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  1. 1. ELE275 (COMPUTER APPLICATIONS I) CONTENTS  Identification, functions, applications, and use of PC parts and peripheral devices.  Safety Precautions and Preventive Maintenance of PC.  Filling system: Word processing applications and use.  Internet: available services, principle of operation, applications and demonstrations.  Spreadsheet: Applications, how to use. (Microsoft Excel)  Database Management Package: Applications, demonstrations.  Report presentation Software Packages:, applications, demonstrations and use. (Microsoft PowerPoint)  Mini-project to test proficiency in the use of the packages. ELECTRICAL AND ELECTRONICS ENGINEERING DEPARTMENT, UNIVERSITY OF ILORIN, ILORIN
  2. 2. LECTURE 02 Database Management Package: Applications, demonstrations. OLAYANJU S. A.
  3. 3. Introduction  Today, more than at any previous time, the success of an organization depends on: • its ability to acquire accurate and timely data about its operations, • managing this data effectively, • using it to analyze and guide its activities.  Information processing is a rapidly growing multibillion dollar industry.  The amount of information available to us is literally exploding.  The value of data as an organizational asset is widely recognized.  Yet without the ability to manage this vast amount of data, and to quickly find the information that is relevant to a given question, as the amount of information increases, it tends to become a distraction and a liability, rather than
  4. 4. Introduction Contd.  This paradox drives the need for increasingly powerful and flexible data management systems.  To get the most out of their large and complex datasets, users must have tools that simplify the tasks of managing the data and extracting useful information in a timely fashion.  Otherwise, data can become a liability, with the cost of acquiring it and managing it far exceeding the value that is derived from it.
  5. 5. History of DBMS  From the earliest days of computers, storing and manipulating data have been a major application focus.  The first general-purpose DBMS was designed by Charles Bachman at General Electric in the early 1960s and was called the Integrated Data Store.  It formed the basis for the network data model, which was standardized by the Conference on Data Systems Languages (CODASYL) and strongly influenced database systems through the 1960s.  In the late 1960s, IBM developed the Information Management System (IMS) DBMS, used even today in many major installations.  IMS formed the basis for an alternative data representation framework called the hierarchical data model.
  6. 6. History Contd. • In 1970, Edgar Codd, at IBM's San Jose Research Laboratory, proposed a new data representation framework called the relational data model. This proved to be a watershed in the development of database systems: it sparked rapid development of several • Database systems matured as an academic discipline, and the popularity of relational DBMSs changed the commercial landscape. • Their benefits were widely recognized, and the use of DBMSs for managing corporate data became standard practice. • In the 1980s, the relational model consolidated its position as the dominant DBMS paradigm, and database systems continued to gain widespread use. • The SQL query language for relational databases, developed as part of IBM's System R project, is now the standard query language.
  7. 7.  DATA could mean: • A fact or more specially a value of attribute of an entity. • Raw material of Information in a form which can be processed by a computer. • A collection of facts and figures that can be processed to produce information.  An entity in general, may be a person, an object, idea, event, condition or situation.  An entity is represented by set of attributes. An attribute is also referred to as data item, data element , data field  An entity set is a set of entities of the same type that share the same properties or attributes .  A set of attributes describes an entity.  For example : In a library environment, Entity Set: Catalogue - Entity: Books, Journals, Thesis, etc Attributes: contains Author, Title, Imprint, ISBN, Publisher, etc
  8. 8.  A database is an organized collection of related data stored with minimum redundancy, in a manner that makes them accessible for multiple application  A database management system (DBMS) is a software system that allows access to data contained in a database.  The objective of the DBMS is to provide a convenient and effective method of defining, storing and retrieving the information contained in the database.  The DBMS interfaces with the application programs, so that the data contained in the database can be used by multiple applications and users.  In addition, the DBMS exerts centralized control of the database, prevents fraudulent or unauthorized users from accessing the data, and ensures the privacy of the data  Characteristics of DBMS • Real-world entity: A modern DBMS is more realistic and uses real-world entities to design its architecture. For example, a school database may use students as an entity and their age as an attribute.
  9. 9. • Relation-based tables: DBMS allows entities and relations among them to form tables. • Isolation of data and application: A database system is entirely different than its data. A database is an active entity, whereas data is said to be passive, on which the database works and organizes. • Less redundancy: DBMS follows the rules of normalization, which splits a relation when any of its attributes is having redundancy in values. Normalization is a mathematically rich and scientific process that reduces data redundancy. • Consistency: a state where every relation in a database remains consistent. There exist methods and techniques, which can detect attempt of leaving database in inconsistent state. • Query Language: DBMS is equipped with query language, which makes it more efficient to retrieve and manipulate data. A user can apply as many and as different
  10. 10. • Multiuser and Concurrent Access: DBMS supports multi-user environment and allows them to access and manipulate data in parallel. • Multiple views: DBMS offers multiple views for different users. A user who is in the Sales department will have a different view of database than a person working in the Production department. This feature enables the users to have a concentrate view of the database according to their requirements. • Security: Features like multiple views offer security to some extent where users are unable to access data of other users and departments. DBMS offers methods to impose constraints while entering data into the database and retrieving the same at a later stage.
  11. 11. DBMS USERS Administrators: • Maintain the DBMS and are responsible for administrating the database. • Are responsible to look after its usage and by whom it should be used. • Create access profiles for users and apply limitations to maintain isolation and force security. • Also look after DBMS resources like system license, required tools, and other software and hardware related maintenance. Designers: • People who actually work on the designing part of the database. • They keep a close watch on what data should be kept and in what format. • They identify and design the whole set of entities, relations, constraints, and views. End Users:
  12. 12. ARCHITECTURE  The design of a DBMS depends on its architecture.  It can be centralized, decentralized or hierarchical.  The architecture of a DBMS can be seen as either single tier or multi-tier.  An n-tier architecture divides the whole system into related but independent n modules, which can be independently modified, altered, changed, or replaced.  In1-tier architecture, the DBMS is the only entity where the user directly sits on the DBMS and uses it. • Any changes done here will directly be done on the DBMS itself.  If the architecture of DBMS is 2-tier, then it must have an application through which the DBMS can be accessed. • Programmers use 2-tier architecture where they access the DBMS by means of an application. • Here the application tier is entirely independent of the database in terms of operation, design, and programming.
  13. 13. 3 - tier Architecture A 3-tier architecture separates its tiers from each other based on the complexity of the users and how they use the data present in the database. Database (Data) Tier: • At this tier, the database resides along with its query processing languages. • We also have the relations that define the data and their constraints at this level. Application (Middle) Tier: • At this tier reside the application server and the programs that access the database. • The application layer sits in the middle and acts as a mediator between the end-user and the database. User (Presentation) Tier: • End-users operate on this tier At this layer, multiple views of the database can be provided by the application. • All views are generated by applications that reside in the application tier.
  14. 14. DATA MODELS  Data models define how: • the logical structure of a database is modeled. • data is connected to each other • they are processed and stored inside the system.  Data Models are fundamental entities to introduce abstraction in a DBMS. Types A. Entity-Relationship Model Entity-Relationship (ER) Model is based on the notion of real- world entities and relationships among them. While formulating real-world scenario into the database model, the ER Model creates entity set, relationship set, general attributes, and constraints. ER Model is best used for the conceptual design of a database. ER Model is based on: Entities and their attributes. Relationships among entities.
  15. 15. ER Model
  16. 16.  Entity: An entity in an ER Model is a real-world entity having properties called attributes. Every attribute is defined by its set of values called domain. For example, in a school database, a student is considered as an entity. Student has various attributes like name, age, class, etc. Relationship: The logical association among entities is called relationship. Relationships are mapped with entities in various ways. Attributes There exists a domain or range of values that can be assigned to attributes. For example, a student's name cannot be a numeric value. It has to be alphabetic. A student's age cannot be negative, etc. Types of Attributes Simple attribute: Simple attributes are atomic values, which cannot be divided further. For example, a student's phone number is an
  17. 17. Derived attribute: The attributes that do not exist in the physical database, but their values are derived from other attributes present in the database. For example, age can be derived from date of birth. Single-value attribute: Contain single value. For example: account number. Multi-value attribute: May contain more than one values. For example, a person can have more than one phone number, Email address, etc. Entity-Set and Keys Key is an attribute or collection of attributes that uniquely identifies an entity. (E.g. a student’s matric number.) among entity set. • Super Key: A set of attributes (one or more) that collectively identifies an entity in an entity set. • Candidate Key: A minimal super key is called a candidate key. An entity set may have more than one candidate key. • Primary Key: A primary key is one of the candidate keys chosen by the database designer to uniquely identify the entity
  18. 18. ER DIAGRAM REPRESENTATION Entities are represented by means of rectangles. Rectangles are named with the entity set they represent. Entities in a school database Attributes are the properties of entities. Attributes are represented by means of ellipses. Every ellipse represents one attribute and is directly connected to its entity. Simple attributes of an entity. STUDENTS TEACHERS PROJECTS STUDENTS MATRIC. N0. NAME LEVEL
  19. 19. If the attributes are composite, they are further divided in a tree like structure. Every node is then connected to its attribute. That is, composite attributes are represented by ellipses that are connected with an ellipse. Composite attributes of an entity. STUDENTS MATRIC. N0. NAME LEVEL FIRST NAME LAST NAME
  20. 20. Multivalued attributes are depicted by double ellipse. STUDENTS MATRIC. N0. NAME LEVEL FIRST NAME LAST NAME PHONE N0.
  21. 21. Derived attributes are depicted by dashed ellipse. STUDENTS MATRIC. N0. NAME LEVEL FIRST NAME LAST NAME PHONE N0. AGE
  22. 22. Relationship Relationships are represented by diamond-shaped box. Name of the relationship is written inside the diamond-box. All the entities (rectangles) participating in a relationship are connected to it by a line. Binary Relationship and Cardinality A relationship where two entities are participating is called a binary relationship. Cardinality is the number of instance of an entity from a relation that can be associated with the relation. • One-to-one: When only one instance of an entity is associated with the relationship, it is marked as '1:1'. The following image reflects that only one instance of each entity should be associated with the relationship. It depicts one-to-one relationship. • 1 1 Entity Relationshi p Entity
  23. 23. One-to-many: When more than one instance of an entity is associated with a relationship, it is marked as '1:N'. 1 N Many-to-one: When more than one instance of entity is associated with the relationship, it is marked as 'N:1'. N 1 Many-to-Many: more than one instance of an entity on the left and more than one instance of an entity on the right can be associated with the relationship. It depicts many-to-many relationship. Entity Relationshi p Entity Entity Relationshi p Entity Entity Relationshi p Entity
  24. 24. Participation Constraints Total Participation: Each entity is involved in the relationship. Total participation is represented by double lines. Partial participation: Not all entities are involved in the relationship. Partial participation is represented by single lines. Generalization The process of generalizing entities, where the generalized entities contain the properties of all the generalized entities, is called generalization. In generalization, a number of entities are brought together into one generalized entity based on their similar characteristics. For example, pigeon, house sparrow, crow, and dove can all be generalized as Birds. Entity Relationshi p Entity Total Participation Partial Participation
  25. 25. Specialization The opposite of generalization. In specialization, a group of entities is divided into sub-groups based on their characteristics. The details of entities are generally hidden from the user; this process known as abstraction. Inheritance is an important feature of Generalization and Specialization. It allows lower-level entities to inherit the attributes of higher-level entities.
  26. 26. B. Relational Model: A very simple and elegant model; here, a database is a collection of one or more relations, where each relation is a table with rows and columns.  This simple tabular representation enables even novice users to understand the contents of a database.  It permits the use of simple, high-level languages to query the data.  The major advantages of the relational model over the older data models are: • its simple data representation • the ease with which even complex queries can be expressed. Concepts Tables: In relational data model, relations are saved in the format of Tables. This format stores the relation among entities. A table has rows and columns, where rows represent records and columns represent the attributes. Tuple: A single row of a table, which contains a single record for
  27. 27. Relational Model MATRIC. NO. NAME AGE LEVEL DEPARTMENT PHONE N0. 13/30GC297 Trump 29 500 ELECTRICAL 0801234567 8 14/30GD876 Macron 26 400 MECHANICAL 0901234567 8 15/30GP765 Markel 24 300 BIOMEDICAL 0701234567 8 16/30GQ612 Buhari 22 200 WATER 0811234567 8 17/30GM531 Atiku 20 100 CHEMICAL 0911234567 8 Attributes Column Tuple
  28. 28. Relation instance: A finite set of tuples in the relational database system represents relation instance. Relation instances do not have duplicate tuples. Relation schema: A relation schema describes the relation name (table name), attributes, and their names. Relation key: Each row has one or more attributes, known as relation key, which can identify the row in the relation (table) uniquely. Attribute domain: Every attribute has some predefined value scope, known as attribute domain. Constraints Every relation has some conditions that must hold for it to be a valid relation. These conditions are called Relational Integrity Constraints. There are three main integrity constraints: Key constraints Domain constraints Referential integrity constraints
  29. 29. Key Constraint: There must be at least one minimal subset of attributes in the relation, which can identify a tuple uniquely. • In a relation with a key attribute, no two tuples can have identical values for key attributes. • a key attribute cannot have NULL values. Key constraints are also referred to as Entity Constraints. Domain Constraints Attributes have specific values in real-world scenario. For example, age can only be a positive integer. For example, age cannot be less than zero and telephone numbers cannot contain a digit outside 0-9. Referential Integrity Constraints Referential integrity constraints work on the concept of Foreign Keys. A foreign key is a key attribute of a relation that can be referred in other relation. Referential integrity constraint states that if a relation refers to a key attribute of a different or same relation, then that key element must exist.
  30. 30. The main highlights of this model are: • Data is stored in tables called relations. • Relations can be normalized. • In normalized relations, values saved are atomic values. • Each row in a relation contains a unique value. • Each column in a relation contains values from a same domain.
  31. 31. RELATIONAL ALGEBRA A procedural query language, which takes instances of relations as input and yields instances of relations as output. It uses operators to perform queries. The fundamental operations of relational algebra are as follows: Select Project Union Set different Cartesian product Rename Select Operation (σ) It selects tuples that satisfy the given predicate from a relation. Notation: σp(r) Where σ stands for selection predicate and r stands for relation. p is prepositional logic formula which may use connectors like and, or, and not. These terms may use relational operators like: =, ≠, ≥, <,
  32. 32. example: σsubject="database"(Books) Output: Selects tuples from books where subject is 'database'. σsubject="database" and price="450"(Books) Output: Selects tuples from books where subject is 'database' and 'price' is 450. σsubject="database" and price < "450" or year > "2010"(Books) Output: Selects tuples from books where subject is 'database' and 'price' is 450 or those books published after 2010. Project Operation (Π): It projects column(s) that satisfy a given predicate. Notation: ΠA1, A2, An (r) Where A1, A2, An are attribute names of relation r. Duplicate rows are automatically eliminated, as relation is a set. Example Πsubject, author (Books) Selects and projects columns named as subject and author from the relation Books.
  33. 33. Selects and projects columns named as subject and author from the relation Books. Notion: r U s Where r and s are either database relations or relation result set (temporary relation). For a union operation to be valid, the following conditions must hold: r and s must have the same number of attributes. Attribute domains must be compatible. Duplicate tuples are automatically eliminated. Example Π author (Books) ∪ Π author (Articles) Output: Projects the names of the authors who have either written a book or an article or both. Set Difference (−) The result of set difference operation is tuples, which are present in one relation but are not in the second relation.
  34. 34. Πauthor(Books) − Πauthor(Articles) Output: Provides the name of authors who have written books but not articles. Cartesian Product (Χ) Combines information of two different relations into one. Notation: r Χ s Where r and s are relations and their output will be defined as: r Χ s = { q t | q ∈ r and t ∈ s} Examlle Πauthor = 'tutorialspoint'(Books Χ Articles) Output: Yields a relation, which shows all the books and articles written by tutorialspoint. Rename Operation (ρ) The results of relational algebra are also relations but without any name. The rename operation allows us to rename the output relation. ‘rename’ operation is denoted with small Greek letter rho ρ. Notation: ρ x (E)
  35. 35. S/ N SUBJECT PRICE YEAR OF PUBLICATION AUTHOR 1 Physics 150 2005 Kim James 2 Database 450 2007 Putin Clarke 3 Database 450 2010 Trump Granny 4 Database 1250 2012 Macron Bill 1. Using the table given above, what will be the serial number of tuples selected as the output of operation: σsubject ‘database and price’ = ‘450’ (Books)[ A. 1 only B. 2 &3 only C. 2, 3 &4 D. None of the above] 2. The output of operation: σsubject = “database” and price < “450” or year> “2010” (B00ks) is [A. 3 only B. 2 &3 only C. 2, 3 &4 D. None of the above]
  36. 36. References: 1. DBMS @ www.tutorialspoint.com 2. Raghu Ramakrishman and Johannes Gehrke, Database Management System second edition available online @ http://www.cs.wisc.edu/~dbbook 3. Database Fundamentals: Johns Hopkins University rrobbins@gdb.org 4. Database Systems: Concept and design by Bhojaraju G. and M.M.Koganurmath Mumbai India.