B.C.A (2st Semester) October/ November – 20121. Answer following (any seven) 14(1) What is the use of Canonical cover during normalization?Ans. Canonical cover during normalization:-

Canonical cover can reduce the efforts spent in checking for violations by testing a simplified set of functional dependencies that has the same closure as the given set. An attribute of a functional dependency is said to be extraneous if we can remove it without changing the closure of the set of functional dependenciesCanonical Cover. It is the use of canonical cover during the normalization.

A canonical cover for F is a set of dependencies Fc such that F logically implies all dependencies in Fc, and Fc logically implies all dependencies in F, andNo functional dependency in Fc contains an extraneous attribute, and

Each left side of functional dependency in Fc is unique.To compute a canonical cover for F:repeat

Use the union rule to replace any dependencies in F a1 ® b1 and a1 ® b2 with a1 ® b1 b2

Find a functional dependency a ® b with an extraneous attribute either in a or in b

If an extraneous attribute is found, delete it from a ® b until F does not changeNote: Union rule may become applicable after some extraneous attributes have been deleted, so it has to be re-appliedComputing a Canonical CoverR = (A, B, C)F = {A ® BC

B ® C A ® BAB ® C}

Combine A ® BC and A ® B into A ® BCSet is now {A ® BC, B ® C, AB ® C}

A is extraneous in AB ® CCheck if the result of deleting A from AB ® C is implied by the other dependencies

Yes: in fact, B ® C is already present!Set is now {A ® BC, B ® C}

C is extraneous in A ® BC Check if A ® C is logically implied by A ® B and the other dependencies

Yes: using transitivity on A ® B and B ® C. – Can use attribute closure of A in more complex cases

The canonical cover is: A ® BB ® C

(2) What is tuple?Ans. Tuple:-

Tuple is the horizontal bar in table object.A tuple is a record or row in table. Tuple is known as the rows in a relation(table).A single row in a table is called tuple .

(3) Define functional dependency.Ans. Functional dependency:-

Given two sets of attributes X & Y. Y is said to be functionally dependent on X if a given value for each attribute in X uniquely determines the value of the attributes in Y. X is called the determinate of the functional dependency and it is denoted by XY.

(4) What do you understand by ‘Logical data independence’?Ans. Logical data independence:-

Logical data independence also insulates application programs from operations such as combining two records into one or splitting an existing record into two or more records.Logical data independence is archived by providing the external level or user view of database.It indicates that the conceptual schema can be changed without affecting the existing external schemas.

(5) What is weak entity set?Ans. Entity set:-

An entity set may not have sufficient attributes form a primary key. Such an entity set is terned a weak entity set.

Denoted by:

(6) State difference between delete and drop.Ans. Delete:

The delete command removes tuples from a relation. It includes a WHERE clause, similar to that used in SQL query, to select tuples to be deleted. Drop: The drop command can be used to drop named schema elements, such as tables, domains, constraints. one can also drop a schema.

(7) What is SQL, and why it is important?Ans. SQL: Structure Query Language

The SQL is presently expended as Structure Query Language. SQL is comprehensive database language: It has statements for database data definitions, queries, and updates. Hence, it is both a DDL and a DML. It is important to note that database programming is a very broad topic

(8) What do you understand by data type memo?Ans. Memo:-

Memo is long text data type. Use for long pieces of text, such as notes and long descriptions. It can store up to 64,000 characters.Example: address, remarks type of column name.

2. Answer following {any three) 18(1) Discuss the disadvantage of file processing.Ans. Disadvantage of file processing:-

1.Program-Data Dependence.

File descriptions are stored within each application program that accesses a given file.

2. Duplication of Data.

 . Applications are developed independently in file processing systems leading to unplanned duplicate files. Duplication is wasteful as it requires additional storage space and changes in one file must be made manually in all files. This also results in loss of data integrity. It is also possible that the same data item may have different names in different files, or the same name may be used for different data items in different files. 3.   Limited data sharing . 

Each application has its own private files with little opportunity to share data outside their own applications. A requested report may require data from several incompatible files in separate systems. 4.   Lengthy Development Times . 

There is little opportunity to leverage previous development efforts. Each new application requires the developer to start from scratch by designing new file formats and descriptions 5.   Excessive Program Maintenance . 

The preceding factors create a heavy program maintenance load.

6.   Integrity Problem

. The problem of integrity is the problem of ensuring that the data in the database is accentuate.

7. Inconsistance data

8. Security

(2) What is normalization explain BCNF and compare how it is better than Third Normal Form?

Ans. Normalization:- Normalization is a process of efficiently organizing data in a database. There are two goals of the normalization (1) eliminating redundant data (for example storing the same data in more than on table) and (2) ensuring data dependencies make sure (only storing related data in a table) both of these are important goals for reduce the amount of space a database consumes and ensure that data is logically stored.Or Normalization is a process of filtering in database which is to reduce anomalies and redundant data.

Introduction.0ne of the more crucial topic in the area of database management is the process of normalizing the table in a relational database.The understanding ideas in normalization are simple enough.Through the normalization we want to design for our relational database asset of files that

Contain all the data necessary for the purposes that the database is to serve.Have as little redundancy as possible.Accommodate multiple values for types of data that require them.Permit efficient updates of the data in the database.Avoid the danger of losing data unknowingly.The primary reason for normalizing database to at least the level of the 3rd Normal Form(the levels are explained below) is that normalization is a potent weapon against the possible corruption of database stemming from what are called “insertion anomalies”,” Deletion anomalies”,” Update anomalies”. These types of error can creep into databases that are insufficiently normalized.

What is normalization?Definitions: Normalization is a technique for producing a set of relations with desirable properties, given the data requirements of an enterprise.

Normalization is the process of efficiently organizing data in a databaseThe process of normalization is a formal method that identifies relations based on their primary or candidate keys and the functional dependencies among their attributes.Normalization is a technique that

Decomposes data into two dimensional tables.Eliminates any relationships in which table data does fully depend upon the primary key of a record.Eliminates any relationship that contains transitive dependencies.

Goals of Normalization : Eliminate redundant data.

Ensuring redundant data is logically store.

Normalization can be viewed as a series of steps designed, one after another, to deal with ways in which tables can be “too complicated for their own good”.

Purpose of normalization :

The purpose of normalization is to reduce the chances for anomalies to occur in a sdatabase. The definitions of the various levels of normalization illustrate complications to be eliminated in order to reduce the chances of anomalies.

At all levels and in every case of table with a complication , the resolution of theproblem turns out to be the establishment of two or more simpler tables which, as a group contain the same information as the original table, but because of their simpler individual structures, lack the complication.

Pros & cons of normalization.Pros:More efficient structure.

Better understanding of your data.More flexible data base structure.Easier to maintain database structure.Few (if any) costly surprise down the road.Validates your common sense and intuition.Insure that distict table exit when necessary.

Cons:You can’t started building the database before you know what the user needs.

Basic Information Of Normalization:

1 ST Normal Form (1 ST NF)

A table (relation) is in 1NF If:

1. There is no duplicate row in the table.

2. Each cell is single-valued (no repeting group or array).

3. Entity in a column (field) are of the same kind.

The requirement that there be no duplicated rows in the table means that the table has a key (although the key might be made up of more than one column, even possibly, of all the columns).

2nd Normal Form (2NF) A table is in 2NF if it is in 1NF and if all non-key attributes are dependent on all of the

key. Since a partial dependency occurs when a non-key attribute is dependent on only a part

of the composite key, the definition of 2NF is sometimes phrased as, “A table is in 2NF if it is in 1NF and if it has no partial dependencies.”

3rd Normal Form (3NF) A table is in 3NF if it is in 2NF and if it has no transitive dependencies.

Boyce-Codd Normal Form (BCNF) A table is in BCNF if it is in 3NF and if every determinant is a candidate key.

4th Normal Form (4NF) A table is in 4NF if it is in BCNF and if it has no multi-valued dependencies.

5th Normal Form (5NF)A table is in 5NF, also called “Projection-join Normal Form” (PJNF), if it is in 4NF and if every join dependency in the table is a consequence of the candidate keys of the table.

Need of normalization.Database normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency. Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining relationships between them.A database table looks like a spreadsheet.  One item of data, such as a first name, a last name, a phone number, or a street address, appears in each box, or cell, at each intersection of a row and column. When you normalize a database you have four goals:

Arranging data into logical groupings such that each group describes a small part of the wholeMinimizing the amount of duplicate data stored in a database; Organizing the data such that, when you modify it, you make the change in only one place Building a database in which you can access and manipulate the data quickly and efficiently without compromising the integrity of the data in storage.

Data normalization helps you design new databases to meet these goals or to test databases to see whether they meet the goals.

Sometimes database designers refer to these goals in terms such as data integrity, referential integrity, or keyed data access.Normalization is a process followed for eliminating redundant data and establishes a meaningful relationship among tables based on rules and regulations in order to maintain integrity of data.Normalization is done for maintaining storage space and also for performance tuning.

Domain-key normal form (DKNF).

A table is in DKNF if every constraint on the table is a logical consequence of the definition of keys and domains.

Insertion anomaly.

It is a failure to place information about a new database entryinto all the places in the database where information about the new entry needs to be stored.

In a properly normalized database, information about a new entry needs to be inserted into only one place in the database, in an inadequatly normalized database, information about a new entry may need to be inserted into more than one place, and human

fallibility being what it is, some of the needed additional insertionsmay be missed.

Deletion anomaly. It is a failure to remove information about an existing database entry when it is time

to remove that entry.

In a properly normalized database, information about an old, to-be-gotten-rid-of entry needs to be deletedfrom only one place in the database, in an inadequatly normalized database, information about that old entry may need to be deleted from more than one place.

Update anomaly.

An update of a database involves modifications that may be additions, deletions, or both.

Thus “update anomalies” can be either of the kinds discussed above.

All three kinds of anomalies are highly undesirable, since thier occurence constitutes corruption of the database. Properly normalized database are much less susceptible to corruption than are un-normalized databases.

Figure of normalization :

Rule 1: Eliminate Repeating Groups. Make a separate table for each set of related attributes, and give each table a primary key.

Unnormalized Data Items for Puppies

puppy number puppy name kennel code kennel name kennel location trick ID trick name trick where learned skill level

In the original list of data, each puppy description is followed by a list of tricks the puppy has learned.

Some might know 10 tricks, somemight not know any. To answer the question “Can Fifi roll over?”

we need first to find Fifi’s puppy record, then scan the list of tricks associated with

the record.This is awkward, inefficient, and extremely untidy.

Moving the tricks into a seperate tablehelps considerably. Seperating the repeating groupsof tricks from the puppy information results in first normal form.

The puppy number in the trick table matches the primarykey in the puppy table, providing a foreign key for relating the two tables with a join operation.

Now we can answer our question with a direct retrieval look to see if Fifi’s puppy number and the trick ID for “roll over” appear together in the trick table.

First Normal Form (1 st NF):

Puppy Tablepuppy number                     — primary keypuppy namekennel namekennel location

Trick Tablepuppy numbertrick IDtrick nametrick where learnedskill level

Rule 2: Eliminate Redundant Data, if an attribute depends on only part of a multi-valued key, remove it to a separate table.

The trick name (e.g. “roll over”) appears redundantly for every puppy that knows it. Just trick ID whould do.

TRICK TABLEPuppy Number Trick ID Trick Name Where Learned Skill Level52                     27         ”roll over”             16                      953                      16         ”Nose Stand”          9                       954                      27         ”roll over”              9                       5

*Note that trick name depends on only a part (the trick ID) of the multi-valued, i.e. composite key.

In the trick table, the primary key is made up of the puppy number and trick ID. This makes sense for the “where learned” and “skill level” attributes, since they will be dif-ferent for every puppy-trick combination. But the trick name depends only on the trick ID. The same name will appear redundantly every time its associated ID appears in the trick ta-

ble.

(3) Explain the binary relationship set and ternary relationship set with an E-R Diagaram.Ans. E-R model

When would we need such a thing?

Consider a DB with information about employees who work on a particular project and use a number of machines doing that work. We get the E-R diagram shown in Figure 2.20.Relationship sets work and uses could be combined into a single set. However, they shouldn't be, as this would obscure the logical structure of this scheme.The solution is to use aggregation.

An abstraction through which relationships are treated as higher-level entities. For our example, we treat the relationship set work and the entity sets employee and

project as a higher-level entity set called work. Figure 2.20 shows the E-R diagram with aggregation.

 

Figure 2.0:E-R diagram with redundant relationships

Figure2.1:E-R diagram with aggregation

 

Figure2.2:Representation of Figure 2.3 using binary relationships

Transforming an E-R diagram with aggregation into tabular form is easy.We createa table for each entity and relationship set as before.

The table for relationship set uses contains a column for each attribute in the primary key of machinery and work.

Database Systems:Design of an E-R Database Scheme

The E-R data model provides a wide range of choice in designing a database scheme to accurately model some real-world situation.Some of the decisions to be made are

Using a ternary relationship versus two binary relationships. Whether an entity set or a relationship set best t a real-world concept.

Whether to use an attribute or an entity set. Use of a strong or weak entity set. Appropriateness of generalization. Appropriateness of aggregation.

1 Mapping Cardinalities

2 Use of Entity or Relationship Sets

3 Use of Extended E-R Features

1 Mapping Cardinalities

The ternary relationship of Figure ?? could be replaced by a pair of binary relationships, as shown in Figure ??.However, there is a distinction between the two representations:

In Figure ??, relationship between a customer and account can be made only if there is a corresponding branch.

In Figure ??, an account can be related to either a customer or a branch alone. The design of figure ?? is more appropriate, as in the banking world we expect to have an

account relate to both a customer and a branch.

Figure 2.23:E-R diagram with account as a relationship set

The design of figure is more appropriate, as in the banking world we expect to have an account relate to both a customer and a branch.

2 Use of Entity or Relationship Sets

It is not always clear whether an object is best represented by an entity set or a relationship set.

Both Figure ?? and Figure ?? show account as an entity. Figure ?? shows how we might model an account as a relationship between a customer and

a branch. This new representation cannot model adequately the situation where customers may have

joint accounts.(Why not?)

If every account is held by only one customer, this method works.

3Use of Extended E-R Features

We have seen weak entity sets, generalization and aggregation. Designers must decide when these features are appropriate.

Strong entity sets and their dependent weak entity sets may be regarded as a single \object" in the database,as weak entities are existence-dependent on a strong entity.

It is possible to treat an aggregated entity set as a single unit without concern for its inner structure details.

Generalization contributes to modularity by allowing common attributes of similar entity sets to be repre-sented in one place in an E-R diagram. Excessive use of the features can introduce unnecessary complexity into the design.

(4) What are the levels o f data abstraction? Mention their features.Ans. Data Abstraction

1. The major purpose of a database system is to provide users with an abstract view of the system.The system hides certain details of how data is stored and maintainedComplexity should be hidden from database users.

2. There are several levels of abstraction:

(a) Physical Level:

How the data are stored. E.g. index, B-tree, hashing. Lowest level of abstraction. Complex low-level structures described in detail.

(b) Conceptual Level:

Next highest level of abstraction.

DATA MODELS

The three levels of data abstraction

Describes what data arestored. Describes the relationships among data. Database administrator level.

(c) View Level:

Highest level. Describes part of the database for a particular group of users. Can be many different views of a database. E.g.tellersinabankgetaviewofcustomeraccounts,butnotofpayrol

Organization of database (Physical, Conceptual, Logical)

The terms "conceptual". "logical", and "physical" are frequently used in data modeling to differentiate levels of abstraction versus detail in the model. Although there is no general agreement, let alone accepted authority, which defines these terms, nevertheless data modelers generally understand the approximate scope of each.     Conceptual E-R ModelA conceptual entity-relationship model shows how the business world sees information. It suppresses non-critical details in order to emphasize business rules and user objects. It typically includes only significant entities which have business meaning, along with their relationships. Many-to-many relationships are acceptable to represent entity associations.

A conceptual model might discover that there is a need to house information about each person in an organization. While considerable thought is given to discovering and describing the relevant properties of each person, the designers accept implicitly that each person is distinct and unique.

A conceptual model may include a few significant attributes to augment the definition and visualization of entities. No effort need be made to inventory the full attribute population of such a model. A conceptual model may have some identifying concepts or candidate keys noted but it explicitly does not include a complete scheme of identity, since identifiers are logical choices made from a deeper context.

 Logical E-R ModelA logical entity-relationship model is provable in the mathematics of data science. Given the current predominance of relational databases, logical models generally conform to relational theory. Thus a logical model contains only fully normalized entities. Some of these may represent logical domains rather than potential physical tables.

For a logical data model to be normalized, it must include the full population of attributes to be implemented and those attributes must be defined in terms of their domains or logical data types (e.g., character, number, date, picture, etc.).

A logical data model requires a complete scheme of identifiers or candidate keys for unique identification of each occurrence in every entity. Since there are choices of identifiers for many entities, the logical model indicates the current selection of identity. Propagation of identifiers as foreign keys may be explicit or implied.

Since relational storage cannot support many-to-many concepts, a logical data model resolves all many-to-many relationships into associative entities which may acquire independent identifiers and possibly other attributes as well.

Physical Database SchemaA physical data model is a single logical model instantiated in a specific database management product (e.g., Sybase, Oracle, Informix, etc.) in a specific installation. The physical data model specifies implementation details which may be features of a particular product or version, as well as configuration choices for that database instance. These include index construction, alternate key declarations, modes of referential integrity (declarative or procedural), constraints, views, and physical storage objects such as tablespaces.     In SummaryThe conceptual model is concerned with the real world view and understanding of data; the logical model is a generalized formal structure in the rules of information science; the physical model specifies how this will be executed in a particular DBMS instance.

Various data modeling methodologies and products provide these layers of abstraction in different ways. Some address only the physical implementation; some model only the logical structure;

others may provide elements of all three but not necessarily in three separate views. In each case it helps the data modeler to understand the level of abstraction to which a particular feature or task belongs.

Express the number of entities to which another entity can be associated via a relationship set.

Most useful in describing binary relationship sets.

For a binary relationship set the mapping cardinality must be one of the following types:

1.One to one

2.One to many

3.Many to one

4.Many to many

One to one One to many

Note: Some elements in A and B may not be mapped to any

elements in the other set

Many to one Many to many

Note: Some elements in A and B may not be mapped to any

elements in the other set

Mapping Cardinalities affect ER Design

Can make access-date an attribute of account, instead of a relationship attribute, if each account can have only one customer

I.e., the relationship from account to customer is many to one, or equivalently, customer to account is one to many .

E-R Diagrams

Rectangles represent entity sets.

Diamonds represent relationship sets.

Lines link attributes to entity sets and entity sets to relationship sets.

Ellipses represent attributes

Double ellipses represent multivalued attributes.

Dashed ellipses denote derived attributes.

Underline indicates primary key attributes (will study later)

E-R Diagram With Composite, Multivalued, and Derived Attributes

Relationship Sets with Attributes

Roles

Entity sets of a relationship need not be distinct

The labels “manager” and “worker” are called roles; they specify how employee entities interact via the works-for relationship set.

Roles are indicated in E-R diagrams by labeling the lines that connect diamonds to rectangles.

Role labels are optional, and are used to clarify semantics of the relationship

Cardinality Constraints

We express cardinality constraints by drawing either a directed line (®), signifying “one,” or an undirected line (—), signifying “many,” between the relationship set and the entity set.

E.g.: One-to-one relationship:

A customer is associated with at most one loan via the relationship borrower

A loan is associated with at most one customer via borrower

One-To-Many Relationship

In the one-to-many relationship a loan is associated with at most one customer via borrower, a customer is associated with several (including 0) loans via borrower

Many-To-One Relationships

In a many-to-one relationship a loan is associated with several (including 0) customers via borrower, a customer is associated with at most one loan via borrower

Many-To-Many Relationship

A customer is associated with several (possibly 0) loans via borrower

A loan is associated with several (possibly 0) customers via borrower3. Answer following (any three) 15(1) What are the difference between the DBMS and file-processing systems?Ans. File-Processing Systems DBMS

A database management system coordinates both the physical and the logical access to the data

File-processing system coordinates only the physical access.

A database management system reduces the amount of data duplication by ensuring that a physical piece of data is available to all programs authorized to have access to it

Data written by one programing a file-processing system may not be readable by another program.

A database management system is designed to allow flexible access to data (i.e., queries)

File-processing system is designed to allow predetermined access to data (i.e., compiled programs).

A database management system is designed to coordinate multiple users accessing the same data at the same time.

A file-processing system is usually designed to allow one or more programs to access different data files at the same time.In a file-processing system, a file can be accessed by two programs concurrently only if both programs have read-only access to the file.

(2) Explain Object-oriented modelAns. The Object-Oriented Model

1. The object-oriented model is based on a collection of objects, like the E-R model.

An object contains values stored in instance variables within the object. Unlike the record-oriented models, these values are themselves objects. Thus objects contain objects to an arbitrarily deep level of nesting. An object also contains bodies of code that operate on the the object. These bodies of code are called methods. Objects that contain the same types of values and the same methods are grouped into

classes. A class may be viewed as a type de nition for objects. Analogy: the programming language concept of an abstract data type.

The only way in which one object can access the data of another object is by invoking the method of that other object.

This is called sending a message to the object. Internal parts of the object, the instance variables and method code, are not visible

externally. Result is two levels of data abstraction.

For example, consider an object representing a bank account.

The object contains instance variables number and balance. The object contains a method pay-interest which adds interest to the balance. Under most data models, changing the interest rate entails changing code in application

programs. In the object-oriented model, this only entails a change within the pay-interest method.

2. Unlike entities in the E-R model, each object has its own unique identity, independent of the values itcontains:

Two objects containing the same values are distinct. Distinction is maintained in physical level by assigning distinct object identi ers.

Record-based logical models:

Also describe data at the conceptual and view levels. Unlike object-oriented models, are used to Specify overall logical structure of the database,

and Provide a higher-level description of the implementation. Named so because the database is structured in xed-format records of several types. Each record type de nes a xed number of elds, or attributes. Each eld is usually of a xed length (this simpli es the implementation). Record-based models do not include a mechanism for direct representation of code in the

database. Separate languages associated with the model are used to express database queries and

updates. The three most widely-accepted models are the relational, network, and hierarchical. This course will concentrate on the relational model. The network and hierarchical models are covered in appendices in the text.

The Relational Model

Data and relationships are represented by a collection of tables. Each table has a number of columns with unique names, e.g. customer, account Figure 1.3 shows a sample relational database.

Figure A sample relational database.

Figure A sample network database

The Network Model

Data are represented by collections of records. Relationships among data are represented by links. Organization is that of an arbitrary graph. Figure 1.4 shows a sample network database that is the equivalent of the relational

database of Figure

The Hierarchical Model

Similar to the network model. Organization of the records is as a collection of trees, rather than arbitrary graphs. Figure shows a sample hierarchical database that is the equivalent of the relational

database of Figure

Figure 1.5: A sample hierarchical database

The relational model does not use pointers or links, but relates records by the values they contain. This allows a formal mathematical foundation to be defined.

2. Database Systems: Physical Data Models

1. Are used to describe data at the lowest level.

2. Very few models, e.g.

Unifying model. Frame memory.

A physical data model (a.k.a. database design) is a representation of a data design which takes into account the facilities and constraints of a given database management system. In the lifecycle of a project it is typically derived from a logical data model, though it may be reverse-engineered from a given database implementation. A complete physical data model will include all the database artifacts required to create relationships between tables or achieve performance goals, such as indexes, constraint definitions,

linking tables, partitioned tables or clusters. The physical data model can usually be used to calculate storage estimates and may include specific storage allocation details for a given database system.

(3) List the responsibilities-functions of database manager?Ans. Responsibilities-functions of database manager:-

1. The database manager is a program module which provides the interface between the low-level data stored in the database and the application programs and queries submitted to the system.

2. Databases typically require lots of storage space (gigabytes). This must be stored on disks. Data

is moved between disk and main memory (MM) as needed.

3. The goal of the database system is to simplify and facilitate access to data. Performance is important. Views provide simpli cation.

4. So the database manager module is responsible for

Interaction with the file manager: Storing raw data on disk using the le system usually provided by a conventional operating system. The database manager must translate DML statements into low-level le system commands (for storing, retrieving and updating data in the database).

Integrity enforcement: Checking that updates in the database do not violate consistency constraints (e.g. no bank account balance below $25) .

Security enforcement: Ensuring that users only have access to information they are permitted to see.

Backup and recovery: Detecting failures due to power failure, disk crash, software errors, etc., and restoring the database to its state before the failure.

Concurrency control: Preserving data consistency when there are concurrent users.

5. Some small database systems may miss some of these features, resulting in simpler database managers. (For example, no concurrency is required on a PC running MS-DOS.) These features are necessary on larger systems.

(4) Explain the types of Database Users.Ans. The database users fall into several categories:

Application programmers are computer professionals interacting with the system through DML calls embedded in a program written in a host language (e.g. C, PL/1, Pascal).

These programs are called application programs. The DML precompiler converts DML calls (prefaced by a special character like $, #, etc.)

to normal procedure calls in a host language. The host language compiler then generates the object code. Some special types of programming languages combine Pascal-like control structures with

control structures for the manipulation of a database. These are sometimes called fourth-generation languages. They often include features to help generate forms and display data.

Sophisticated users interact with the system without writing programs.

They form requests by writing queries in a database query language These are submitted to a query processor that breaks a DML statement down into

instructions for the database manager module.

Specialized users are sophisticated users writing special database application programs. These may be CADD systems, knowledge-based and expert systems, complex data systems(audio/video), etc.

Naive users are unsophisticated users who interact with the system by using permanent application

programs (e.g. automated teller machine).

4. Answer following (any four) 12(1) What is meant by ‘instances and schemas’? What are the types of schemas?Ans. Schema:

Schema is the logical structure of the database- E.g, The database consists of information about a set of customers and accounts and the

relationship between them.- Analogous to type information of variable in a program.Instance: Instance is the actual content of the database at a particular point in a time- Analogous to the value of a variable.- There are two types of schemas:

1. Physical schema : Database design at the physical level.Logical schema : Database design at the logical level.

Database Systems: Instances and Schemes

1. Databases change over time.

2. The information in a database at a particular point in time is called an instance of the database.

3. The overall design of the database is called the database scheme.

4. Analogy with programming languages:

Data type de nition - scheme Value of a variable - instance

5. There are several schemes, corresponding to levels of abstraction:

Physical scheme Conceptual scheme Subscheme (can be many)

(2) What is ‘data manipulation’?Ans. Data manipulation

A data-manipulation language(DML) is a language that enables user to access or manipulate data as organized by the appropriate data model. The types of access are:- Retrieval of information stored in the database- Insertion of new information into the database- Deletion of information from the database- Modification of information stored in the databaseThere are basically two types:- Procedural DMLs require a user to specify what data are needed and how to get those data.- Declarative DMLs (also referred to as nonprocedural DMLs) require a user to specify what

data are needed without specifying how to get those data.

Declarative DMLs are usually easier to learn and use than are procedural DMLs. However, since a user does not have to specify how to get the data, the database system has to figure out and efficient means of accessing data. A query is a statement requesting the retrieval of information. The portion of a DML that involves information retrieval is called a query language. Although technically incorrect, it is common practice to use the terms query language and data manipulation language synonymously.

(3) What is Total Participation.Ans. Total participation

Every member of entity set must participate in the relationship Represented by double line from entity rectangle to relationship diamond E.g., A Class entity cannot exist unless related to a Faculty member entity in this

example, not necessarily at Juniata. You can set this double line in Dia In a relational model we will use the references clause.

Every member of entity set must participate in the relationship

Represented by double line from entity rectangle to relationship diamond E.g., A Class entity cannot exist unless related to a Faculty member entity in this example,

not necessarily at Juniata. You can set this double line in Dia

In a relational model we will use the references clause

(4) What is Domain IntegrityAns. Integrity

Enforcing data integrity guarantees the quality of the data in the database. For example, if an employee is entered with an employee ID value of 123, the database should not permit another employee to have an ID with the same value. If you have an employee_rating column intended to have values ranging from 1 to 5, the database should not accept a value outside that range. If the table has a dept_id column that stores the department number for the employee, the database should permit only values that are valid for the department numbers in the company.Two important steps in planning tables are to identify valid values for a column and to decide how to enforce theintegrity of the data in the column.Data integrity falls into the followingcategories:1.Entity integrity2.Domain integrity3.Referential integrity4.User-defined integrityDomain Integrity Domain integrity is to do with the values that may be contained within a specific column. All columns have an implicitly domain derived from their data type but miner sql also supports the create domain statement. A domain can be defined with a number of check clauses and a default value. A domain definition can be used instead of a data type in a column definition.This has the advantage that the same definition of data type ,check clauses and default values can be used many column definitions and therefore those columns are guaranteed to have the same attributes. Domain integrity is the validity of entries for a specific column. You can enforce domain

integrity to restrict the type by using data types, restrict the format by using CHECK constraints and rules, or restrict the range of possible values by using FOREIGN KEY constraints, CHECK constraints, DEFAULT definitions, NOT NULL definitions, and rules

(5) Give the list of various types of joinsAns. Types of join

-simple join-equijoin-non-equijoin-outer join

Left-outer join Right-outer join Full-outer join

-self join-theta join-pi join-union join

Simple join:Syntax:SELECT table1.column,table2.columnFROM table1.table2WHERE table1.column=table2.column

-write the join condition the WHERE clause.-precede each column name with the table name for clarity.- column names must be prefixed with the table name when the same column name appear in more than one table.

Example:SQL> SELECT EMP.ENAME,DEPT.DNAME FROM EMP, DEPT WHERE EMP.DNO=DEPT.DNO and EMP.salary>5000;

Self joins:

-join rows in a table to rows in the same table by using a self join.-simulate two tables in the FROM clause by creating two aliases for the table.

Example:

Eno Ename Esr1 A 22 B 33 C 33 C 1

SQL> SELECT e. ename, es. ename srname FROM emp e, emp es Where e.esr =es.eno ;

Output:Ename SrnameA BB CC CC A

5.(a) Write SQL Statement to create these tables 3

Emp(emp_id,name,city ,salary ,post,dept_no,join_date)Ans. Create table Emp

(Em_pid Number primary Key,Name Text,City Text,Salary Curancy,Post Text,Dept_no Number,Join_date Curancy); Department(dept_no,dept_name,location)

Ans. Create table Department(Dept_no Number,Dept_name Text,Location );Skill(emp_id,skill)

Ans. Create table skill(Emp_id Number,Skill Text);

(b) Solve the following queries 8(i) List the employee details that are from ’’Ahmadabad” or Baroda”Ans. Select *

From cityWhere like=” Ahmadabad or Baroda”;

(ii) Count the no. of employees from “account: dept.Ans. Select emp,department

From dept-NameWhere like=”

(iii) Display the information of employees having “typing"Ans. Select post

From employeeWhere employee.post like=”typing”;

(iv) List the department name of employees whose join date is 21st of any month.Ans. Select departname,empemp,dep,doj

From department,empWhere employee.doj like”21/3/2013”;Product By :- PATEL AKASH & GROUP 12BCA06 BHAVSAR BHAKTI12BCA07 CHAUHAN DIVYA12BCA08 AKASH R PATEL12BCA09 CHAUHAN YATIN12BCA10 DESAI AMISHI