GEO 377P/387H Physical Climatology DIAGNOSTIC EXAM
12 pages
English

GEO 377P/387H Physical Climatology DIAGNOSTIC EXAM

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GEO 377P/387H Physical Climatology DIAGNOSTIC EXAM Name________________ You may not refer to any other materials during the exam. For each question (except otherwise explicitly stated), select the best answer for that question. Read all choices carefully before selecting an answer and make sure your choice answers the question asked. 1. The noteable The Year Without a Summer came after which volcanic eruption? A. Mt.
  • geologic past
  • ice sheets e.
  • ice volume
  • atmospheric co2
  • pressure
  • mountain
  • climate
  • carbon dioxide
  • temperature
  • time

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Nombre de lectures 43
Langue English

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UNIT 1
Lesson 7
Normalization
Hi! We are going to discuss one of the fascinating and important topics in a DBMS.
Analysis of Redundancies
Before we go in to the detail of Normalization I would like to discuss with you the
redundancies in the databases.
A redundancy in a conceptual schema corresponds to a piece of information that can be
derived (that is, obtained through a series of retrieval operations) from other data in the
database.
Examples of Redundancies


Deciding About Redundancies

The presence of a redundancy in a database may be decided upon the following factores

an advantage: a reduction in the number of accesses necessary to
obtain the derived information;

a disadvantage: because of larger storage requirements, (but, usually
1UNIT 1
at negligible cost) and the necessity to carry out additional operations in order
to keep the derived data consistent.

The decision to maintain or delete a redundancy is made by comparing the cost of
operations that involve the redundant information and the storage needed, in the case of
presence or absence of redundancy.

Cost Comparison: An Example
Now we will see the impact of redundancy with the help of an example.

Load and Operations for the Example



Table of Accesses, with Redundancy
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Issues related to Redundancies (Anomalies)
The time has come to reveal the actual facts why normalization is needed. We will look
in to the matter in detail now.
The serious problem with using the relations is the problem of update anomalies. These
can be classified in to
Insertion anomalies
Deletion anomalies
Modification anomalies
Insertion Anomalies
An "insertion anomaly" is a failure to place information about a new database entry into
all the places in the database where information about that 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 inadequately 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 insertions may be missed.
This can be differentiated in to two types based on the following example
Emp_Dept
EName SSN BDate Address DNumber DName DMGRSSN
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Smith 123456789 1965-01-09 Kandivly5 Research 333445555
Rajeev 333445555 1955-12-08 Vashi 5 Research 333445555
Greta 999887777 1968-07-19 Sion 4 Admin 987654321
Rajesh 987654321 1941-06-20 Dadar 4 Admin 987654321
First Instance: - To insert a new employee tuple in to Emp_Dept table, we must include
either the attribute values for the department that the employee works for, or nulls (if the
employee does not work for a department as yet). For example to insert a new tuple for
an employee who works in department no 5, we must enter the attribute values of
department number 5correctly so that they are consistent, with values for the department
5 in other tuples in emp_dept.
Second Instance: - It is difficult to insert a new department that has no employees as yet
in the emp_dept relation. The only way to do this is to place null values in the attributes
for the employee this causes a problem because SSN in the primary key of emp_dept
table and each tuple is supposed to represent an employee entity- not a department entity.
Moreover, when the first employee is assigned to that department, we do not need this
tuple with null values anymore.
Deletion Anomalies
A "deletion anomaly" 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 deleted from only one place in the
database; in an inadequately normalized database, information about that old entry may
need to be deleted from more than one place, and, human fallibility being what it is, some
of the needed additional deletions may be missed.
The problem of deletion anomaly is related to the second insertion anomaly situation
which we have discussed earlier, if we delete from emp_dept an employee tuple that
happens to represent the last employee working for a particular department, the
information concerning that department is lost from the database.
Modification Anomalies
4UNIT 1
In Emp_Dept, if we change the value of one of the attribute of a particular department-
say, the manager of department 5-we must update the tuples of all employees who work
in that department; other wise, the database will become inconsistent. If we fail to update
some tuples, the same department will be shown to have 2 different values for manager in
different employee tuple which would be wrong.
All three kinds of anomalies are highly undesirable, since their occurrence constitutes
corruption of the database. Properly normalized databases are much less susceptible to
corruption than are unnormalized databases.
Normalization
Designing a normalized database structure is the first step when building a database that
is meant to last. Normalization is a simple, commonsense, process that leads to flexible,
efficient, maintainable database structures. We'll examine the major principles and
objectives of normalization and denormalization, and then take a look at some powerful
optimization techniques that can break the rules of normalization.
What is Normalization?
Yes, but what is this normalization all about? If I am simply putting it, normalization is a
formal process for determining which fields belong in which tables in a relational
database. Normalization follows a set of rules worked out at the time relational databases
were born. A normalized relational database provides several benefits:
Elimination of redundant data storage.
Close modeling of real world entities, processes, and their relationships.
Structuring of data so that the model is flexible.
Normalization ensures that you get the benefits relational databases offer. Time
spent learning about normalization will begin paying for itself immediately.
Why do they talk like that?
Some people are intimidated by the language of normalization. Here is a quote from a
classic text on relational database design:
5UNIT 1
A relation is in third normal form (3NF) if and only if it is in 2NF and every nonkey
attribute is nontransitively dependent on the primary key.
Huh? Relational database theory, and the principles of normalization, was first
constructed by people intimately acquainted with set theory and predicate calculus. They
wrote about databases for like-minded people. Because of this, people sometimes think
that normalization is "hard". Nothing could be more untrue. The principles of
normalization are simple, commonsense ideas that are easy to apply.
Design versus Implementation
Now we will look in to the aspects regarding the tasks associated with designing and
implementing a database.
Designing a database structure and implementing a database structure are different tasks.
When you design a structure it should be described without reference to the specific
database tool you will use to implement the system, or what concessions you plan to
make for performance reasons. These steps come later. After you've designed the
database structure abstractly, then you implement it in a particular environment--4D in
our case. Too often people new to database design combine design and implementation in
one step. 4D makes this tempting because the structure editor is so easy to use.
Implementing a structure without designing it quickly leads to flawed structures that are
difficult and costly to modify. Design first, implement second, and you'll finish faster and
cheaper.
Normalized Design: Pros and Cons
Oh, now we’ve implied that there are various advantages to producing a properly
normalized design before you implement your system. Let's look at a detailed list of the
pros and cons:
Pros of Normalizing Cons of Normalizing
More efficient database structure. You can't start building the database before you
know what the user needs.
6UNIT 1
Better understanding of your data.
More flexible database structure.
Easier to maintain database
structure.
Few (if any) costly surprises down
the road.
Validates your common sense and
intuition.
Avoids redundant fields.
Ensures that distinct tables exist
when necessary.
We think that the pros outweigh the cons.
Terminology
There are a couple terms that are central to a discussion of normalization: "key" and
"dependency". These are probably familiar concepts to anyone who has built relational
database systems, though they may not be using these words. We define and discuss them
here as necessary background for the discussion of normal forms that follows.
Formal Definitions of the Normal Forms
• 1st Normal Form (1NF)
Def: A table (relation) is in 1NF if
1. There are no duplicated rows in the table.
2. Each cell is single-valued (i.e., there are no repeating groups or arrays

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