently in the database. B. ROLLBACK: to undo all changes of a transactions. C. SAVEPOINT: creates points within groups of transactions on which to rollback. Savepoint Commit Rollback - names and marks the current point in the processing of a transaction. - if you want to make your update permanent. - used to save changes invoked by a transaction to the database. - erases any savepoints you marked since last commit. - if you change your mind or need to correct a mistake. - inverse of COMMIT statements. - during current transaction. Data Control Language(DCL): component of the SQL language is used to create privileges to allow users access to, and manipulation of the database. two main commands: Grant - in order to log onto the database you must be given appropriate privileges. - SQL command to grant privilege on a table: Revoke - remove a privilege from a user. - SQL command to revoke privilege on a table: - any combination is allowed. - you can issue this on any table you created. - example: - example: الحمدلل ه الذي بنعمته تتم الصالحات. الل هم عل منا ماينفعنا وانفعنا بما علمتنا وزدنا اللهم علم ا 15

Lecture2: Database Environment

College of Computer and Information Sciences - Information Systems Dept. Lecture2: Database Environment 1 IS220 : D a t a b a s e F u n d a m e n t a l s Topics Covered Data abstraction Schemas and Instances