History & Evolution of DBMS
Table of Contents
Introduction
Storing and managing database is one of the
most primary aspects to maintain data in an organized fashion. Previously
during absence of Information Technology, people used to maintain data manually
like organizing data with cards, notes etc. This was feasible maintaining
smaller amount of data like indexing a small set of books or records. But when
data grows bigger, using manual process becomes a well-nigh affair to manage.
Rather it turns into a daunting task to keep track of the same. Another
disadvantage with manual data set is longevity. Data’s that are maintained
manually are prone to damage and further decay. With this growing problem in
maintaining records and owing to the inception of Information Technology which
brought revolutionary changes in managing large set of data in electronic
format, things not only became easier to handle but also enables a process to
store data for a longer time (even life-time, if kept in a proper fashion).
Moreover indexing becomes easier and one can access data from any part of the
world provided they have access to the database. The coming of RDBMS
(Relational Database Management System) has defined a new level where storage
of data is formulated in a way not only to retain it for a longer time but also
in a systematic fashion. As year’s progresses and so does the advancement of
technological changes for further betterment, the database system has gained
newer heights where one can maintain gargantuan set of data without any hassle.
The aim of the paper is thus to provide a
clear rationale behind the objective of database management system and its
critical importance for maintenance of records.
What is Database Technology?
Database is any
kind of storage of Data, be they in raw format, be they in machine code, in
fields, in records, in files in tables or in any other fashion. It is
conglomeration of data that has some kind separation defined as distinction
marks.
Data are
combined to impart some meaning into it through categorization and arrangement.
A data arrangement is a database. At this stage the basic agenda is to store.
Data base
management is a little different. It is an arrangement where the motive is
different. When Data is arranged with an eye to ease of retrieval, we talk
about Data base. The objective is just the opposite. One stores nicely to
retrieve them easily at any later point in time.
Data base management has actually gone
through a great evolution within a very short period. From the late 70’s the
database technology has evolved from the software and from the hardware or
platform perspective as well.
From the early
days of big main frame machines data were arranged in flat sequential files to
be followed by a DBMS we call hierarchical database. This arrangement mimics
the old management style and organization structure. Data cells or elements are
arranged from top-down structure where any particular cell does have one parent
and one or many children. So to navigate to any cell, the user has to navigate
along with the genesis or parental logic of the data(Booch, et al., 2007) .
The next step
was a Network structured database, where any cell can be a child of more than
one parents and do have many children. The leaf most cells or units of data can
have reverse connection to the head level data cell. The programmer can
navigate to one cell through multiple data paths. It became faster, and to
navigate very large databases (VLDB} this arrangement offers the fastest
retrieval. However to insert, or delete one cell becomes a nightmare demanding
many lines of code. Real life, real-time heavy transactions like banking
operations where huge number of updates, inserts and deletes are undertaken
this kind of arrangement is a killer in time. The load of so many users hitting
the same database becomes prohibitive.
To solve this
predicament, data base technology took the help of discrete mathematics or more
pointedly the set theory and set algebra. Data cells are now arranged in simple
heaps with their indices arranged in a virtual two dimensional “tables” in rows
and columns. The entire set algebra operations are used to seek any cell (as a
positional co-ordinate of <row id, column id>). It is remarkably simple
and elegant. All kinds of operation in searching of the data cells, from
various tables and stores can be collected through a language called Structured
Query Language (SQL).
Data are
arranged in separate table following a mathematical structure called
normalization where a row or a record would be formed with different cells or
fields. These fields would be solely and exclusively depended on one field
called the key field. There should not be any inter-dependency of the non-key
fields among themselves. Such a key-led (called principal key) row structure of
repeating records form one table. Many such tables are drawn and they are
designed to have relations that are the principal key of one table can be a
member of another table. This is known as foreign key. That is very akin to a
structure where the second table would be like a child table of the former.
Through this system any table can be made to relate to any table on the fly
where in physical reality the data records are actually stored in a heap
structure. Any record or for that matter any cell is known by the principal key
of the record. There is a special name for this kind of structure; it is called
a duplet with <key, cell name>. This arrangement however is the data of
the data or the meta-data that means it is the structure that is imposed on the
data value. A data value only has its significance with respect to the
meta-data. A meta-data therefore describes the data in the physical value. Very
detailed logic has gone into developing the language of navigation.
Relational
database management system had the longest surviving life in the evolution of
Data base technology. Even today what are used in every industry are different
versions of RDBMS(Date, 2003) .
But industry
needs have gone much beyond this. The exigency of handling VLDBs, of extremely
fast retrieval, of handling many applications that are apparently independent,
and that of connecting data tables on-the-fly, has led the designers to go
beyond. In came the Object Oriented data base management system. This
arrangement is the result of the arrangement of the system that is found in
nature. Everything in the natural world are objects. Each object has some
characteristics and has some methods or protocols that it exposes to others to
collaborate, there are methods for the friends, for the children, for the
parents. But they have some internal features which the object might want to
protect only to be exposed for other members within the object or for others.
Obviously objects have internal objects too. Or objects can pile up to become
parts of a super object. Objects can inherit other objects and can extend
friendship with others. Objects can have multiple inheritances. Objects have
many methods encapsulated and a method might mean something to one set of
friends but would mean different to the other set that means the same method
would work differently with different objects outside. This algebra did
revolutionize Database technology by which the nature can be replicated and
mimicked within a virtual storage of information. This had triggered the
revolution of application processing.
Object oriented
Data base management system (OODBMS) is very effective in understanding and
mimicking complex data structures in applications. But the old problematic of
handling VLDB sustained and even aggravated. A compromise was struck and Object
Relational Database Management System (ORDBMS) was applied. Today all the
vendors of DBMS use this structure (Booch, et al., 2007) .
While this was
revolution was going on, there came up another exigency- that of analysis. A
data item has many ways of viewing and the same data element has qualifiers,
for example Sales amount in dollars and sales volume in quantity can be qualified
by the shop, by the product, by the region, by the company etc., the list goes
on and on. A retail outlet sells many products from one to many of their
outlets and they are further classified in terms of unit-of-measure, packets,
they go in combination with other products. These sales are undertaken by sales
people, by the regions, by the country, by many other qualifiers. This sale
figure can be rolled up a hierarchy or drilled down the hierarchy. The sale
figure is termed as a “fact” and the qualifiers are termed as “dimension”. A
fact is only meaningful with respect to a dimension. This style has ushered the
new revolution of Analytics.
Every business
is ultimately measured through some metric. By a metric an analyst can compare
the performances in a relative scale. Businesses communicate with each other
and within their organization through metrics and the customers also understand
businesses through metrics. Metrics are to be figured out through Data
Analysis. Analysis of facts with respect to various dimensions, the aggregate
values of facts calculated through complex calculations help generate the analysis;
these are then depicted in charts and graphs. Decision makers can understand
and can publish them for others through these representations. People can
extrapolate data and figure out the trend and the position where the results
would be if there are some variations in the output- this is called What-if analysis.
Business now has gone numerically oriented.
The analytics
revolution has brought us into advanced mathematical levels where people are
interested in statistical treatment and they would like to know how far they
can trust on a set of data in corroborating some hypothesis, some assumptions
and some target.
Universally the
social requirement of business is to know the functional representation of
facts with respect to the most significant dimensions. This is a famous
problematic of today’s business world.
In order to do that many techniques have developed. The entire knowledge pool
of mathematics has come to help data analysis. Today we are coming out of the
regime of fixed assumptions. Neural networking techniques with genetic
algorithm have allowed us to form the right equation and the correct result
through advanced simulation. Statistical and Stochastic databases and
genetically programmed data structures are now giving us solutions that could
not have been done manually.
Conclusion
Database technologies
have gone beyond their initial agenda or storing and retrieving. We now have
come up with solutions, solutions from data that are unstructured. Previously
we formed the data structure that is the metadata and tried to fit in the dat.
Now we mine the relations of the data from datagrams without any prior
structure. We now evaluate a data pool, figure out the inter-relations, figure
out their innate functional representation and can extrapolate the future
probable value with respect to some level of confidence. Data base technology
now has opened up a new world of scientific investigation with the business
generated data. The journey has not stopped and newer technology is coming out
solving problems that human civilization could not surmise. The discovery of
Chaos theory is changing the knowledge level to much a higher extent.
Data within organization
An organization
is characterised by the regular transactions that is generated every day
through their routine operations. These operative data are collected and are
processed locally and the final aggregated results flow up the organizational
hierarchy. This is the approach of the age old Management information system.
The entire organization cannot access the atomic data elements in any
application and cannot re-use them even when it is extremely needed. The same
data or the primary transaction had to be entered in various applications. The
duplication is a big loss of effort, money and accuracy because one little
human error expands to unimaginable proportions through the system.
The solution had
to be evolved over the years. Data from different applications were pooled in
one data store called Operational data store where rationalization over all
applications data was undertaken and stored in a universal format. This data
was then fed to the various applications and shared through other applications
on a need to know basis and on strict user access privilege.
This data store then collates all the data
into a different type of database where the aggregated data are stored
classified by the dimensions. Data now resides in terms of facts and dimensions
culled in from various applications. Any individual application can thus
download the necessary data for their use and pool back the data after some
possible modification.
Enterprise level
warehouse of data is the newest and trusted structure in the business world.
From this huge Enterprise level Data base comes out Data marts which are one
fact and many dimension tables where each numeric fact of metric is stored with
respect of all possible dimensions. From these data marts we form data cubes –
a smaller mart with two or a few dimensions. This representation is then
pictorially depicted in terms of three dimension cubes. Mathematics of
transformation are used to see what happens if a fact turns into a dimension
and another dimension ( transformed into cardinal or ordinal number systems)
becomes a fact. All possible angles of view give analysts a holistic view of
the data. The analysis is made simpler(Laberge, 2011) .
In the present
stage of development these EDW (Enterprise Data Warehouses) Data marts and data
cubes are then fed into analytics software to evaluate the health of operations
quantitatively and parametrically.
The spectrum is now complete – from atomic
level data from operations are collected, processed all the way to appropriate
pictorial description and analysed with appropriate weights. This gives a trend
analysis and a future extrapolated value. The actual value when arrives is
tallied with this and the gap found and analysed till the optimum result
arrives. The final concept of optimization is the final stage and we want to
know what the solution space isand where we can operate our business and not
compromise in output, quality and profit.
New Technology aiding Regional Health Authority
Introduction
Regional health Authority is an
organization that has many leaf level health outlets reporting to it. The total
number of people that are served by these outlets is 1 million. They include
primary care trusts and General Practitioners too. Regional health authority does
collect data of the patients, their history and treatment meted out to them.
However there are discrepancies. The General Practitioners are individual level
agents who treat people but in most cases do not collect the data and these
data are in such free format, that they are not recorded in RHA database, but
some data do come in. Even those data do not get recorded in organized
fashioned. The retrieval of those data has not been therefore possible and
these data are not of any serious us in following up. The local trusts and the
agents do their work on familial basis only, simply keeping everything in their
head. The data has no value in researching ailment pattern and treatment
pattern.
Technology is available to serve this kind
of problem. Technology would combine hardware and software applications. I
would discuss a solution as a suggestion.
Data Capture
We now have hand held devices that work
with mobile phones. Every GP independently working or working with the local
trusts should have one mobile device (most likely working with Android
operating system) where a templatized application is loaded. Now, there are two
possibilities, one is an application that is installed in their device- but
this has its perils that are known. The latest technology is accessing the
“cloud” – which means the person should click an URL that would take them to an
application downloaded in their local machine, they would key in their user-id
and password, to get an authorized access. A form will appear where the data
need to be keyed in. Typical data format or template would require the GP to
fill in patients’ name. As soon as the name is keyed in the id of the patient
with the latest information collected from the patient in the previous visits,
would appear. If the patient is new then the GP has to key in the necessary
details like the name, NHA number, address, age, sex, ailment reported, ailment
diagnosed, medicines suggested, and general note of the GP. The id and the date
are automatically generated. Once such a screen appears with the necessary
details the GP has to key in the new comments, and save the format. The
application will record all these comments serialized in a history file with
respect to dates.
The GP’s name would be automatically saved
and if a new GP sees the patient, she will find out who saw the patient and
what all the comments were.
Next step
This is the point of occurrence data that
is captured. The application is installed in the cloud. The specific cloud
application will store the data of the individual patients. To have these data
usable, they need to store with some dimensions. In this particular case there
is no numerical fact. But the comments are the basic facts that are qualified
with respect to the ailments, day, principal diagnosis and the doctors. These
four appear as dimensions to the fact –that is comment. This kind of
arrangement puts the records in a certain fashion.
Every data that comes from different GPs
are categorized automatically and stored in a grand table with the necessary dimensions
( in this application the fields that are captured are same) and the dimensions
are also the same, which means that the meta-data capturing data from all the
GPs are same. The grand table is a data mart which has the comment and various
dimensions. This data-mart can be accessed by GPs in any retrieval. Data are
categorized. An analyst at the RHA or any one with the right privilege can see
the aggregations. Say for example what were the diagnoses for a particular
ailment type? How many visits does a particular GP has charged per patient on
an average? What are the number of days that one patient is detained on an
average for a particular type of ailment? What medicines are generally
administered on a patient? An analyst
can make a general mapping of how a particular local trust or GP is faring? Is
there any standard treatment style that is emerging? And which doctor and why
would she deviate from the standard treatment that is emerging? A pattern of
ailments with respect to age is also evident from these treatments.
These information’s about ailment-age
mapping, ailment-neighbourhood mapping, ailment-treatment mapping and
ailment-medicine mapping are ferreted out from the transaction data. This is
what is known as data mining. Data mining takes the data and finds out
relations that are not possible to know before-hand.
How to get these?
The personal atomic level data can then be
collated with the existing data that the trust collected. Those applications
may be different. RHAs have inventory of medicines and consumables. There would
be another application where records would be arranged for collecting the usage
of consumables with respect to the doctor using that. Doctors’ performances
with respect to using consumables, medicines need to be measured. This is more
important for surgeons and for the emergency cases. How does one doctor use the
medicines as compared to another doctor? This will give the result that some
doctors use outlying amount of consumables, then there should be some standard
features that can be discussed and if necessary imposed.
Another application would be a simple
inventory management system for medicines and consumables. Some local trusts
would have a perennial shortage of consumables depending on what they need and
what they get. RHA had started a standardised quota system for the local
trusts. But there would be some local trusts, who would need more than the
average depending on the season and terrain and some special local conditions.
Flood affected or disaster prone areas would need more of the consumables and
some particular type of medicines for their use. One might find that there are
a few trusts where things are in excess and are rotting. From the inventory
application there can be an ABC analysis ( analysis of fast moving materials
and standard moving and low moving materials). This analysis would reveal the
economic ordered quantity to stock and their usage required.
Integration
These applications have different data. The
inventory data must be integrally connected with the sales and distribution
data, so that the local trust and the RHA can know unit does more sales or
distribution and what is their demand. Thus internal transportation can
rationalize the demand provisions among the different units.
The Challenge is to integrate these data
with the data of individual patients’ usage. An Enterprise level data warehouse
would have in one database all these information. The first step is to
standardize the data type and length of the data for same fields across applications.
This will help a single door place of insert and update, the same data will be
seen by every application connected to the Enterprise Data Warehouse.
Various applications pool in data,
standardize them in same format arrange them in proper tables and as the tables
would have connections and stored procedures therefore these data can then be
re-distributed to the applications for their use. The Data warehouse in turn
can churn out data for aggregation studies and special studies generating
reports with chosen formats. Analysts who are interested in the aggregation at
the corporate level can then use them as they want.
Rationalizing the data coming from
different type of databases can be handled through cross database utilities
that communicate to and fro the databases. An Enterprise Data warehouse would
be there in one single database technology. Here comes the techno-financial
decision of the cost of licensing and the support of the database vendor in
cases of disasters.
Writing stored procedures in the Enterprise
application takes care of the to and fro data movement between two applications
and databases. Generally the front-end application that connects different
applications are known as Enterprise Application Integration.
Enterprise data warehouse at the back end
and Enterprise Application integration at the front end can share data tables
with applications and users with the proper privileges. The user security logic
allows some users to see some specified data from the same table where others
might see other fields or the whole field set. User access logic is installed
right at the inception and that is part in the technology.
Reports are the face of the application
that shows data outside the application. Modern data ware houses do have nice
report writing and report generating sections that automatically show data in
pre-designed report format or in customised report format. The user can simply
drag and drop the fields she wants to see in a report space, declare the aggregation
arrangement and design the place holders to have a report generated.
Reports can be externalized for use in
other applications. A URL is generated that contains an XML file and that file
can be send to an external application with proper authentication. This
externalization is a novel way to integrate the data churned out of this
application into the programming use of an external application.
How the organizational hierarchy and the data view can be implemented?
Organization hierarchy needs and deals with
different data elements differently. HR and finance would be interested in the
salary details that they might not want to share with everyone. Sales and
Marketing would like to share prices of consumables and medicines that they
might not want to share with others. Some information may be superfluous even
for people at the highest levels. All these would depend on the user-access
level privileges. Privileges are not simply associated with form or screen
level rather now-a-days every data element or cell can be associated with
specific user privileges Applications are so sophisticated these days that user
access can be restricted on certain values of a field or data element, for
example only a few people would see the those salaries that are below a certain
value. This advanced filtering can have complex logic as per the set theory.
Combination of logic imposed on various fields can be combined in complex logic
and the required values are filtered. These logic are best installed at the
database level and not at the application level. These checks are generally
implemented with triggers which connect these logic and acts during the insert
or update of any record. Only allowed fields and values will be shown to
specified users. They show up in the forms of applications with those fields
disabled or blanked out.
Data warehouse and Data marts is now very
essential central core of any application. Data are first cleaned and
rationalized so that all the anomalies are removed. Anomalies can come up in
trying to push in data with undesired data values. Data cleaning is another
important application. Here the data formats are checked and the boundary
values are checked. Checks also take place with cross data field and cross
values, for example if the person is a female and of an age more than a value
then some medicines may not be allowed to be administered. If a person does
have some ailments then they might not be admitted in the hospitals, if a
patient is diagnosed with some critical ailments as diagnosed then they may be
mandatory referred to a more advanced hospital. If the number of seats falls
below the number required then any further admittance will have to be barred.
These are business logic that need to be placed either in the application level
or at the data base level.
Physical considerations
When a big number of users are using the
application then it is exigent that after entering every field the value is
authenticated by the database. This makes many database accesses and slows down
the response time. Response time from any application is the most crucial
consideration while designing a system. Programmers and designers try to reduce
the data base accesses to the minimum so that the access is once and the
response time is mitigated.
Generally forms in hand held devices
connect the cloud through web. There is a web-server that takes care of the
load balancing of the requests. In any kind of web application there are three
layers the web-server, the application server and the database server. Each such
server sends a request to the next level and waits for the response. The
summation of these responses comprise the response time. A web server balances
the load and attaches a handler (with a handling ID) and then collects all the
requests coming from the users. Each terminal is identified through this
handler id. A map is maintained with the web-server where the handler id is
mapped with the browser and the terminal (terminals are now identified through
their IP address). The application server collates these requests and groups
them on similar requests. These similar requests are bunched and sent to the
database server for fetching the values. Those responses are then served back
to the application server and the request-response is thus resolved at that
stage. The application server then redistributes the proper response and sends
it back to the web server. The web server sends it back to the user’s terminal.
The whole operation should take a fraction of a second. People do not want to
wait a while to get their responses. With faster response time the numbers of
transactions that can be served are more, and the efficacy of the system
depends on how many transactions are served per unit time. The whole algorithm
are now used by front end application languages like Java (with J2EE) framework
and with .NET technology of Microsoft. These languages connect to the database
mainly through ODBC and JDBC protocols. These protocols do impose a limitation
in the data transfer volume. This has influenced the designers to use native
database communication. Native database communications are specified protocols
that database vendors expose to the external applications for data transfer.
In lieu of Conclusion
After the EDW, there should be some sort of
analytic tools that analyse the data, in terms of aggregations and then show
the result pictorially. These are called Data Analytics tools. Almost every
database vendor has their proprietary analytic tools. The analytic tools then
feed these aggregation results either for exposure through reports and
externalization of dashboards or they can feed data into further applications
for very advanced statistical and genetic programming tools so that one can
find out the trends of data sets, and the innate rule in the data sets. This is
what is called data mining of the advanced level where advanced mathematical
formulae are used. Predictive analytics and Descriptive analytics are now being
used to fetch hidden relations from huge data gram, helping analysts figure out
where they are going. Neural network algorithms and genetic programming are now
extensively used to find the hidden functional representation(Miller, 2013) . Even in terms of
dash-boarding advanced techniques are used with Flash to draw out dynamic graph
and running graphs in 3 dimensional and rotating representations. The general
health of a health care system changes with every transaction and those changes
need to be continuously depicted on a running basis. The present technology
thus allows dealing with dynamical changes. Dynamical representation thus helps
analysts take instant decision very with agility and immediately at the point
of occurrence.
Analyse and Design a System
The purpose of the project is to develop a
travel portal that will fetch information from other hotel sites that are present
in Philippines. With the influx of traveller in the country, the rise of hotel
and tourism is rampant and visitors often looks for a specific website that
caters online booking facilities to most of the registered hotels.
Based upon the aforesaid situation, the
idea to build a portal allowing online booking and updated information has been
considered. Most of the registered hotels do have their website but at times
they lack online booking procedure. The proposed website will help user to find
list of available hotels and can search based upon the range of budget, date
and availability. Once user finds the detail they can opt for online booking
using integrated payment gateway and receive online confirmation via email.
Moreover the site will also enable user to cancel reservations in case of any
events or unforeseen circumstances that may arise to the traveller.
The website will be designed with a simple
look offering maximum functional modules to enable a user-friendly application.
The site will also be designed to make it compatible with mobile system. User
can review the site on any handheld device besides the web browsers.
Audience
a.
End-user / Customer
b.
Hotel owner
c.
Administrator
Functional Detail
End-User/Customer
Following are the functional detail that
one viewer finds when they enter the site.
|
Module Name
|
Details
|
|
Search
|
The search function will let user find the hotels
that one visitor is looking for. In order to perform the same, following are
the attributes that one has to enter as stated below:
·
Select location in Philippines
·
Select check-in date
·
Select check-out date
·
Select number of nights
·
Select number of nights
·
Select members type (Adult / Children)
Once user fills the information and clicks the
‘Search’, it will list all the hotels within the parameters as entered by a
visitor.
|
|
View hot deals
|
The home page will also list new deals from hotels
one user can avail. In order to do the same, the visitor simply has to enter
promotional code to avail an offer.
|
|
View hotels
|
This section in the page will list some of the
popular hotels. The rating is calculated based upon the feedback received
from visitors who availed the hotels during their stay in Philippines.
|
|
Contact Helpdesk
|
User can contact support section to connect with the
customer support team at any point of time.
|
Hotel Owner Module
This said module is mainly for the hotel
owner who would like to advertise and list their services with the proposed
website. Following are the functional modules that we propose for the said
user-type:
|
Functional
Module
|
Details
|
|
Contact for submission
|
A hotel owner in order to be a member of the site
has to first intimate the webmaster. In order to do the same, they have to
fill up the form with the following details as mentioned below:
·
City
·
Hotel Name
·
First Name / Last Name
·
Contact No
·
Email ID
·
Confirm Email ID
Once the aforesaid information is provided the
webmaster will receive notification after which the company will take
necessary actions to register their hotel with the site and enable listing.
|
Administrative Module
The admin module is mainly developed for
super-user where one can manage the site along with end-users / hotel owners
besides other functional areas. Following are the some of the basic
functionalities that we aim to propose in the section:
·
Login for admin
·
Update account (incl. password update)
·
Assign sub-admin with specific task using Task
Manager
·
View visitors list
·
View list of new user that made online booking
·
View number of online booking (Report format: Daily
/ Weekly / Monthly)
·
View transaction details
·
Refund management
·
View hotel owner’s information
·
View number of booking for a hotel
·
Payment gateway management
·
Manage payment transaction with hotel owner
·
Manage API
Use of API
API (Application Programming Interface) is
one of the most common and acceptable methods in order to fetch information
from other websites. Here in this case, once a hotel owner registers and gets
approved for listing, the hotel owner’s website database will be connected with
the API to fetch real-time data. Once the data is achieved and stored in the
main database, they are organized and presented to the user in a friendly
format.
Technologies
The proposed website will be developed with
the following technologies:
·
ASP.NET Framework will be used to develop the
web portal (It is envisioned to design a new system by revamping the existing
website)
·
MS-SQL Server will be used as database.
·
The system will use XML-Data Feed Method to
fetch data from other website. In short, an API will be developed to do the
same.
·
The website will be designed using HTML 5 to
develop a web and mobile friendly website.
·
SSL will be used to implement security in case
of online transactions.
·
The payment gateway to use would be online
merchant account for credit card transaction.
Reference
Booch, G., Maksimchuk, R., Engle,
M., Young, B., Conallen, J., & Houston, K. (2007). Object-Oriented
Analysis and Design with Applications. New York: Addison-Wesley
Professional.
Chamberlin, D. (1976). Relational
Data-Base Management Systems . Computing Surveys, 43-66.
Date, C. (2003). An Introduction to
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