Colombia Email Lists
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Colombia Email Database
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Colombia Email List 2022
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Colombia Total Contacts: 150K
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Colombia has grown into an employment-generating center and an attractive trade partner for millions. It’s set to be a significant contribution to the world economy.
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Colombia Email Lists Fields
1. Company name
2. Email address
3. Mailing address
7. Phone number
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11. Web address
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Frequently Asked Questions
Our email list is divided into three categories: regions, industries and job functions. Regional email can help businesses target consumers or businesses in specific areas. Colombia email list broken down by industry help optimize your advertising efforts. If you’re marketing to a niche buyer, then our email lists filtered by job function can be incredibly helpful.
Ethically-sourced and robust database of over 1 Billion+ unique email addresses
Our B2B and B2C data list covers over 100+ countries including APAC and EMEA with most sought after industries including Automotive, Banking & Financial services, Manufacturing, Technology, Telecommunications.
In general, once we’ve received your request for data, it takes 24 hours to first compile your specific data and you’ll receive the data within 24 hours of your initial order.
After the completion of the payment, we will send you the email list in Microsoft Excel format.
We maintain the highest accuracy by performing strict quality checks and updating the Colombia Business Mailing List every 30 days. Our team makes several verification calls and sends more than 8 million verification emails to keep the records free from errors and redundancy.
Yes. The data we offer in our Colombia Business Email List is highly trustworthy as our team of specialists compiles it using authentic and reliable sources. Some of the sources include – business websites, government records, B2B directories, surveys, trade shows, yellow pages, local directories, business meetings, conferences, newsletters, magazine subscriptions, etc.Our Colombia Decision Makers Email List is highly reliable as it comes with upto 95% accuracy and beyond 95% deliverability rate. Our team spends significant time and effort to deliver you such a precise list.
Our data standards are extremely high. We pride ourselves on providing 97% accurate Email Lists, and we’ll provide you with replacement data for all information that doesn’t meet your standards our expectations.
Yes. Our Colombia Business Database lets you customize the given records based on specific campaign requirements. The selects for customization include geographical location, job title, SIC code, NAICS code, company revenue, and many more.
Yes. By availing our Colombia Email List, you can easily gain access to all the B2B marketing information that is crucial for successful campaign performance. The data fields include – first name, last name, location, phone number, company name, job title, website, fax, revenue, firm size, SIC code, NAICS code, and others.
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Database System : Concepts and Design
An organization must have accurate and reliable data for effective decision
making. To this end, the organization maintains records on the various facets maintaining
relationships among them. Such related data are called a database. A database system is
an integrated collection of related files, along with details of the interpretation of the data
contained therein. Basically, database system is nothing more than a computer-based
record keeping system i.e. a system whose overall purpose is to record and maintain
information/data Colombia database for sale .
A database management system (DBMS) is a software system that allows access
to data contained in a database. The objective of the DBMS is to provide a convenient
and effective method of defining, storing and retrieving the information contained in the
database. The DBMS interfaces with the application programs, so that the data contained
in the database can be used by multiple applications and users. In addition, the DBMS
exerts centralized control of the database, prevents fraudulent or unauthorized users from
accessing the data, and ensures the privacy of the data Colombia database for sale.
Generally a database is an organized collection of related information. The
organized information or database serves as a base from which desired information can
be retrieved or decision made by further recognizing or processing the data. People use
several databases in their day-to-day life. Dictionary, Telephone directory, Library
catalog, etc are example for databases where the entries are arranged according to
alphabetical or classified order Purchase Colombia email lists .
The term ‘DATA’ can be defined as the value of an attribute of an entity. Any
collection of related data items of entities having the same attributes may be referred to as
a ‘DATABASE’. Mere collection of data does not make it a database; the way it is
organized for effective and efficient use makes it a database Colombia quality email lists .
Database technology has been described as “one of the most rapidly growing areas
of computer and information science”. It is emerged in the late Sixties as a result of
combination of various circumstances. There was a growing demand among users for
more information to be provided by the computer relating to the day-to-day running of
the organization as well as information for planning and control purposes. The
technology that emerged to process data of various kinds is grossly termed as
‘DATABASE MANAGEMENT TECHNOLOGY’ and the resulting software are
known as ‘DATABASE MANAGEMENT SYSTEM’ (DBMS) which they manage a
computer stored database or collection of data.
* Librarian , Fr.C.Rodrigues Institute of Technology, Sector 9A,Vashi, Navi Mumbai – 400 703.
**Librarian, Tata Institute of Social Sciences, Deonar, Mumbai – 400 088.
Database System : Concepts and Design
3 Colombia email leads
1.1Meaning and Definition of Database :
An entity may be concrete as person or book, or it may be abstract such as a loan
or a holiday or a concept. Entities are the basic units of objects which can have concrete
existence or constitute ideas or concepts. An entity set is a set of entities of the same type
that share the same properties or attributes .
An entity is represented by set of attributes. An attribute is also referred as data
item, data element , data field, etc. Attributes are descriptive properties possessed by each
member of an entity set. A groping of related entities becomes an entity set.
For ex : In a library environment,
Entity Set -Catalogue –
Entity -of Books, Journals, AV-Materials, etc
Attributes – contains Author, Title, Imprint, Accn. No., ISBN, etc
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The word ‘DATA’ means a fact or more specially a value of attribute of an entity.
An entity in general, may be an object, idea, event, condition or situation. A set of
attributes describes an entity. Information in a form which can be processed by a raw
computer is called data. Data are raw material of information.
The term ‘BASE’ means the support, foundation or key ingredient of anything.
Therefore base supports data.
A ‘DATABASE’ can be conceived as a system whose base, whose key concept, is
simply a particular way of handling data. In other words, a database is nothing more than
a computer-based record keeping. The objective of database is to record and maintain
information. The primary function of the database is the service and support of
information system which satisfies cost.
In short, ” A database is an organized collection of related information stored with
minimum redundancy, in a manner that makes them accessible for multiple application”.
1. Prakash Naveen : “Database is a mechanized shared formally defined and central
collection of data used in an organization”.
2. J.M.Martin : ” Database is a collection of inter-related data stored together without
harmful or unnecessary redundancy to serve multiple application”.
3. Mac-Millan dictionary of Information Technology : defines a database as a ” a
collection of inter-related data stored so that it may be accessed by authorized users with
simple user-friendly dialogues”.
1.2 Functions of Database : Colombia Email Database
The general theme behind a database, is to handle information as an integrated whole.
The general objective is to make information access easy, quick, inexpensive and flexible
for the user.
Controlled redundancy : Redundant data occupies space and therefore is wasteful.
By controlled redundancy, system performance is improved.
User-friendly (i.e. ease to learning and use) : A major feature of a user-friendly
database package is how easy it is to learn and use.
Database System : Concepts and Design
Data independence : means it allows for changes at one level of the database without
affecting the other levels i.e. changing hardware and storage procedures or adding
new data without having to rewrite application program.
Economy (i.e. more information at low cost) : Using, storing and modifying data at
low cost are important.
Accuracy and integrity : Even if redundancy is eliminated, however, the database
may still contain incorrect data. Centralized control of the database helps in avoiding
these situation. The accuracy of a database ensures that data quality and content
remain constant. Integrity controls detect data inaccuracies where they occur.
Recovery from failure : With multi-user access to a database, the system must
recover quickly after it is down with no loss of transactions. It helps to maintain data
accuracy and integrity.
Privacy and Security : For data to remain private, security measures must be taken
to prevent unauthorized access i.e. complete jurisdiction over the operational data.
DBMS ensures proper security through centralized control.
Performance : It emphasizes response time to inquiries suitable to the use of the data
depends on the nature the user-database dialogue.
Database retrieval, analysis, storage :.It facilitates Database retrieval, analysis and
Compatibility : Usefulness i.e. hardware/software can work with different
Concurrency control : is a feature that allows simultaneous access to a database,
while preserving data integrity.
Support : Support of complex file structure and access path. Ex : MARC
Data Sharing : A database allows sharing of data under its control by any number of
Standards can be enforced : Standardizing stored data formats is particularly
desirable as an aid to data interchange between systems.
1.3 Types of Databases : Colombia Leads
Database is considered as a central pool of data which can be shared by a
community of users. There are three yard sticks to determine the nature of data we can
deal with. They are :
a. Whether data is free of format or whether it is formatted.
b. Whether definition of data is of the same size as data itself.
c. Whether the data is active or passive.
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Whether these yard sticks are applied to data. We can classify database into four kinds
1.3.1 Bibliographic Databases
1.3.2 Knowledge Databases
1.3.3 Graphic-Oriented Databases
1.3.4 Decision-making Databases
Database System : Concepts and Design
1.3.1 Bibliographic Databases : have data which is free of format (unformatted data).
They are composed of textual data which, by it’s very nature, displays little or no format.
Such databases are often used in Library and information system. Here data could be
composed of abstracts of books and such documents with key words and key phrases.
Through the abstract, one can determine the document is of interest or not. Bibliographic
database contains descriptive information about documents, titles, authors, Journal name,
Volume and Number, date, keywords, abstract, etc.
1.3.2 Knowledge Databases : are used in Artificial Intelligence applications. The data
contained in these is discrete and formatted. In these there are typically many kinds of
data, with only a very few occurrence of each kind. Such databases having the size of the
data is as large as the definition of the data.
1.3.3 Graphic-Oriented Databases : could possibly used in Computer-Aided Design
(CAD). The data in such database is characterized as being active. This means that data is
a procedure capable of being executed. Any modification can be made in data, as the
above 1 and 2 cannot be executed in a computer. Colombia Email Database
Ex : Computer-Aided Design (CAD)
Computer-Aided Learning (CAL)
Computer-Aided Instruction (CAI)
1.3.4 Decision-making Databases : are used in corporate management and allied
administrative tasks. Using data contained in these databases, one could handle problem
like resource planning and sales forecasting. These databases are characterized by the fact
and their data contents are :
b. Far longer than description
These Decision-making databases are often referred to as just databases.
Depending upon the kind of databases being handled Database Management Systems
(DBMS) can be classified as for example : Bibliographic Database Management Systems,
Knowledge Database Management Systems and so on.
1.4 Concept of Data Structure :
Data are structured according to the Data model. A group of data elements handled
as a unit. Ex : Book details – is a data structure consisting of the data elements – Author
name, Title, Publisher’s name, ISBN and Quantity.
There are several different approaches to analyzing the logical structure of data in
complex databases. Although all DBMS’s have a common approach to data management,
they differ in the way : the structure of data.
There are three types of data structure, viz
1.4.1 List Structure
1.4.2 Tree / Hierarchical Structure
1.4.3 Network Structure
Database System : Concepts and Design
1.4.1 List Structure : A list is nothing morethan a special data structure made up of data
record where the Nth record is related (N-1) and (N-2) simply because of positioning.
This brings one-to-one relationship. This structure is illustrated as below :
Fig. Simple List Structure
1.4.2 Tree / Hierarchical Structure : A tree structure is a non-linear multilevel
hierarchical structure in which each node may be related to N-nodes at any level below it.
But to only one node above it in the hierarchy.
The entry is from the top and the direction of search or passing is downward and
no branches on the tree trunk (touch).
Data storage in the form of a parent-child relationship. The origin of a data tree is
the root. Data located at different levels along a particular branch from the root is called
the node. The last node in the series is called the leaf. Each child has pointers to
numerous siblings and there is just one pointer to the parent thus resulting in a one-tomany relationship.
Fig. Tree / Hierarchical Structure Colombia Mailing Database.
1.4.3 Network Structure : Network Structure is another form of hierarchical structure.
In this view as in the hierarchy approach, the data is represented by records and links.
However, a network is a more general structure than a hierarchy.
A network structure allows relationships among entities. Here user views the
database as a number of individual record occurrences in which a given node may have
any number of subordinates nodes. Network Structure is equated to a graph structure.
This brings many-to-many relationship. The relationship between the different item is
called as sets.
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Database System : Concepts and Design
Fig. Network Structure
2. Introduction to Database Management System (DBMS) :
A DBMS is essentially a collection of interrelated data and a set of programs to
access this data. This collection of data is called the Database which facilitates storage,
retrieval and management of information.
A DBMS consists of a collection of interrelated data and a set of programs to
access those data. The collection of data, usually referred to as the database. The primary
goal of DBMS is to provide an environment that is both convenient and efficient to use in
retrieving and storing database information.
Database systems are designed to manage large bodies of information. The
management of data involves both the definition of structures for the storage of
information and the provision of mechanisms for the manipulation of information.
In addition, the database system must provide for the safety of the information
stored, despite system crashes or attempts at unauthorized access. If data are to be shared
among several users, the system must avoid possible anomalous results.
DBMS is a software system which manages the databases providing facilities for
organization access and control. DBMS is like an operator for database. Database is
passive where as DBMS is active one. It provides the interface between the data file on
disk and the program that requests processing.
2.1 Objectives of DBMS :
The primary objective of a DBMS is to provide a convenient environment to
retrieve and store database information. It support single-user and multi-user
environment. Colombia Mail Leads.
Provide for mass storage of relevant data.
Make access to the data easy for user.
Provide prompt response to user requests for data.
Make the latest modifications to the database available immediately.
Eliminate the redundant data.
Allow multiple users to be active at one time.
Allow for growth in the database system.
Protect the data from physical harm and unauthorized access.
Control over data correctness, consistency, integrity, security, etc.
Database System : Concepts and Design
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2.2Functions of DBMS :
According to the Codd, a comprehensive DBMS provides eight major functions. viz
Data storage, retrieval and update : A database may be shared by many users thus,
the DBMS must provide multiple users views and allow users to store, retrieve and
update easily and effectively.
Data dictionary and directory : The DBMS must maintain a user accessible data
Transaction integrity : A transaction is sequence of steps that constitute some well
defined business activity. To maintain transaction integrity, the DBMS must provide
facilities for the user or application program to define transaction boundaries i.e. the
logical beginning and end of transactions. The DBMS should then commit changes
for successful transactions and reject changes for aborted transactions.
Recovery Services : The DBMS must be able to restore the database in the events of
some system failure. Sources of system failure include operator error, disk head
crashes and program errors.
Concurrency Control : Since a database is a shared by multiple users, two or more
users may attempt to access the same data simultaneously. If two users attempt to
update the same data record concurrently, erroneous results may occur. Since the
safeguards must be built into the DBMS to prevent or overcome the efforts of
Security mechanisms : Data must be protected against accidental or intentional
misuse or distraction. The DBMS provides mechanisms for controlling access to data
and for defining what action may be taken by each user.
Data Communication interface : Users often access a database by means of remote
terminates in telecommunication network. A telecommunication monitor is used
process the flow of transactions to and from the remote terminates. The DBMS must
provide a interface with one or more telecommunication monitors so that all the
necessary functions are performed and the system will assist, rather than a burden on
the end user. Colombia Emails
Integrity services: The DBMS must provide facilities that assist users in
manufacturing the integrity of their data. A variety of edit checks and integrity
constraints can be designed into the DBMS and its software interfaces. These checks
are normally administered through the data dictionary.
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2.3Components of a DBMS :
A DBMS is a complex structure that is used to manage, store and manipulate data
and the metadata used to describe the data. It is utilized by a large variety of users to
retrieve and manipulate data under its control. A system is a composed of set of
1. Atleast one person who owns and is responsible for the database.
2. A set of rules and relationship that defines and governs the interactions among
elements of the database.
Database System : Concepts and Design
3. People who put data into the database.
4. People who get data out of the database.
5. The database itself.
3. Database Design :
Database design is the design of the database structure that will be used to store
and manage data rather than the design of the DBMS software. Once the database design
is completed, the DBMS handles all the complicated activities required to translate the
designer’s view of the structures into structures that are usable to the computer.
A poorly designed database tends to generate errors that are likely to lead to bad
decisions. A bad database design eventually can be self correcting: organizations using
poorly designed databases often fail because their managers do not have access to timely
(or even correct) information, thereby dominating the bad database design.
The availability of a DBMS makes it possible to tackle far more sophisticated
uses of the data resources, if the database is designed to make use of that available power.
The kinds of data structures created within the database and the extent of the relationships
among them play a powerful role in determining how effective the DBMS is. Therefore,
database design become a crucial activity in the database environment.
Database design is made much simpler when we use models. A Database model is
a collection of a logical constructs used to represent the data structure and the data
relationships found within the database i.e. simplified abstractions of real-world events or
conditions. If the models are not logically sound, the database designs derived from them
will not deliver the database system’s promise to effective information drawn from an
efficient database. “Good models yield good database design that are the basis for good
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3.1 Goals of Database Design :
Database Design normally involves defining the logical attributes of the database
designing the layout of the database file structure.
The main objectives of database design is
1. To satisfy the information content requirement of the specified user and application.
2. To provide a natural and easy way to understand structuring of the information.
3. To support processing requirements and any performance objectives such as
i. Response time
ii. Processing time
iii. Storage space
The main objective of the database design is to ensure that the database meets the
reporting and information requirements of the users efficiently. The database should be
designed in such a way that :
i. It eliminates or minimizes data redundancy.
ii. Maintains the integrity and independence of the data.
Database System : Concepts and Design
3.2 Logical and Physical View of Database :
Computer Application DBMS Operating Database
User logical Program logical Overall logical Physical View
View View View
(Schema) Colombia Contact Database
1 2 3 4
* IOCS- Input/ Output Control System
In database design, several views of data must be considered along with the
persons who use them. There are three views :
1. The overall logical view
2. The program logical view
3. Physical view
The logical view is what the data look like, regardless of how they are stored
physical view is the way data exist in physical storage, it deals with how data are stored,
accessed or related to other data in storage.
Four views of data : THREE logical views and ONE physical view.
The logical view as the user’s view, the programmer’s view and the overall logical
The overall logical view (schema) helps the DBMS to decide what data in storage
it should act upon as required by the application program.
A DBMS is a collection of interrelated files and a set of programs that allow users
to access and modify these files. A major purpose of a database system is to provide users
with an abstract view of the data i.e. the system hides certain details of how the data are
stored and maintained.
3.3 An Architecture for a Database System :
3.3.1 Data Abstraction : Many database system users are not computer trained,
developers hide the complexity from users through several level of abstraction, to
simplify users’ interaction with the system. The architecture is divided into three general
levels : internal, conceptual and external.
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Database System : Concepts and Design
a. Internal / Physical level : The internal level is the one closest to physical storage i.e.
one concerned with the way in which the data is actually stored. It is the lowest level of
abstraction describes how the data are actually stored. At the physical level, complex low
level data structures are described in detail.
b. Conceptual / Logical level : is a “level of indirection” between the internal and
external. The next higher level of abstraction describes what data are stored in the
database, and what relationships exists among those data. The entire database is thus
described in term of a small number of relatively simple structures. This level is used by
Database Administrators(DBA), who must decide what information is to be kept in the
c. External / View level : The external level is the one closest to the users, i.e. the one
concerned with the way in which the data is viewed by individual users. It is the highest
level of abstraction describes only part of the entire database. Despite the use of simpler
structures at the logical level, some complexity remains, because of the large size of the
database. Many users of the database system will not be concerned with all this
information. Instead, such users need to access only a part of the database so that their
interaction with the system is simplified, the view level of abstraction is defined. The
system may provide many views for the same database.
If the external level is concerned with the individual user views, the conceptual
level may be thought of as defining a community user view. In other words, there will be
many “external views,” each consisting of a more or less abstract representation of some
portion of the database, and there will be a single “conceptual view,” consisting of a
similarity abstract representation of the database in its entirety. Likewise, there will be a
single “internal view,” representing the total database as actually stored.
3.3.2 Instances and schemes : Databases change over time as information is inserted or
deleted. The collection of information stored in the database at a particular moment is
called an instance of the database. The overall design of the database is called the
database schema. Schemas are changed infrequently, if at all.
Database System : Concepts and Design
The view at each of these levels is described by a Schema. A schema is an outline
or a plan that describes the records and relationships existing in the view. The word
schema is used in the database literature for the plural instead of schemata, the
grammatically correct word. The schema also describes the way in which entities at one
level of abstraction can be mapped to the next level.
Database systems have several schemas, partitioned according to the levels of
abstraction(that we discussed). At the lowest level is the physical schema; at the
intermediate level is the logical schema; and at the highest level is a subschema. In
general, database system support one physical schema, one logical schema and several
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3.3.3 Data independence : The ability to modify a schema definition in one level
without affecting a schema definition in the next higher level is called data
independence. There are two levels of data independence viz.
a. Physical data independence : is the ability to modify the physical schema without
causing application programs to be rewritten. Modifications at the physical level are
occasionally necessary to improve performance.
b. Logical data independence : is the ability to modify the logical schema without
causing application programs to be rewritten. Modifications at the logical level are
occasionally necessary whenever the logical structure of the database is altered.
Logical data independence is more difficult to achieve than is physical data
independence, since the application programs are heavily dependent on the logical
structure of the data that they access.
3.3.4 Database languages : Data Sublanguage (DSL) is a subset of the total language i.e.
concerned with the database objects and operations. DSL is a user’s / query language
which is being embedded in a host language. In principle, any given DSL is really
combination of two languages :
a. Data Definition Language (DDL) : is one which specify the database schema. A
database schema is specified by a set of definitions. This definition includes all the
entities and their associated attributes as well as the relationships among the entities. The
result of compilation of DDL statements is a set of tables i.e. stored in a special file called
data dictionary or data directory, which caontains metadata i.e. data about data. This
file is consulted before actual data are read or modified in the database system.
The storage structure and access methods used by the database system are
specified by a set of definitions in a special type of DDL called a data storage and
b. Data Manipulation Language (DML) : is one which is used to express data queries
and updates i.e. manipulate data in the database. DML helps in
– the retrieval of information stored in the database
– the insertion of new information into the database
– the deletion of information from the database
– the modification of information stored in the existing database
Database System : Concepts and Design
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A DML is a language that enables users to access or manipulate data as organized
by the appropriate data model. There are basically two types :
i. Procedural DMLs : requires a user to specify what data are needed and how to get those
ii.Non- Procedural DMLs : requires a user to specify what data are needed without
specifying how to get those data.
Mapping : There are two levels of mapping :
i. one between the external and conceptual levels of the system; and
ii. the other between the conceptual and internal levels.
The Conceptual/Internal mapping defines the correspondence between the
conceptual view and the stored database. The External/Conceptual mapping defines the
correspondence between a particular external view and the conceptual view.
Fig. Database System Architecture
The DBMS is the software that handles all access to the database. Conceptually
what happens is the following :
1. A user issues an access request, using some particular Data Manipulation
2. the DBMS intercepts the requests and interprets it;
Database System : Concepts and Design
14 Colombia Email Leads.
3. the DBMS inspects, in turn the external schema, the external/conceptual mapping, the
conceptual schema, the conceptual/internal mapping, and the storage structure definition;
4. the DBMS performs the necessary operations on the stored database.
3.4 Storage Structures :
Storage Structures describes the way in which data may be organized in secondary
storage i.e. direct access media such as disk packs, drums and so on.
Fig : The Stored record interface
User operations are expressed (via the DML) in terms of external records, and
must be converted by the DBMS into corresponding operations on internal or stored
records. These later operations must be converted in turn to operations at the actual
hardware level, i.e. to operations on physical record or blocks. The component
responsible for this internal/physical conversion is called an access method. Its function
is to conceal all device-dependent details from the DBMS and to present the DBMS with
a stored record interface. The stored interface thus corresponds to the internal level, just
as the user interface corresponds to the external level. The Physical record interface
corresponds to the actual hardware level.
The stored record interface permits the DBMS to view the storage structure as a
collection of stored files, each one consisting of all occurrences of one type of stored
record(see architecture of DBMS).Specifically, the DBMS knows (a). what stored files
exist, and, for each one, (b) the structure of the corresponding stored record, (c) the stored
field(s), if any, on which it is sequenced, and (d) the stored field(s), if any, that can be
used as search arguments for direct access. This information will all be specified as part
of the storage structure definition.
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Database System : Concepts and Design
3.5 Phases in Database Design :
3.5.1 First phase : The overall purpose of the database initial study is to
a. analyze the organization/system situation
b. define problem and constraints
c. define objectives
d. define scope and boundaries.
3.5.2 Second phase : The second phase focuses on the design of the database model that
will support organization operations and objectives.
In this phase, we can identify six main phases of the database design :
I. Requirements collection and analysis
II. Conceptual database design
III. Choice of DBMS
IV. Data model mapping
V. Physical database design
VI. Database system implement
Fig : Procedure flow in database design
Database System : Concepts and Design
I. Conceptual Design : It involves two parallel activities
a. Conceptual scheme design
b. Transaction design
a. The first activity of Conceptual design examines the data requirements resulting
from Phase 1 and produces a Conceptual database scheme.
b. The second activity Transaction design examines the database applications
analyzed in Phase 1 and produces high-level specifications for the presentation. The goal
of Phase 2 is to produce a Conceptual schema for the database i.e. independent of a
In this stage, data modeling is used to create an abstract database structure that
represents real world objects in the most realistic way possible. The conceptual model
must embody a clear understanding of the transaction or system and its functioning areas.
This design is software and hardware independent.
i. Data Analysis and Requirements : Before we can effectively design a database, we
must know the expectations of the users and the intended users of the database as much
detail as possible. The process of identifying and analyzing the intended users is called “
Requirements collection and analysis”.
It is the first step in Conceptual design is to discover the data element
characteristics. Appropriate data element characteristics are those that can be performed
into appropriate information. Therefore designers has to focussed on :
a. information needs;
b. information users ;
c. information sources; and
d. information constitution.
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In order to develop an accurate data model, the designer must have a thorough and
complete understanding of the organization’s data. Consequently, the designer must
identify the organization’s goals and objectives, rules and analyze their impact on the
nature, role and scope of data.
ii. Entity-Relationship modeling and normalization : Before creating the E-R model(
data model) the designer must communicate and enforce appropriate standards to be used
in the documentation of the design. Failure to standardize documentation often means a
failure to communicate later. And communication failures often leads to poor design
iii. Data model verification : The E-R model must be verified against that the proposed
system processes in order to corroborate that the intended processes can be supported by
the database model.
Verification requires that the model can be run through a series of test against :
a. End user data views and their required transactions : SELECT, INSERT, UPDATE and
DELETE operations and queries and reports.
b. Access paths, security and concurrency control.
c. System / Business-imposed data requirements and constraints.
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Dat,alog and Recursion
After completing this unit, you’ll be able:
Learn about datalog and recursion.
Define the datalog program evaluation
Discuss the difference between generalization and specialization.
Although relational algebra is able to provide a range of useful operations for relationships, there are a few
computations that are not able to be written in the form of an expression for relational algebra. Common type of
processing the data we are unable to communicate in the form of relational algebra is an infinite process, recursively
A defined series of expressions that are similar to each other.
10.1 Datalog , Recursion and
We are now defining a relationship called Components which defines the parts that make up each.
Look over the following programs, or the collection of rules
These are rules that are part of Datalog the relational query language that is influenced by Prolog The well-known logic
programming language. In fact the notation is based on Prolog. The first rule must be understood as
follows: Colombia Emails Database.
In all cases, for the values Part, Subpart and Qty,
If there’s a Tuple in the Assembly,
There must be a Tuple in Components.
The second rule can be translated as follows:
In all cases, Part of Part2, Part2, Subpart and Qty
If there’s a Tuple in Assembly and
A triplication in Components,
Then there has to be a Tuple in Components.
The right-hand side of the symbol is known as”the rule’s body and the one that is left of the symbol is called
The rules head. The symbol signifies the logical implications; If the tuples in the
If the body is within the databases, it’s implied that the tuple listed in the first paragraph of the rule should be present in the database as well.
Be in the database.
Thus, if presented with a set of Assembly as well as Components tuples, every rule is able to be used to
Deduce, or infer, new tuples which belong to Components. This is why databases are able to infer, or deduce, new tuples.
Support Datalog rules are commonly known as deductive database systems.
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Every rule is an inference-making template by assigning constants the variables that
In a normal case that is, we can draw certain Components tuples. For instance when we set Part=trike
Subpart=wheel and Qty=3, we can conclude that is contained in Components. In analyzing
every tuple of Assembly is a tuple at a time, and the first rule permits us to determine the succession of tuples created through
projecting to project Assembly onto its initial two fields can be done by using Components.
The second rule permits us to mix Components that we have previously found with
Assembly tuples to determine the tuples of Components that are new. It is possible to apply the second rule by examining
the cross-products from Assembly and (the currently running example of) Components, and assigning values to
the variables used in your cross-product rule. Repeat the process for each row taking one row at. Take note of how
Repeated use of the variable Part2 stops specific rows from being a part of the cross product.
any new tuples. In the end, it specifies an equality join requirement on Assembly and Components.
The tuples that result from one of the applications of this rule can be seen on Figure 10.1. (In addition,
Components includes the tuples that are obtained through the application of the first rule. are not displayed.)
The tuples that result from another use of the rule is displayed in figure 10.2. Be aware that every
The tuple in the figure 10.1 is a reinferred tuple. Only the two tuples in Figure 10.1 are completely new.
The second rule is applied three times will not result in any additional tuples. The set of
Components tuples in Figure 10.2 contains all the tuples which can be inferred from using the
Two Datalog rules for defining Components as well as the specific Assembly. There are two Datalog rules that define Components and the given Assembly. The components
of a trike may now be identified by selecting all Components tuples having the trike’s value in the
Every application of the Datalog rule is explained in terms of relational algebra. The first rule
In our example program, it simply adds projection on the Assembly relation, and then adds the resultant
Tuples are added to the Components relationship that is initial empty. The second rule connects Assembly
by using Components. Then it creates by projecting. The results of each rule’s application is merged
by using the existing set Components tuples by using union.
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The one and only Datalog operation that is not relationship algebra would be the repetition of the application of
the Components rules until the time that no new tuples are created. The repeated application of
A set of rules is known as the fixpoint operation.
This section concludes by writing the Datalog description of Components in terms of an extended
SQL uses the syntax that was proposed by the draft SQL:1999, and currently supported by IBM’s DB2
Version 2 DBMS Version 2:
The WITH clause creates the relation as part of the query definition. The relationship is similar to
to a view, however the scope of a relationship that is created with WITH is specific in relation to the definition of the query. It is a relation that belongs
RECURRENT keyword indicates it is a sign that tables (in our case Components) is defined recursively.
The form in the definition resembles Datalog rules. Datalog rules. It is also true that if we wanted to
identify the elements of a specific part such as trike, then we just replace the previous line
that includes the following:
Evaluation of the Datalog Program
We categorize the relationships within Datalog programs Datalog program as output relations or input relationships.
Output relations are defined using rules (e.g. Components) Input relations come with a set tuples
explicitly identified (e.g., Assembly). Based on the relation between the input relations, we have to calculate instances
for the output relationships. The definition of the term “datalog” is determined by the output relations. Datalog program is typically described in two distinct ways.
methods, both of which basically define the relation instances for output relations. Technically,
A query is a decision in one of the input relationships (e.g. All Components tuples C ) with
C.part = trike). But, the meaning of a query is obvious when we know how the relation
instances are linked to the output relations of the Datalog program.
The initial approach to define what is a Datalog program is called the semantics of the least model
and allows users to get a better understanding of the program, without having to consider how the program will be used
to be carried out. This means that the meaning is declarative, just like the logic of relational calculus and
Not operational as a the semantics of relational algebra. This is significant due to the absence of
Recursive rules can make it difficult to comprehend the program from an assessment strategy.
The second method, referred to as the semantics of the lowest fixpoint provides a conceptual strategy for evaluating
to calculate the desired relation instances. This provides the foundation for to compute the desired query instances.
within an DBMS. The most efficient evaluation methods are implemented in the actual implementation, however, they do not have the same efficiency. Colombia Email Listings.
The accuracy of the approach is demonstrated by their compatibility with the least fixpoint method. The
Fixpoint semantics are therefore operational and plays a part like that of relational algebra.
semantics for non-recursive queries.
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Least Model Semantics
We want our users to be able to comprehend the Datalog program, by comprehending each rule
independent of any other rules, having the meaning that if the body is true, then the head is also true. This
A quick and intuitive read of the rule implies that, given specific instances of relation names, there are name of the relationship
which appear within the body of a regulation, the instance of a relation for the relation in the body of a rule.
The rule should contain the requisite list of tuples. If the name of a relationship R is found on the minds of more than one
rules that the relation instance for R must meet the intuition-based interpretation of all these rules. However,
We don’t wish to include tuples in the R instance unless they are needed for one reason or another.
of the rules that define R. This means that we will only compute tuples of the rules defining R that are backed by one or more
rules for R.
To understand these concepts in a precise manner it is necessary to explain concepts about models and at the very least, models. A
Models are collections of relations instances one instance for every relation in the program which
It meets the following conditions. Each rule of the program, we make a change, we will replace every rule.
In the rule, if a variable is represented by a constant that is corresponding to it The following is true:
1. If each tuple of the body (obtained by replacing variables by constants) is in
the relation instance that is corresponding,
2. Then the tuple that was generated by the head (by the assignment of constants to variables )
The head appears) is also present in the related relation case.
Note that the examples for input relations are provided, as is the definition of the term model is given.
The main goal is to limit the number of instances of output relations. This basically restricts the instances of the output.
Take a look at the rule
Components(Part, Subpart) :- Assembly(Part, Part2, Qty),
Imagine that we replace Part with the constant wheel Part2 by tire Qty by 1 and
Subparts by rim:
Components(wheel, rim) :- Assembly(wheel, tire, 1),
Consider A as an example in Assembly and C is an instance of Components. If A is a tuple,
and C is the Tuple Then C must contain the tuple rim> for an instance A or C form an example. Of course, both instances A and C
Must meet the inclusion requirements as illustrated above for each assignment of constants
The rules’ variables the following way: If the tuples of the body of rule are B and A, then the head tuple must be in A.
to be C.
Safe Datalog Programs
Take a look at the following program:
Complex Parts(Part) Complex Parts(Part) Assembly(Part, Subpart Qty) Qty > 2.
As per this rule, a complex part is defined as a part with greater than 2 copies
Any one subpart. Each part that is that is mentioned in the Assembly relation, we are able to quickly determine if it’s an assembly or a
Complex component. Contrary to this, think about this program
This variation aims to connect the price to each intricate element. But the variable Price
The rule does not mention it inside the rules body. This implies that an infinity of tuples are required.
Included in the models that is part of the program! To understand this imagine that we replace Part with the variables Part by
the constant trike SubPart by wheel and Qty divided by the constant trike, SubPart by wheel, and Qty by. This is a variation of the rule that includes
the only remaining variable is price:
Price Parts(trike,Price) Price Parts (trike, Price) Assembly(trike wheel, trike, 3) 3 > 2.
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Any change in a constant’s Price will result in an tuple that can be used in the output relationship.
Price Parts. In this case, for example substituting Price with 100 will give us the Tuple Price Parts(trike,100). If you are able to calculate the
the simplest model of an application is not finite, even for one of its input relationships, then we can say that the
The program is not safe.
Database systems prohibit dangerous programs because they require that every variable within the head of the program be accounted for.
rules must be included within the rule must also be reflected in the. The rules are referred to as limited in their range, and each
Range-restricted Datalog program is an infinite minimum model when the instances of the input relation are
Negative and Recursive Queries
The two rules could be viewed as a way of dividing into parts (those which are listed in the
The first column in the Assembly table) divided into two classes: the Big and Small classes. The first rule categorizes the term “big” to
It is the group of components that comprise at minimum three copies of the subpart and are not classified as small.
parts. The second rule is to define Small as the group of parts that aren’t considered to be big.
In the particular instance of Assembly depicted in Figure 10.3 The trike is the only component.
that is composed of at minimum three copies of the subpart. If the tuple could be described as the category of Small or Big? If we
Apply the first rule first and the second rule. This Tuple will be in the Big. In order to follow the first rule, we
Take a look at the tuples when you are in Assembly Select those that have Qty > 2. (which is basically ) Remove those
that are present in the current version of Small (both Small and Big were initially empty) Add the
Tuples left to the Big. So applying the first rule will add to Big. To Big.
In the same way, we can also see that, if we apply the second rule used prior to the first one, is an addition to Small
instead of Big
This program contains two fixpoints, neither one of which is less than the other, as is shown in
Figure 10.4. The first fixpoint contains an enormous tuple, which is not present at the fixpoint that is second Therefore,
It’s not bigger in comparison to the first fixpoint. The second fixpoint contains an a Small tuple which doesn’t
The first fixpoint is the one that appears that is why it’s not less than the first fixpoint. It is therefore not less than the first. The the order is the order in which
We apply the rules to determine which fixpoints are computed, and the result is disappointing.
We hope that users are able to comprehend their questions without having to think about what the
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What causes the issue is the usage of the word not. If we apply the first rule, certain inferences are
not allowed due to Tuples within Small. Parts that meet the other requirements of
The rule’s body can be used to add to Big. We take the parts of Small from
This set of candidates. Therefore, certain inferences can be made if Small has no value (as it is prior to the
Second rule applies) are prohibited second rule is applied. Small is a tuple (generated through the
Second rule preceding the second rule before the). This is the issue in the event that it is not used to add Tuples to an
Relationships can block the possibility of inference from other relationships. This situation cannot ever occur.
The addition of more tuples an existing relation will never impede an inference from other tuples
Negation and Range-Restriction
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If the rules allow be incorporated into the body, then the scope-restriction definition must be
It is extended to ensure that range-restricted programs are secure. If a relationship is found in
The body or rule that is preceded by not, we consider this negated event. Relationships that occur in
Body that is not negated are referred to as positive occurrences. A program is restricted in its range in the event that each
Variable in the rule’s head occurs in a positive to the body.
10.4 Modifying Complex Data Semantics
Data modeling, using an specific type of data model and as an exclusive method of storing data collection.
The design of the system is usually associated with Charles Bachman (1969) who presented the Data
The Structure Diagram to be one of the earliest, extensively used data models for designing databases in networks.
A variety of alternative data models were suggested shortly after The most famous of them is
The relational model was swiftly criticised for being flat which means that every information can be
The representation is an array of tables that have the atomic values of each cell. The definition of a well-formed relational
models demand that you have complex attributes types (hierarchic, multi-valued, composite, and models that are derived)
Be converted into atomic attributes and relations normalized. Inter-entity (inter-relation)
relations are difficult to visualize and the resultant set of relationships, making it difficult to manage the
Completeness and accuracy of the model is difficult. The relational model easily maps to the
physical properties that are characteristic of digital storage mediums and , as such, is an excellent tool for designing an
The model based on relationships between entities had two major goals one was to visualise
inter-entity relationships , and secondly to break down relationships between entities and secondly to separate the DB designing process in two distinct phases:
1. Record, within the ER model, is the entity as well as inter-entity relations are required “by the
enterprise”, i.e. owned by the user or owner of the application or system. This is the phase that must be completed by the owner/user of the information system or application
The model and the resulting model must and the resulting model should DBMS tool that will be used
Realizing the DB.
2. Convert the ER model into the model of data supported by the DBMS to be used
This design is two-phase and supports the independence of data, i.e. it is possible to DB structure modifications
at the physical level , without having to change the user or enterprise perspective of DB the content.
The term “specialization” is a limitation on the scope of a procedure that is, a process p1 can be described as an example of a
Specialization of a procedure the process p0 when every instance of p1 is an example of the p0 however, it is not always.
and vice and vice versa. The definition is reformulated to include the frame of reference. There are two
instances to be considered:
1. The two processes can be described in the same reference frame. In this case , extensions are used.
All of the of the processes of the processes are described in the same terms and are able to be directly compared. So p1 is
A specialization of p0 only if and only it is the case that an extension to p1 can be described in the frame
Of reference refers to a portion of extension of p0 that is similarly described.
2. The process is described in different frame of reference however there is an underlying “common”
Frame of Reference (which is a refined version of both). In this particular case the frame of reference is p1. It’s a particularization
of p0 only of p1 if the refinement is a particularization to the refinement of p0 in the conditions of
common frame of common frame of. Therefore, this second instance reduces to the one one by way of
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One possible method of expressing this concept of specialization is to think in the context of the concept of a set
of transformations to any specific process representationthat can be applied to a particular process
description, generate the description of a specificization of the procedure. The two-part definition of
Specialization suggests that two types of transformations will be required:
A specialization transformation is an action that, when applied to a procedure that is described with
A given representation, and a reference frame is able to produce a new description of the process under
the frame of reference that correspond to an additional version of the original
process. The specialization of transformations can alter the duration of a process , while keeping the
frames of reference.
A refining transformation is an operation that alters the frame of reference of an
process, while also preserving its extension and generating a description of the same procedure
in an entirely different framework of reference.
For every type of transformation , there’s an inverse type of transformation that is an generalizing transformation
is based on a description of the process that is based on a process description to generate a generalization the original procedure and thus is the
the reverse of a specializing transformation. In the same way, an abstracting transform is the reverse of a specializing transformation.
of refining the process of refining, resulting in a fresh description of the refining process within the framework
of reference in that frame, which is of reference for which the original frame is.
In the event that refining or abstracting transformations do not limit the duration of an operation, it follows
From what we define as process specificization as a specialization transformation that is composed of
refining or abstracting transformations in any order results in an additional specialization. Similar to
statement can be used to describe general transformations.
The set of refinement/abstracting or refining transformations is considered to be complete if, for any procedure p it is was described Colombia Database
within a frame of reference the description of the procedure in the other reference frame could
It is possible to obtain the set p a finite number transformations taken out of the collection.
A set of specialization transformations can be considered locally complete when they are in every frame
and any other process described in that reference frame or any particularization of the process that is described
Within that framework of reference can be achieved by applying to p a finite amount of transformations
taken from drawn from. This is the first portion of the definition of process specificization.
There is also a concept of completeness that is a reference to the second portion of this definition. A set
of specializing transformations as well as refining or abstracting transformations is believed to be global
full if for any particular process p, there is a particularization of p, for which the common frame of reference is used
existence can be obtained by applying p to the number of transformations taken out of the collection.
The proposition is that A should be an entire set of refining/abstracting transforms, and S be a local
A complete set of specialization transformations. Then A [[union]] is global.
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The proof: Take a look at the process p0 as well as the specialization p1 which is the common frame of reference is used.
exists. Since A is complete one could apply a finite number of transformations from A to
It is then possible to refine it within its common reference frame. With local completeness, one can create its refinement.
Apply specialized transformations to create the refined version of p1 (since the latter is an specificization of
the refinement of p0 using the assumption). In the end, through the totality of A, one is able to change the
The refinement of p1 in the p1.
We suggest that one effective method of expressing this concept of specialization is by referring to it in the context of the concept of a set
of transformations to any specific of process representations can be applied to a specific process
description, create an explanation of a particularization of the process, and then produce a description of a specialization of that. The two-part definition of
Specialization suggests that two types of transformations are required:
A specialization transformation is an action that, when applied to a procedure described in
A given representation, and a particular frame of reference result in a brand new process description in
the frame of reference that correspond to the specialization of the original
process. The specialization of transformations can alter the duration of a procedure while maintaining the
frames of reference.
A refining transformation is a process that alters the frame of reference of an
process, while also preserving its potential for extension, and generating an explanation of the exact process
with an entirely different framework of reference.
For every kind of transformation, there is an inverse type that is related to it: generalizing transformation
uses a description of a process to create a generalization of the original procedure and, as such, is the
the reverse of a specializing transformation. Similar to an abstracting transformation, it is the reverse of a specializing transformation.
of refining of refining, resulting in a fresh description of the refining process within the context of a frame
of reference, for that frame, which is an improvement.
In the event that refining or abstracting transformations allow for the extension of the process, it is logical to follow
From the definition we have of process specificization as a specialization transformation that is composed of
refining/abstracting transforms within any sequence results in the possibility of a specificization. The similar
statement is applicable to generalizing transformations.
A set of refining or abstracting transformations is considered complete if there is a the process defined
within the frame of reference the description of that process in another frame is possible.
It is possible to obtain the set p a finite number transformations that are drawn out of the collection.
A set of transformations that specialize is considered to be locally complete in the reference frame
and any other process described in that reference frame and any other specificization of the process that is described
Within that framework of reference can be obtained by applying the p-value a finite number of transformations
that are drawn from drawn from. This is the first portion of the definition of process specialization.
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There is also a concept of completeness that is a reference to the second portion in the definition. A set
of specialized in transformations and refining/abstracting them is said to be global
Complete if, for any process p, there is a particularization of p, for which is a common frame of reference is used
existence can be determined through applying to p an infinite number of transformations by the sets.
Propogation: Let A comprise an entire set of refining/abstracting transforms, and S be a local
Complete set of specialized transformations. Then A [[union]] is global.
A proof: Think of an p0 process and the specialization p1 which is the common frame of reference is used.
exists. Because A is complete, it is possible to apply a finite number of transformations from A to
It is then possible to refine it within it’s common framework of reference. Through local completeness, one could create its refinement.
Utilize specializing transformations for the refinement of p1 (since the latter is an particularization of
the improvement of p0 through the assumption). In the end, through the totality of A, one is able to change the
The refinement of p1 in refinement of p1 into.
Generalization buy Colombia email database.
The generalization hierarchy can be described as an organized collection of entities that have the same characteristics. It
is a highly effective and widely utilized method of identifying common traits among entities
while keeping their distinctions. The relationship that exists between an individual and one or more
refined versions. The thing being refined is known as the supertype, and each refined version is
named the subtype.
Generalization hierarchies are recommended when (1) the majority in a group appear
similar type, (2) attributes are the same for different entities the same type, or (3) models are constantly updated.
evolving. Generalization hierarchies enhance the reliability of the model by permitting changes to
Only be distributed to individuals who are relevant to the change and make the model simpler by cutting down on the
Number of entities within the model.
Making the Generalization Hierarchy
To create the generalization structure, every of the common traits are assigned the Supertype.
The supertype also has an attribute known as an discriminator, and whose values define the
categories of subtypes. Unique attributes to a category belong to the relevant
subtype. Each subtype is also inherited from the primary key from the supertype. Subtypes with only one key.
The primary key must be removed. Subtypes can be linked to supertypes by a one-to-one
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Different types of Hierarchies
A generalization hierarchy could be disjoint or overlapping. In an overlapped hierarchy,
An entity instance may be an element of many subtypes. For instance, it could represent students at the university
You have found the top-level entity PERSON with three subtypes: FACULTY STAFF,
and as a STUDENT. It is possible for a person to belong to multiple subtypes or for example, a employee
member who is an active student.
The fundamental rule of hierarchy of generalization is that every instances of the entity supertype should
are present in the subtype in at minimum. similarly instances of the subtype should be as a supertype.
Subtypes may be element of only one Generalization Hierarchy. In other words, a type cannot be associated with another subtype.
to multiple supertypes. Generalization hierarchies can be nested through having
The subtype of one hierarchy could may be the supertype of another.
Subtypes can include the primary entity of an existing relationship, but not the child. If it was allowed it would be the
Subtypes could have two primary keys.
The goal for data modeling is create an appropriate data structure for the database to be as perfect as possible.
it is possible to do so with a relevant world, typically associated with an organization as possible with a relevant world, often associated with
information is needed.
In general, there is a connection between a data model and a component of the existing
The world is not perfect, but it’s quite possible to imagine that a model of data may have a connection with an imagined world.
Datalog: Program for datalogging is either input or output relations.
Modeling of data: Data modelling employing a particular type of data model, and as an exclusive activity in
information system design.
Specialization: an extension restriction of a procedure.
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Safe Datalog Programs
Take a look at the following program:
Complex Parts(Part) Complex Parts (Part) Assembly(Part and Subparts, Quality) Qty > 2.
As per this rule, a “complex part” is defined as a part with greater than 2 copies
Any one subpart. For each component that is mentioned in the Assembly relation, we are able to quickly determine if it’s an assembly or a
complicated element. Contrary to this, think about these programs:
Price Parts(Part,Price) Parts of Price: Assembly(Part Subpart Qty) Qty > 2.
This variant aims to link an amount to each element. But Price is the variable
It is not included within the text of the rule. This implies that an infinity of tuples have to be
that is included in every model in this software! To understand this imagine that we replace part with Part by
the trike that is constant, SubPart by wheel, and Qty divided by the constant trike, SubPart by wheel, and Qty by. This is a variation of the rule that includes
the only remaining variable is price:
Price Parts(trike,Price) Price Parts (trike, Price) Assembly(trike wheel, trike) 3 > 2.
Then, any assignment of a constant Price will result in the possibility of including a tuple in the output relationship.
Price Parts. In this case, for example substituting Price with 100 will give us the price tuple Parts(trike,100). If you are able to calculate the
The simplest model for the program isn’t infinite, not even for one of its input relationships, we can say that the
This program is dangerous.
Database systems block unsafe programs , requiring each variable that is in the head of an
rules must be included within the rule must also be reflected in the. These programs are believed to be restricted in their range, and each
Range-restricted Datalog program is the finite minimum model if the input relations are
The two rules could be viewed as an attempt to separate the parts (those which are included in the
First column on the Assembly table) split into two classes, the Big and Small classes. The first rule categorizes the term “big” to
It is the group of components that have at minimum three copies of the subpart and are not considered to be small.
parts. The second rule is to define Small as the group of parts that aren’t classified as large parts.
When we consider the case of Assembly depicted in Figure 10.3 Trike is the sole component.
that makes at three copies of a subpart. If the tuple Be in Big or Small? If we
Apply the first rule, and the second rule to this tuple, it is Big. In order to follow the first rule, we
Take a look at the tuples when you are in Assembly Choose those that have Qty > 2. (which is not a problem). ) Remove those
that are present in the current version of Small (both Small and Big were initially empty) and then add the
Tuples left to the Big. Thus the application of the first rule will add to Big. Moving forward
In the same way, we can also see that, if we apply the second rule used before the first is included in Small
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The program is divided into two fixpoints, neither one of which is less than the other, as is shown in
Figure 10.4. The first fixpoint contains the Big tuple which is not present at the fixpoint that is second Therefore,
It’s not bigger that the fixpoint. The second fixpoint contains an a Small tuple which is not
The first fixpoint is the one that appears that is why it’s not bigger than the first fixpoint. Therefore, it is not smaller than the first. The the order is the order in which
We apply the rules to determine the fixpoint that is computed and this is disappointing.
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We would like users to comprehend their questions without having to think about what they are asking.
The cause of the issue is in the use of the word not. If we apply the first rule, there are some inferences that are
not allowed due to Tuples Small. Parts that meet the other requirements of
The rule’s body could be added to Big. We take the parts of Small from
This set of possibilities. This means that some inferences are possible Small does not exist (as it is before
Second rule applies) are denied second rule is applied. Small is a tuple (generated through the
Second rule prior to the second rule (before the). This is the problem when it comes to using to add the tuples is an
The relation may not allow the possibility of inference from other relations. This situation will never occur;
The addition of tuples to an existing relation will never hinder an inference from other tuples.