Wednesday, July 9, 2008

unit 1

database :
A database is a collection of stored operational data used by various applications and/or users by some particular enterprise or by a set of outside authorized applications and authorized users.
DataBase Management System :
A DataBase Management System (DBMS) is a software system that manages execution of users applications to access and modify database data so that the data security, data integrity, and data reliability is guaranteed for each application and each application is written with an assumption that it is the only application active in the database.
What Is Data ?
Different view points:
–A sequence of characters stored in computer memory or storage
–Interpreted sequence of characters stored in computer memory or storage
–Interpreted set of objects
– Database supports a concurrent access to the data

File Systems :
File is uninterpreted, unstructured collection of information
•File operations: delete, catalog, create, rename, open, close, read, write, find, …
•Access methods: Algorithms to implement operations along with internal file organization
•Examples: File of Customers, File of Students; Access method: implementation of a set of operations on a file of students or customers.
File Management System Problems :
Data redundancy
•Data Access: New request-new program
•Data is not isolated from the access implementation
•Concurrent program execution on the same file
•Difficulties with security enforcement
•Integrity issues .

Database Applications :
Airline Reservation Systems – Data items are: single passenger reservations; Information about flights and airports; Information about ticket prices and tickets restrictions.
Banking Systems – Data items are accounts, customers, loans, mortgages, balances, etc. Failures are not tolerable. Concurrent access must be provided
Corporate Records – Data items are: sales, accounts, bill of materials records, employee and their dependents


Data independence: Application programs should be as independent as possible from details of data representation and storage. The DBMS can provide an abstract view of the data to insulate application code from such details.
cient data access: A DBMS utilizes a variety of sophisticated techniques to store and retrieve data efciently. This feature is especially important if the data is stored on external storage devices.
Data integrity and security: If data is always accessed through the DBMS, the DBMS can enforce integrity constraints on the data. For example, before inserting salary information for an employee, the DBMS can check that the department budget is not exceeded. Also, the DBMS can enforce access controls that govern what data is visible to dierent classes of users.
Data administration: When several users share the data, centralizing the administration
of data can oer signi cant improvements. Experienced professionals who understand the nature of the data being managed, and how dierent groups of users use it, can be responsible for organizing the data representation to minimize redundancy and for ne-tuning the storage of the data to make retrieval efcient.

concarence recovery: A DBMS schedules concurrent accesses to the data in such a manner that users can think of the data as being accessed by only one user at a time. Further, the DBMS protects users from the eects of system failures.
Reduced application development time: Clearly, the DBMS supports many important functions that are common to many applications accessing data stored in the DBMS. This, in conjunction with the high-level interface to the data, facilitates quick development of applications. Such applications are also likely to be more robust than applications developed from scratch because many important tasks are handled by the DBMS instead of being implemented by the application.
Data Levels and their Roles :

Physical – corresponds to the first view of data: How data is stored, how is it accessed, how data is modified, is data ordered, how data is allocated to computer memory and/or peripheral devices, how data items are actually represented (ASCI, EBCDIC,…) .The physical schema speci es additional storage details. Essentially, the physical schema summarizes how the relations described in the conceptual schema are actually stored on secondary storage devices such as disks and tapes. We must decide what le organizations to use to store the relations, and create auxiliary data structures called indexes to speed up data retrieval operations.
Conceptual – corresponds to the second view of data: What we want the data to express and what relationships between data we must express, what “ story” data tells, are all data necessary for the “story’ are discussed. The conceptual schema (sometimes called the logical schema) describes the stored data in terms of the data model of the DBMS. In a relational DBMS, the conceptual schema describes all relations that are stored in the database. In our sample university database, these relations contain information about entities, such as students and faculty, and about relationships, such as students' enrollment in courses. All student entities can be described using records in a Students relation, as we saw earlier. In fact, each collection of entities and each collection of relationships can be described as a relation, leading to the following conceptual schema:
Students(sid: string, name: string, login: string, age: integer, gpa: real)
Faculty( d: string, fname: string, sal: real)
Courses(cid: string, cname: string, credits: integer)
Rooms(rno: integer, address: string, capacity: integer)
Enrolled(sid: string, cid: string, grade: string)
Teaches( d: string, cid: string)
Meets In(cid: string, rno: integer, time: string)
The choice of relations, and the choice of elds for each relation, is not always obvious,
and the process of arriving at a good conceptual schema is called conceptual
database design.
View – corresponds to the third view of data:What part of the data is seen by a specific application .External schemas, which usually are also in terms of the data model of the DBMS, allow data access to be customized (and authorized) at the level of individual users or groups of users.The external schema design is guided by end user requirements. For example, we might ant to allow students to nd out the names of faculty members teaching courses, as well as course enrollments. This can be done by de ning the following view:
Courseinfo(cid: string, fname: string, enrollment: integer)


When a user issues a query, the parsed query is presented to a query optimizer, which uses information about how the data is stored to produce an effcient execution plan for evaluating the query. An execution plan is a blueprint for evaluating a query, and is usually represented as a tree of relational operators.

The code that implements relational operators sits on top of the le and access methods layer. This layer includes a variety of software for supporting the concept of a le, which, in a DBMS, is a collection of pages or a collection of records. This layer typically supports a heap le, or le of unordered pages, as well as indexes. In addition to keeping track of the pages in a le, this layer organizes the information within a page.The les and access methods layer code sits on top of the buer manager, which brings pages in from disk to main memory as needed in response to read requests.

The lowest layer of the DBMS software deals with management of space on disk, where the data is stored. Higher layers allocate, deallocate, read, and write pages through (routines provided by) this layer, called the disk space manager.
The DBMS supports concurrency and crash recovery by carefully scheduling user requests and maintaining a log of all changes to the database. DBMS components associated with concurrency control and recovery include the transaction manager, which ensures that transactions request and release locks according to a suitable locking protocol and schedules the execution transactions; the lock manager, which keeps track of requests for locks and grants locks on database objects when they become available; and the recovery manager, which is responsible for maintaining a log, and restoring the system to a consistent state after a crash. The disk space manager, buer manager, and le and access method layers must interact with these components.

Data Models:
A collection of tools for describing ......

  • Data.
  • Data relationships.
  • Data semantics.
  • Data constraints.

Relational model..........

Entity-Relationship data model (mainly for database design) .

Object-based data models (Object-oriented and Object-relational).
  • Semistructured data model (XML).
Other older models:.........
  • Network model .
  • Hierarchical model.

Database Access from Application Programs:

To access the database, DML statements need to be executed from the host language.There are two ways to do this:

• By providing an application program interface (set of procedures) that can be used to send DML and DDL statements to the database, and retrieve the results.The Open Database Connectivity (ODBC) standard defined by Microsoft for use with the C language is a commonly used application program interfacestandard. The Java Database Connectivity (JDBC) standard provides correspondingfeatures to the Java language.
• By extending the host language syntax to embed DML calls within the host language program. Usually, a special character prefaces DML calls, and a preprocessor, called the DML precompiler, converts the DML statements to normal procedure calls in the host language.

Database Users and Administrators:

  • Naive users are unsophisticated users who interact with the system by invoking one of the application programs that have been written previously.
  • Application programmers are computer professionals who write application programs.
  • Sophisticated users interact with the system without writing programs. Instead,they form their requests in a database query language. They submit each such query to a query processor, whose function is to break down DML statements into instructions that the storage manager understands. Analysts who submit queries to explore data in the database fall in this category.
  • Specialized users are sophisticated users who write specialized database applications that do not fit into the traditional data-processing framework.
  • Database Administrator:A person who has such central control over the system is called a database administrator (DBA)
  • Schema definition. The DBA creates the original database schema by executing a set of data definition statements in the DDL.
  • Storage structure and access-method definition.
  • Schema and physical-organization modification. The DBA carries out changes to the schema and physical organization to reflect the changing needs of the organization, or to alter the physical organization to improve performance.
  • Granting of authorization for data access.
  • Routine maintenance.

Data Model:

Entity:An entity is a “thing” or “object” in the real world that is distinguishable from all other objects. For example, each person in an enterprise is an entity.

Entity set:An entity set is a set of entities of the same type that share the same properties, orattributes. The set of all persons who are customers at a given bank, for example, can be defined as the entity set customer. Similarly, the entity set loan might represent the set of all loans awarded by a particular bank.
An entity is represented by a set of attributes. Attributes are descriptive properties possessed by each member of an entity set. The designation of an attribute for an entity set expresses that the database stores similar information concerning each entity in the entity set; however, each entity may have its own value for each attribute.
Simple and composite attributes:the attributes havebeen simple; that is, they are not divided into subparts is called as "simple attributes". on the other hand, can be divided into subparts is called as "composite attributes".For example, an attribute name could be structured as a composite attribute consisting of first-name, middle-initial, and last-name.
Single-valued and multivalued attributes:For instance, the loan-number attribute for a specific loan entity refers to only one loan number. Such attributes are said to be single valued. There may be instances where an attribute has a set of values for a specific entity. Consider an employee entity set with the attribute phone-number. An employee may have zero, one, or several phone numbers, and different employees may have different numbers of phones.
This type of attribute is said to be multivalued.

Derived attribute:The value for this type of attribute can be derived from the values of other related attributes or entities. For instance, let us say that the customer entity set has an attribute loans-held, which represents how many loans a customer has from the bank.We can derive the value for this attribute by counting the number of loan entities associated with that customer.

Relationship Sets:A relationship is an association among several entities. A relationship set is a set of relationships of the same type.

Mapping Cardinalities:Mapping cardinalities, or cardinality ratios, express the number of entities to which another entity can be associated via a relationship set. Mapping cardinalities are most useful in describing binary relationship sets, although they can contribute to the description of relationship sets that involve more than two entity sets.

  • One to one. An entity in A is associated with at most one entity in B, and an entity in B is associated with at most one entity in A.
  • One to many. An entity in A is associated with any number (zero or more) of entities in B. An entity in B, however, can be associated with at most one entity in A.
  • Many to one. An entity in A is associated with at most one entity in B. An entity in B, however, can be associated with any number (zero or more) of entities in A.
  • Many to many. An entity in A is associated with any number (zero or more) of entities in B, and an entity in B is associated with any number (zero or more) of entities in A.

Keys: A key allows us to identify a set of attributes that suffice to distinguish entities from each other. Keys also help uniquely identify relationships, and thus distinguish relationships from each other.

superkey:A superkey is a set of one or more attributes that, taken collectively, allow us to identify uniquely an entity in the entity set. For example, the customer-id attribute of the entity set customer is sufficient to distinguish one customer entity from another. Thus, customer-id is a superkey. Similarly, the combination of customer-name and customer-id is a superkey for the entity set customer. The customer-name attribute of customer is not a superkey, because several people might have the same name.

candidate key:minimal superkeys are called candidate keys.If K is a superkey, then so is any superset of K. We are often interested in superkeys for which no proper subset is a superkey.It is possible that several distinct sets of attributes could serve as a candidate key.Suppose that a combination of customer-name and customer-street is sufficient to distinguish among members of the customer entity set. Then, both {customer-id} and {customer-name, customer-street} are candidate keys. Although the attributes customerid and customer-name together can distinguish customer entities, their combination does not form a candidate key, since the attribute customer-id alone is a candidate key.

primary key:which denotes the unique identity is called as primary key.primary key to denote a candidate key that is chosen by the database designer as the principal means of identifying entities within an entity set. A key (primary, candidate, and super) is a property of the entity set, rather than of the individual entities. Any two individual entities in the set are prohibited from having the same value on the key attributes at the same time. The designation of a key represents a constraint in the real-world enterprise being modeled.

Weak Entity Sets:An entity set may not have sufficient attributes to form a primary key. Such an entity set is termed a weak entity set. An entity set that has a primary key is termed a strong entity set.

For a weak entity set to be meaningful, it must be associated with another entityset, called the identifying or owner entity set. Every weak entity must be associated with an identifying entity; that is, the weak entity set is said to be existence dependent on the identifying entity set. The identifying entity set is said to own the weak entity set that it identifies. The relationship associating the weak entity set with the identifying entity set is called the identifying relationship. The identifying relationship is many to one from the weak entity set to the identifying entity set, and the participation of the weak entity set in the relationship is total.
In our example, the identifying entity set for payment is loan, and a relationship loan-payment that associates payment entities with their corresponding loan entities is the identifying relationship.
Although a weak entity set does not have a primary key, we nevertheless need a means of distinguishing among all those entities in the weak entity set that depend on one particular strong entity. The discriminator of a weak entity set is a set of attributes that allows this distinction to be made.

In E-R diagrams, a doubly outlined box indicates a weak entity set, and a doubly outlined diamond indicates the corresponding identifying fig the weak entity set payment depends on the strong entity set loan via the relationship set loan-payment.

The figure also illustrates the use of double lines to indicate total participation—the of the (weak) entity set payment in the relationship loan-payment is total, meaning that every payment must be related via loan-payment to some loan. Finally, the arrow from loan-payment to loan indicates that each payment is for a single loan. The discriminator of a weak entity set also is underlined, but with a dashed, ratherthan a solid, line.

Specialization:An entity set may include subgroupings of entities that are distinct in some wayfrom other entities in the set. For instance, a subset of entities within an entity set may have attributes that are not shared by all the entities in the entity set. The E-R model provides a means for representing these distinctive entity groupings. Consider an entity set person, with attributes name, street, and city. A personmay be further classified as one of the following:
• customer
• employee

Each of these person types is described by a set of attributes that includes all the attributes of entity set person plus possibly additional attributes. For example, customer entities may be described further by the attribute customer-id, whereas employee entities may be described further by the attributes employee-id and salary. The process of designating subgroupings within an entity set is called specialization. The specialization of person allows us to distinguish among persons according to whether they are employees or customers.

Generalization:The design process may also proceed in a bottom-up manner, in which multiple entity sets are synthesized into a higher-level entity set on the basis of common features. The database designer may have first identified a customer entity set with the attributes name, street, city, and customer-id, and an employee entity set with the attributes name, street, city, employee-id, and salary. There are similarities between the customer entity set and the employee entity set in the sense that they have several attributes in common. This commonality can be expressed by generalization, which is a containment relationship that exists between a higher-level entity set and one or more lower-level entity sets. In our example, person is the higher-level entity set and customer and employee are lower-level entity sets.
Higher- and lower-level entity sets also may be designated by the terms superclass and subclass, respectively. The person entity set is the superclass of the customer and employee subclasses.For all practical purposes, generalization is a simple inversion of specialization. We will apply both processes, in combination, in the course of designing the E-R schema for an enterprise. In terms of the E-R diagram itself, we do not distinguish between specialization and generalization. New levels of entity representation will be distinguished (specialization) or synthesized (generalization) as the design schema comes to express fully the database application and the user requirements of the database. Differences in the two approaches may be characterized by their starting point and overall goal.Generalization proceeds from the recognition that a number of entity sets share some common features (namely, they are described by the same attributes and participatein the same relationship sets).

Aggregation is an abstraction in which relationship sets (along with their associated entity sets) are treated as higher-level entity sets, and can participate in relationships.

Symbols used in the E-R notation:

ER Model For a college DB:

Assumptions :

  • A college contains many departments
  • Each department can offer any number of courses
  • Many instructors can work in a department
  • An instructor can work only in one department
  • For each department there is a Head
  • An instructor can be head of only one department
  • Each instructor can take any number of courses
  • A course can be taken by only one instructor
  • A student can enroll for any number of courses
  • Each course can have any number of students

Steps in ER Modeling:

  • Identify the Entities
  • Find relationships
  • Identify the key attributes for every Entity
  • Identify other relevant attributes
  • Draw complete E-R diagram with all attributes including Primary Key

Step 1: Identify the Entities:


Step 2: Find the relationships:

  • One course is enrolled by multiple students and one student enrolls for multiple courses,
    hence the cardinality between course and student is Many to Many.
  • The department offers many courses and each course belongs to only one department,
    hence the cardinality between department and course is One to Many.
  • One department has multiple instructors and one instructor belongs to one and only one
    department , hence the cardinality between department and instructor is one to Many.
  • Each department there is a “Head of department” and one instructor is “Head of
    department “,hence the cardinality is one to one .
  • One course is taught by only one instructor, but the instructor teaches many courses,
    hence the cardinality between course and instructor is many to one.

Step 3: Identify the key attributes

  • Deptname is the key attribute for the Entity “Department”, as it identifies the Department uniquely.
  • Course# (CourseId) is the key attribute for “Course” Entity.
  • Student# (Student Number) is the key attribute for “Student” Entity.
  • Instructor Name is the key attribute for “Instructor” Entity.

Step 4: Identify other relevant attributes

For the department entity, the relevant attribute is location

  • For course entity, course name,duration,prerequisite
  • For instructor entity, room#, telephone#
  • For student entity, student name, date of birth

  • ER model for Banking Business :

    Assumptions :

    • There are multiple banks and each bank has many branches. Each branch has multiple customers
    • Customers have various types of accounts
    • Some Customers also had taken different types of loans from these bank branches
    • One customer can have multiple accounts and Loans

    Step 1: Identify the Entities
    • BANK
    • BRANCH
    • LOAN
    Step 2: Find the relationships
    • One Bank has many branches and each branch belongs to only one bank, hence the
    cardinality between Bank and Branch is One to Many.
    • One Branch offers many loans and each loan is associated with one branch, hence the
    cardinality between Branch and Loan is One to Many.
    • One Branch maintains multiple accounts and each account is associated to one and
    only one Branch, hence the cardinality between Branch and Account is One to Many
    • One Loan can be availed by multiple customers, and each Customer can avail multiple
    loans, hence the cardinality between Loan and Customer is Many to Many.
    • One Customer can hold multiple accounts, and each Account can be held by multiple
    Customers, hence the cardinality between Customer and Account is Many to Many

    Step 3: Identify the key attributes
    • BankCode (Bank Code) is the key attribute for the Entity “Bank”, as it identifies the bank
    • Branch# (Branch Number) is the key attribute for “Branch” Entity.
    • Customer# (Customer Number) is the key attribute for “Customer” Entity.
    • Loan# (Loan Number) is the key attribute for “Loan” Entity.
    • Account No (Account Number) is the key attribute for “Account” Entity.
    Step 4: Identify other relevant attributes
    • For the “Bank” Entity, the relevant attributes other than “BankCode” would be “Name”
    and “Address”.
    • For the “Branch” Entity, the relevant attributes other than “Branch#” would be “Name”
    and “Address”.
    • For the “Loan” Entity, the relevant attribute other than “Loan#” would be “Loan Type”.
    • For the “Account” Entity, the relevant attribute other than “Account No” would be
    “Account Type”.
    • For the “Customer” Entity, the relevant attributes other than “Customer#” would be
    “Name”, “Telephone#” and “Address”.

    E-R diagram with all attributes including Primary Key: