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Database Management Systems

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Database Management System (CS403)
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Lecture No. 06
Reading Material
"Database Systems Principles, Design and Implementation"
Section 2.4
written by Catherine Ricardo, Maxwell Macmillan.
Overview of Lecture
o Detailed DFD Diagrams:
o Database Design Phase
o Data Models
o Types of Data Models
o Types of Database Designs
Detailed Data Flow Diagram:
This Type of the Data flow diagrams is used when we have to further explain the
functionality of the processes that we showed briefly in the Level 0 Diagram. It means
that generally detailed DFDS are expressed as the successive details of those processes
for which we do not or could not provide enough details.
The symbols and other rules regarding the detailed DFD are same as are in other types of
DFDs. The special features associated with this diagram are that, one, it is optional, that
is, it is created for only those processes from the level 0 diagram for which we want to
show the details. For a small sized system we may not need to develop even a single
detailed DFD, since the level 0 diagram might be covering it sufficiently. Second specific
characteristic of the detailed DFD is its processes' numbering. Numbering of processes in
the detailed DFD is done on the basis of numbering of the particular process in level 0
diagrams whose sub-processes are being included in the detailed DFD. For example, a
specific process which was numbered in the level 0 diagram as 1.0 or 1 may have a
number of sub-processes since we did not represent the process 1.0 in detail in level 0
diagrams. So in the detailed dataflow diagram we create sub-processes of that process
and then number all the sub processes of that specific process as the sublets of the process.
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Numbering of such sub processes is done as 1.1, 1.2, and 1.3... for first second and third
sub-processes of the process 1.0 respectively. The phenomenon of creating sub-processes
does not end at creating a few sub-processes for a specific process shown at level 0
diagrams. Rather it may continue deeper if there is requirement for further explanation of
the any process or sub-processes. In such a case when we create sub-process of a sub-
process 1.2 then the numbering is done in further extension of that specific sub processes
number and example of such a numbering process is 1.2.1, 1.2.2, 1.2.3,...
Another point that is worth mentioning here is that we call processes in the detailed
DFDs as sub-processes, but they are sub-processes only in reference to the process whose
details they are explaining otherwise they are just like processes; transforming some input
data into some form of output. The sub-processes may be performing relatively small
amount of operations, still they are processes.
Maximum Number of Process in a DFD should not be very huge. Having a moderate
number for a detailed DFD is also recommended because it adds clarity to our detailed
data flow diagram. For clarity propose it is good to have a maximum of 7 or 9 processes
in one detailed DFD. Moreover all the processes, sub processes, data stores, entities data
flows and all other components of the DFD must be named properly, so that anyone who
is using this DFD should be able to understand the DFD easily.
In all the levels of DFD it must be considered that all the processes have data inputs as
well as data outputs. Data being sent to one process should be processed so that it
changes its form and transforms from one form to another.
When creating a detailed diagram the data inputs and data outputs must be in coincidence,
mean in both the diagrams the data input to a process and data output in the form of data
flows must be same.
Data Dictionary
A database that containing data about all the databases in the database system. Data
dictionaries store all the various schema and file specifications and their locations. They
also contain information about which programs use which data and which users are
interested in which reports.
Types of Data Dictionaries:
o Integrated
There are basically two types of data dictionaries which are available for use by a DBMS,
with respect to their existence.
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The first type of data dictionary in this context is the integrated data dictionary. Such a
data dictionary is place embedded into the database system, and is created by the DBMS
for its usage under the directions and requirements provided by the DBA
As the DBMS needs to talk with the "three level architecture" of database and mapping
information along with all the database design information lies in the database schema.
The DBMS uses the data dictionary to access the database at each layer or model, for this
purpose the data dictionary of any type can be used but the integrated data dictionary is
far more efficient than any free standing data dictionary because an integrated data
dictionary is created by the DBMS itself and uses the same data accessing techniques etc.
o Free Standing
Second type of data dictionary is free standing data dictionary create by any CASE tool
and then attached to the database management systems. A number of case tools are
available for this purpose and help user designing the database and the database
applications as well in some modern forms of the CASE tools.
Cross Reference Matrix
This is a tool available in the data dictionary and helps us in finding entities of the
database and their associations. CRM is developed at the designing stage of the database;
we can say that at the time of creation of the user views of reports for certain users we
identify the material required by the users. In the cross reference matrix, on the Y axis we
specify the accessible components of the database such as transitions, reports, or database
objects and on the x axis we specify the attributes that will be accessed in the
corresponding accessed object.
Now the matrix gets a shape of two dimensional arrays on which we have accessible
objects of the database and on the other hand we have the elements which are available
for access through those objects. Then whichever data item is accessible through a certain
object we place a tick on the intersection of that row and column and thus we can easily
identify the deferent items accessed in different reports.
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Table 1: An example cross reference matrix
The cross reference matrix shown in table 1 lists different attributes against different
reports required by different user groups of an exam system. Rows in this matrix contain
different attributes and the columns contain different reports. Now the tick mark in the
cells represents the use or presence of attributes in different reports. This matrix
represents, on one side, the relative importance or use of different attributes. On the other
hand it also helps to identify different entity types and their defining attributes. The
attributes that are represented collectively on one or more reports are candidates of
combining into a single entity type. Although it is necessary that attributes appearing
together should be grouped into same entity type, but still they are candidates for
combining into the one.
Data Dictionary in not very necessary for using such a cross reference matrix, instead for
relatively small systems it can be created manually.
Outcome of the Analysis Phase
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In the preliminary study phase, database designers collect information about the existing
system from the users of the system. For this purpose they may interview different users
or concerned persons, or they may distribute questionnaires among different users and
ask them to fill them in and later may use these questionnaires in the analysis phase.
Designers represent their understanding of the working of existing system in the form of
DFDs and discuss it with the users to make it sure that they have understood all details of
the existing system and the requirements of different users groups.
The DFDs are input to the analysis phase, where designers analyze the requirements of
the users and establish the procedure to meet those requirements. From the database
perspective, in the analysis phase designers have to identify the facts or data that is
required to be stored in order to fulfill the users' requirements. For this purpose they may
use some CASE tools, like cross reference matrix. Generally, in the analysis phase,
designers prepare a draft or initial database design that they ultimately finalize in the next
phase, that is, the database design phase. So in short we can say, that DFDs are the output
of the preliminary phase and are input to the analysis phase. The initial design or a draft
form of design (generally in entity-relationship data model) is the output of the analysis
phase and input to the design phase. In the design phase, then you finalize the design.
The sequence of the activities mentioned above is not much important, however, the
activities mentioned are important and must be performed in order to have a correct
database or database application design. In the following lectures, we are going to study
different tools that are used in the design phase, that is, the data models. We will be
studying, both, the data models and their implementation in the database design phase.
Database Design Phase
Database design phase follows the analysis phase. Before starting the discussion on the
design activity, it will be wise if we clearly understand some basic concepts that are
frequently used in this phase.
o Database Design /Database Model
These terms can be used interchangeably for the logical structure of the database. The
database design/model stores the structure of the data and the links/relationships between
data that should be stored to meet the users' requirements. Database design is stored in
the database schema, which is in turn stored in the data dictionary.
o Database Modeling
The process of creating the logical structure of the database is called database modeling.
It is a very important process because the designing of the application provides us the
basis for running our database system. If the database is not designed properly the
implementation of the system can not be done properly. Generally the design of the
database is represented graphically because it provides an ease in design and adds
flexibility for the understanding of the system easily.
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Data Model
Data model is a set or collection of construct used for creating a database and producing
designs for the databases. There are a few components of a data model:
o Structure:
What structures can be used to store the data is identified by the structures provided by
the data model structures.
o Manipulation Language
For using a certain model certain data manipulations are performed using a specific
language. This specific language is called data manipulation language.
o Integrity Constraints
These are the rules which ensure the correctness of data in the database and maintain the
database in usable state so that correct information is portrayed in designing the database.
Generally these components are not explicitly defined in data models, they may be
available in some of the modern DBMSs but in traditional and general model, these may
not be available.
Significance of the Data Model
Data model is very important tool because it is something which is sued for designing the
database for a DBMS and no DBMS can exist independent of any data model, now if we
use a specific DBMS but are not sure about the data model it uses for data abase usage,
we can not create a proper database.
As a specific DBMS is base on the use of a specific data model so when using a DBMS it
is of great use to know that what structures, manipulation languages and integrity
constraints are implemented by a specific DBMS. As it is the only way to know the
facilities and functionalities offered by the DBMS.
This is the reason whenever we get a specific DBMS, it is explicitly mentioned with that
DBMS, that which data model this DBMS uses.
Types of Data Models
o Semantic Data Model
These are the data models which provide us better flexibility in implementing constraints,
better language utilities and better data structure constructs. As a result actions performed
using proper data and structure tools gives us better data designing and manipulation
facilities. A better data model provides better opportunities to express multiple situations
in the database design and as a result get better output from the tool or model in the form
of a better database design.
·
ER- Data Model
·
Object oriented data model
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·  Record Based Data Model
This is the second type of data models available to use and has three basic types
·
Hierarchical Data Model
·
Network Data model
·
Relational Data model
These models are records based and are not in similarity with those of semantic data
models. These models handle the data at almost all the three level of the three layers of
the database architecture. Semantic data models are generally used for designing the
logical or conceptual model of the database system, once very common example of the
semantic data model is ER-Data Model and is very much popular for designing databases.
No DBMS is based on ER Data model because it is purely used for designing whereas a
number of DBMS are available based on OO data model, network data model, relational
data model l and hierarchical data model.
Types of Database Design
Conceptual database design
This design is implemented using a semantic data model, for example for creating a
design for an organization database we can use and we do use the ER-Data model.
Logical Database design
This design is performed using a data model for which we have a DBMS available and
we are planning to run our database system that DBMS.
Physical Database Design:
The Logical design created using a specific data model and created after the analysis of
the organization, it needs to be implemented in a physical DBMS software so the
Physical database design is performed and the design created so far in the logical form
are implemented on that very DBMS.
By separating the three design levels we get the benefit of abstraction on one hand
whereas on the other hand we can create our logical and conceptual designs using better
design tools, which would have not been possible if we are using the same design-tool for
al the three levels. Moreover if in future there is a need to make a change in the physical
implementation of the data we will have to make no changes in the logical or conceptual
level of the database design , rather the change can be achieved by only using the existing
conceptual model and implementing it again on Physical model using a separate DBMS.
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Table of Contents:
  1. Introduction to Databases and Traditional File Processing Systems
  2. Advantages, Cost, Importance, Levels, Users of Database Systems
  3. Database Architecture: Level, Schema, Model, Conceptual or Logical View:
  4. Internal or Physical View of Schema, Data Independence, Funct ions of DBMS
  5. Database Development Process, Tools, Data Flow Diagrams, Types of DFD
  6. Data Flow Diagram, Data Dictionary, Database Design, Data Model
  7. Entity-Relationship Data Model, Classification of entity types, Attributes
  8. Attributes, The Keys
  9. Relationships:Types of Relationships in databases
  10. Dependencies, Enhancements in E-R Data Model. Super-type and Subtypes
  11. Inheritance Is, Super types and Subtypes, Constraints, Completeness Constraint, Disjointness Constraint, Subtype Discriminator
  12. Steps in the Study of system
  13. Conceptual, Logical Database Design, Relationships and Cardinalities in between Entities
  14. Relational Data Model, Mathematical Relations, Database Relations
  15. Database and Math Relations, Degree of a Relation
  16. Mapping Relationships, Binary, Unary Relationship, Data Manipulation Languages, Relational Algebra
  17. The Project Operator
  18. Types of Joins: Theta Join, Equi–Join, Natural Join, Outer Join, Semi Join
  19. Functional Dependency, Inference Rules, Normal Forms
  20. Second, Third Normal Form, Boyce - Codd Normal Form, Higher Normal Forms
  21. Normalization Summary, Example, Physical Database Design
  22. Physical Database Design: DESIGNING FIELDS, CODING AND COMPRESSION TECHNIQUES
  23. Physical Record and De-normalization, Partitioning
  24. Vertical Partitioning, Replication, MS SQL Server
  25. Rules of SQL Format, Data Types in SQL Server
  26. Categories of SQL Commands,
  27. Alter Table Statement
  28. Select Statement, Attribute Allias
  29. Data Manipulation Language
  30. ORDER BY Clause, Functions in SQL, GROUP BY Clause, HAVING Clause, Cartesian Product
  31. Inner Join, Outer Join, Semi Join, Self Join, Subquery,
  32. Application Programs, User Interface, Forms, Tips for User Friendly Interface
  33. Designing Input Form, Arranging Form, Adding Command Buttons
  34. Data Storage Concepts, Physical Storage Media, Memory Hierarchy
  35. File Organizations: Hashing Algorithm, Collision Handling
  36. Hashing, Hash Functions, Hashed Access Characteristics, Mapping functions, Open addressing
  37. Index Classification
  38. Ordered, Dense, Sparse, Multi-Level Indices, Clustered, Non-clustered Indexes
  39. Views, Data Independence, Security, Vertical and Horizontal Subset of a Table
  40. Materialized View, Simple Views, Complex View, Dynamic Views
  41. Updating Multiple Tables, Transaction Management
  42. Transactions and Schedules, Concurrent Execution, Serializability, Lock-Based Concurrency Control, Deadlocks
  43. Incremental Log with Deferred, Immediate Updates, Concurrency Control
  44. Serial Execution, Serializability, Locking, Inconsistent Analysis
  45. Locking Idea, DeadLock Handling, Deadlock Resolution, Timestamping rules