Tugas Kelompok 5 XI MIPA-1

Kelompok 5
Nama
: Ai Lisnur
Dinasri Lestari
Muhamad Eka
Selly Oktaviani
Vini Mulyani Edi Putri
Kelas : XI MIPA-1
Tugas
: TIK (Teknologi Informasi dan Komunikasi)

Database

A database is an organized collection of data. It is the collection of
schemes, tables, queries, reports, views and other objects. The data is
typically organized to model aspects of reality in a way that supports
processes requiring information, such as modelling the availability of rooms
in hotels in a way that supports finding a hotel with vacancies.
A database management system (DBMS) is a computer software
application that interacts with the user, other applications, and the database
itself to capture and analyze data. A general-purpose DBMS is designed to
allow the definition, creation, querying, update, and administration of
databases. Well-known DBMSs include MySQL, PostgreSQL, Microsoft SQL
Server, Oracle, Sybase and IBM DB2. A database is not generally portable
across different DBMSs, but different DBMS can interoperate by using
standards such as SQL and ODBC or JDBC to allow a single application to
work with more than one DBMS. Database management systems are often
classified according to the database model that they support; the most

popular database systems since the 1980s have all supported the relational
model as represented by the SQL language. Sometimes a DBMS is loosely
referred to as a 'database'.
Terminology and overview
Formally, a "database" refers to a set of related data and the way it is
organized. Access to this data is usually provided by a "database
management system" (DBMS) consisting of an integrated set of computer
software that allows users to interact with one or more databases and
provides access to all of the data contained in the database (although
restrictions may exist that limit access to particular data). The DBMS
provides various functions that allow entry, storage and retrieval of large
quantities of information as well as provides ways to manage how that
information is organized.
Because of the close relationship between them, the term "database"
is often used casually to refer to both a database and the DBMS used to
manipulate it.
Outside the world of professional information technology, the term
database is often used to refer to any collection of related data (such as a
spreadsheet or a card index). This article is concerned only with databases
where the size and usage requirements necessitate use of a database
management system.
Existing DBMSs provide various functions that allow management of a
database and its data which can be classified into four main functional
groups:
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Data definition – Creation, modification and removal of definitions that
define the organization of the data.
Update – Insertion, modification, and deletion of the actual data.
Retrieval – Providing information in a form directly usable or for further
processing by other applications. The retrieved data may be made
available in a form basically the same as it is stored in the database or
in a new form obtained by altering or combining existing data from the
database.
Administration – Registering and monitoring users, enforcing data
security, monitoring performance, maintaining data integrity, dealing
with concurrency control, and recovering information that has been
corrupted by some event such as an unexpected system failure.

Both a database and its DBMS conform to the principles of a particular
database model. "Database system" refers collectively to the database
model, database management system, and database.
Physically, database servers are dedicated computers that hold the
actual databases and run only the DBMS and related software. Database
servers are usually multiprocessor computers, with generous memory and
RAID disk arrays used for stable storage. RAID is used for recovery of data if
any of the disks fail. Hardware database accelerators, connected to one or
more servers via a high-speed channel, are also used in large volume
transaction processing environments. DBMSs are found at the heart of most
database applications. DBMSs may be built around a custom multitasking
kernel with built-in networking support, but modern DBMSs typically rely on a
standard operating system to provide these functions. Since DBMSs comprise
a significant economical market, computer and storage vendors often take
into account DBMS requirements in their own development plans.
Databases and DBMSs can be categorized according to the database
model(s) that they support (such as relational or XML), the type(s) of
computer they run on (from a server cluster to a mobile phone), the query
language(s) used to access the database (such as SQL or XQuery), and their
internal engineering, which affects performance, scalability, resilience, and
security
Databases are used to support internal operations of organizations and
to underpin online interactions with customers and suppliers (see Enterprise
software).
Databases are used to hold administrative information and more
specialized data, such as engineering data or economic models. Examples of
database applications include computerized library systems, flight
reservation systems and computerized parts inventory systems.

Application areas of DBMS
1. Banking: For customer information, accounts, and loans, and banking
transactions.
2. Airlines: For reservations and schedule information. Airlines were
among the first to use databases in a geographically distributed
manner - terminals situated around the world accessed the central
database system through phone lines and other data networks.

3. Universities: For student information, course registrations, and grades.
4. Credit card transactions: For purchases on credit cards and generation
of monthly statements.
5. Telecommunication: For keeping records of calls made, generating
monthly bills, maintaining balances on prepaid calling cards, and
storing information about the communication networks.
6. Finance: For storing information about holdings, sales, and purchases
of financial instruments such as stocks and bonds.
7. Sales: For customer, product, and purchase information.
8. Manufacturing: For management of supply chain and for tracking
production of items in factories, inventories of items in
warehouses/stores, and orders for items.
9. Human resources: For information about employees, salaries, payroll
taxes and benefits, and for generation of paychecks.
General-purpose and special-purpose DBMSs
A DBMS has evolved into a complex software system and its
development typically requires thousands of person-years of development
effort. Some general-purpose DBMSs such as Adabas, Oracle and DB2 have
been undergoing upgrades since the 1970s. General-purpose DBMSs aim to
meet the needs of as many applications as possible, which adds to the
complexity. However, the fact that their development cost can be spread
over a large number of users means that they are often the most costeffective approach. However, a general-purpose DBMS is not always the
optimal solution: in some cases a general-purpose DBMS may introduce
unnecessary overhead. Therefore, there are many examples of systems that
use special-purpose databases. A common example is an email system that
performs many of the functions of a general-purpose DBMS such as the
insertion and deletion of messages composed of various items of data or
associating messages with a particular email address; but these functions
are limited to what is required to handle email and don't provide the user
with the all of the functionality that would be available using a generalpurpose DBMS.
Many other databases have application software that accesses the
database on behalf of end-users, without exposing the DBMS interface
directly. Application programmers may use a wire protocol directly, or more
likely through an application programming interface. Database designers and
database administrators interact with the DBMS through dedicated interfaces
to build and maintain the applications' databases, and thus need some more

knowledge and understanding about how DBMSs operate and the DBMSs'
external interfaces and tuning parameters.

History
Following the technology progress in the areas of processors, computer
memory, computer storage and computer networks, the sizes, capabilities,
and performance of databases and their respective DBMSs have grown in
orders of magnitude. The development of database technology can be
divided into three eras based on data model or structure: navigational,
SQL/relational, and post-relational.
The two main early navigational data models were the hierarchical
model, epitomized by IBM's IMS system, and the CODASYL model (network
model), implemented in a number of products such as IDMS.
The relational model, first proposed in 1970 by Edgar F. Codd, departed
from this tradition by insisting that applications should search for data by
content, rather than by following links. The relational model employs sets of
ledger-style tables, each used for a different type of entity. Only in the mid1980s did computing hardware become powerful enough to allow the wide
deployment of relational systems (DBMSs plus applications). By the early
1990s, however, relational systems dominated in all large-scale data
processing applications, and as of 2015 they remain dominant : IBM DB2,
Oracle, mySQL and SQL server are the top DBMS. The dominant database
language, standardised SQL for the relational model, has influenced
database languages for other data models.
Object databases were developed in the 1980s to overcome the
inconvenience of object-relational impedance mismatch, which led to the
coining of the term "post-relational" and also the development of hybrid
object-relational databases.
The next generation of post-relational databases in the late 2000s
became known as NoSQL databases, introducing fast key-value stores and
document-oriented databases. A competing "next generation" known as New
SQL databases attempted new implementations that retained the
relational/SQL model while aiming to match the high performance of NoSQL
compared to commercially available relational DBMSs.

1960s, navigational DBMS
The introduction of the term database coincided with the availability of
direct-access storage (disks and drums) from the mid-1960s onwards. The
term represented a contrast with the tape-based systems of the past,
allowing shared interactive use rather than daily batch processing. The
Oxford English dictionary cites a 1962 report by the System Development
Corporation of California as the first to use the term "data-base" in a specific
technical sense.
As computers grew in speed and capability, a number of generalpurpose database systems emerged; by the mid-1960s a number of such
systems had come into commercial use. Interest in a standard began to
grow, and Charles Bachman, author of one such product, the Integrated Data
Store (IDS), founded the "Database Task Group" within CODASYL, the group
responsible for the creation and standardization of COBOL. In 1971 the
Database Task Group delivered their standard, which generally became
known as the "CODASYL approach", and soon a number of commercial
products based on this approach entered the market.
The CODASYL approach relied on the "manual" navigation of a linked
data set which was formed into a large network. Applications could find
records by one of three methods:
1. Use of a primary key (known as a CALC key, typically implemented by
hashing)
2. Navigating relationships (called sets) from one record to another
3. Scanning all the records in a sequential order
Later systems added B-Trees to provide alternate access paths. Many
CODASYL databases also added a very straightforward query language.
However, in the final tally, CODASYL was very complex and required
significant training and effort to produce useful applications.
IBM also had their own DBMS in 1968, known as Information
Management System (IMS). IMS was a development of software written for
the Apollo program on the System/360. IMS was generally similar in concept
to CODASYL, but used a strict hierarchy for its model of data navigation
instead of CODASYL's network model. Both concepts later became known as
navigational databases due to the way data was accessed, and Bachman's
1973 Turing Award presentation was The Programmer as Navigator. IMS is
classified[by whom?] as a hierarchical database. IDMS and Cincom Systems'

TOTAL database are classified as network databases. IMS remains in use as
of 2014.

1970s, relational DBMS
Edgar Codd worked at IBM in San Jose, California, in one of their
offshoot offices that was primarily involved in the development of hard disk
systems. He was unhappy with the navigational model of the CODASYL
approach, notably the lack of a "search" facility. In 1970, he wrote a number
of papers that outlined a new approach to database construction that
eventually culminated in the groundbreaking A Relational Model of Data for
Large Shared Data Banks.
In this paper, he described a new system for storing and working with
large databases. Instead of records being stored in some sort of linked list of
free-form records as in CODASYL, Codd's idea was to use a "table" of fixedlength records, with each table used for a different type of entity. A linked-list
system would be very inefficient when storing "sparse" databases where
some of the data for any one record could be left empty. The relational model
solved this by splitting the data into a series of normalized tables (or
relations), with optional elements being moved out of the main table to
where they would take up room only if needed. Data may be freely inserted,
deleted and edited in these tables, with the DBMS doing whatever
maintenance needed to present a table view to the application/user.
In the relational model, records are "linked" using virtual keys not
stored in the database but defined as needed between the data contained in
the records.
The relational model also allowed the content of the database to
evolve without constant rewriting of links and pointers. The relational part
comes from entities referencing other entities in what is known as one-tomany relationship, like a traditional hierarchical model, and many-to-many
relationship, like a navigational (network) model. Thus, a relational model
can express both hierarchical and navigational models, as well as its native
tabular model, allowing for pure or combined modeling in terms of these
three models, as the application requires.
For instance, a common use of a database system is to track
information about users, their name, login information, various addresses
and phone numbers. In the navigational approach all of these data would be
placed in a single record, and unused items would simply not be placed in

the database. In the relational approach, the data would be normalized into a
user table, an address table and a phone number table (for instance).
Records would be created in these optional tables only if the address or
phone numbers were actually provided.
Linking the information back together is the key to this system. In the
relational model, some bit of information was used as a "key", uniquely
defining a particular record. When information was being collected about a
user, information stored in the optional tables would be found by searching
for this key. For instance, if the login name of a user is unique, addresses and
phone numbers for that user would be recorded with the login name as its
key. This simple "re-linking" of related data back into a single collection is
something that traditional computer languages are not designed for.
Just as the navigational approach would require programs to loop in
order to collect records, the relational approach would require loops to collect
information about any one record. Codd's solution to the necessary looping
was a set-oriented language, a suggestion that would later spawn the
ubiquitous SQL. Using a branch of mathematics known as tuple calculus, he
demonstrated that such a system could support all the operations of normal
databases (inserting, updating etc.) as well as providing a simple system for
finding and returning sets of data in a single operation.
Codd's paper was picked up by two people at Berkeley, Eugene Wong
and Michael Stonebraker. They started a project known as INGRES using
funding that had already been allocated for a geographical database project
and student programmers to produce code. Beginning in 1973, INGRES
delivered its first test products which were generally ready for widespread
use in 1979. INGRES was similar to System R in a number of ways, including
the use of a "language" for data access, known as QUEL. Over time, INGRES
moved to the emerging SQL standard.
IBM itself did one test implementation of the relational model, PRTV,
and a production one, Business System 12, both now discontinued.
Honeywell wrote MRDS for Multics, and now there are two new
implementations: Alphora Dataphor and Rel. Most other DBMS
implementations usually called relational are actually SQL DBMSs.
In 1970, the University of Michigan began development of the MICRO
Information Management System based on D.L. Childs' Set-Theoretic Data
model. Micro was used to manage very large data sets by the US
Department of Labor, the U.S. Environmental Protection Agency, and

researchers from the University of Alberta, the University of Michigan, and
Wayne State University. It ran on IBM mainframe computers using the
Michigan Terminal System. The system remained in production until 1998.

Integrated approach
In the 1970s and 1980s attempts were made to build database
systems with integrated hardware and software. The underlying philosophy
was that such integration would provide higher performance at lower cost.
Examples were IBM System/38, the early offering of Teradata, and the Britton
Lee, Inc. database machine.
Another approach to hardware support for database management was
ICL's CAFS accelerator, a hardware disk controller with programmable search
capabilities. In the long term, these efforts were generally unsuccessful
because specialized database machines could not keep pace with the rapid
development and progress of general-purpose computers. Thus most
database systems nowadays are software systems running on generalpurpose hardware, using general-purpose computer data storage. However
this idea is still pursued for certain applications by some companies like
Netezza and Oracle (Exadata).

Late 1970s, SQL DBMS
IBM started working on a prototype system loosely based on Codd's
concepts as System R in the early 1970s. The first version was ready in
1974/5, and work then started on multi-table systems in which the data
could be split so that all of the data for a record (some of which is optional)
did not have to be stored in a single large "chunk". Subsequent multi-user
versions were tested by customers in 1978 and 1979, by which time a
standardized query language – SQL – had been added. Codd's ideas were
establishing themselves as both workable and superior to CODASYL, pushing
IBM to develop a true production version of System R, known as SQL/DS,
and, later, Database 2 (DB2).
Larry Ellison's Oracle started from a different chain, based on IBM's
papers on System R, and beat IBM to market when the first version was
released in 1978.
Stonebraker went on to apply the lessons from INGRES to develop a
new database, Postgres, which is now known as PostgreSQL. PostgreSQL is
often used for global mission critical applications (the .org and .info domain

name registries use it as their primary data store, as do many large
companies and financial institutions).
In Sweden, Codd's paper was also read and Mimer SQL was developed
from the mid-1970s at Uppsala University. In 1984, this project was
consolidated into an independent enterprise. In the early 1980s, Mimer
introduced transaction handling for high robustness in applications, an idea
that was subsequently implemented on most other DBMSs.
Another data model, the entity–relationship model, emerged in 1976
and gained popularity for database design as it emphasized a more familiar
description than the earlier relational model. Later on, entity–relationship
constructs were retrofitted as a data modeling construct for the relational
model, and the difference between the two have become irrelevant.

1980s, on the desktop
The 1980s ushered in the age of desktop computing. The new
computers empowered their users with spreadsheets like Lotus 1-2-3 and
database software like dBASE. The dBASE product was lightweight and easy
for any computer user to understand out of the box. C. Wayne Ratliff the
creator of dBASE stated: "dBASE was different from programs like BASIC, C,
FORTRAN, and COBOL in that a lot of the dirty work had already been done.
The data manipulation is done by dBASE instead of by the user, so the user
can concentrate on what he is doing, rather than having to mess with the
dirty details of opening, reading, and closing files, and managing space
allocation." dBASE was one of the top selling software titles in the 1980s and
early 1990s.
1980s, object-oriented
The 1980s, along with a rise in object-oriented programming, saw a
growth in how data in various databases were handled. Programmers and
designers began to treat the data in their databases as objects. That is to say
that if a person's data were in a database, that person's attributes, such as
their address, phone number, and age, were now considered to belong to
that person instead of being extraneous data. This allows for relations
between data to be relations to objects and their attributes and not to
individual fields.[21] The term "object-relational impedance mismatch"
described the inconvenience of translating between programmed objects and
database tables. Object databases and object-relational databases attempt

to solve this problem by providing an object-oriented language (sometimes
as extensions to SQL) that programmers can use as alternative to purely
relational SQL. On the programming side, libraries known as object-relational
mappings (ORMs) attempt to solve the same problem.

2000s, NoSQL and NewSQL
The next generation of post-relational databases in the 2000s became
known as NoSQL databases, including fast key-value stores and documentoriented databases.
XML databases are a type of structured document-oriented database
that allows querying based on XML document attributes. XML databases are
mostly used in enterprise database management, where XML is being used
as the machine-to-machine data interoperability standard. XML database
management systems include commercial software MarkLogic and Oracle
Berkeley DB XML, and a free use software Clusterpoint Distributed XML/JSON
Database. All are enterprise software database platforms and support
industry standard ACID-compliant transaction processing with strong
database consistency characteristics and high level of database security.
NoSQL databases are often very fast, do not require fixed table
schemas, avoid join operations by storing denormalized data, and are
designed to scale horizontally. The most popular NoSQL systems include
MongoDB, Couchbase, Riak, Memcached, Redis, CouchDB, Hazelcast, Apache
Cassandra and HBase, which are all open-source software products.
In recent years there was a high demand for massively distributed
databases with high partition tolerance but according to the CAP theorem it
is impossible for a distributed system to simultaneously provide consistency,
availability and partition tolerance guarantees. A distributed system can
satisfy any two of these guarantees at the same time, but not all three. For
that reason many NoSQL databases are using what is called eventual
consistency to provide both availability and partition tolerance guarantees
with a reduced level of data consistency.
NewSQL is a class of modern relational databases that aims to provide
the same scalable performance of NoSQL systems for online transaction
processing (read-write) workloads while still using SQL and maintaining the

ACID guarantees of a traditional database system. Such databases include
ScaleBase, Clustrix, EnterpriseDB, MemSQL, NuoDB and VoltDB.

Research
Database technology has been an active research topic since the
1960s, both in academia and in the research and development groups of
companies (for example IBM Research). Research activity includes theory
and development of prototypes. Notable research topics have included
models, the atomic transaction concept and related concurrency control
techniques, query languages and query optimization methods, RAID, and
more.
The database research area has several dedicated academic journals
(for example, ACM Transactions on Database Systems-TODS, Data and
Knowledge Engineering-DKE) and annual conferences (e.g., ACM SIGMOD,
ACM PODS, VLDB, IEEE ICDE).
Examples
One way to classify databases involves the type of their contents, for
example: bibliographic, document-text, statistical, or multimedia objects.
Another way is by their application area, for example: accounting, music
compositions, movies, banking, manufacturing, or insurance. A third way is
by some technical aspect, such as the database structure or interface type.
This section lists a few of the adjectives used to characterize different kinds
of databases.
 An in-memory database is a database that primarily resides in main
memory, but is typically backed-up by non-volatile computer data
storage. Main memory databases are faster than disk databases, and
so are often used where response time is critical, such as in
telecommunications network equipment. SAP HANA platform is a very
hot topic for in-memory database. By May 2012, HANA was able to run
on servers with 100TB main memory powered by IBM. The co founder
of the company claimed that the system was big enough to run the 8
largest SAP customers.
 An active database includes an event-driven architecture which can
respond to conditions both inside and outside the database. Possible
uses include security monitoring, alerting, statistics gathering and
authorization. Many databases provide active database features in the
form of database triggers.

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A cloud database relies on cloud technology. Both the database and
most of its DBMS reside remotely, "in the cloud", while its applications
are both developed by programmers and later maintained and utilized
by (application's) end-users through a web browser and Open APIs.
Data warehouses archive data from operational databases and often
from external sources such as market research firms. The warehouse
becomes the central source of data for use by managers and other
end-users who may not have access to operational data. For example,
sales data might be aggregated to weekly totals and converted from
internal product codes to use UPCs so that they can be compared with
ACNielsen data. Some basic and essential components of data
warehousing include extracting, analyzing, and mining data,
transforming, loading and managing data so as to make them available
for further use.
A deductive database combines logic programming with a relational
database, for example by using the Datalog language.
A distributed database is one in which both the data and the DBMS
span multiple computers.
A document-oriented database is designed for storing, retrieving, and
managing document-oriented, or semi structured data, information.
Document-oriented databases are one of the main categories of NoSQL
databases.
An embedded database system is a DBMS which is tightly integrated
with an application software that requires access to stored data in such
a way that the DBMS is hidden from the application’s end-users and
requires little or no ongoing maintenance.[28]
End-user databases consist of data developed by individual end-users.
Examples of these are collections of documents, spreadsheets,
presentations, multimedia, and other files. Several products exist to
support such databases. Some of them are much simpler than fullfledged DBMSs, with more elementary DBMS functionality.
A federated database system comprises several distinct databases,
each with its own DBMS. It is handled as a single database by a
federated
database
management
system
(FDBMS),
which
transparently integrates multiple autonomous DBMSs, possibly of
different types (in which case it would also be a heterogeneous
database system), and provides them with an integrated conceptual
view.
Sometimes the term multi-database is used as a synonym to federated
database, though it may refer to a less integrated (e.g., without an

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FDBMS and a managed integrated schema) group of databases that
cooperate in a single application. In this case typically middleware is
used for distribution, which typically includes an atomic commit
protocol (ACP), e.g., the two-phase commit protocol, to allow
distributed (global) transactions across the participating databases.
A graph database is a kind of NoSQL database that uses graph
structures with nodes, edges, and properties to represent and store
information. General graph databases that can store any graph are
distinct from specialized graph databases such as triplestores and
network databases.
An array DBMS is a kind of NoSQL DBMS that allows to model, store,
and retrieve (usually large) multi-dimensional arrays such as satellite
images and climate simulation output.
In a hypertext or hypermedia database, any word or a piece of text
representing an object, e.g., another piece of text, an article, a picture,
or a film, can be hyperlinked to that object. Hypertext databases are
particularly useful for organizing large amounts of disparate
information. For example, they are useful for organizing online
encyclopedias, where users can conveniently jump around the text.
The World Wide Web is thus a large distributed hypertext database.
A knowledge base (abbreviated KB, kb or Δ[29][30]) is a special kind of
database for knowledge management, providing the means for the
computerized collection, organization, and retrieval of knowledge. Also
a collection of data representing problems with their solutions and
related experiences.
A mobile database can be carried on or synchronized from a mobile
computing device.
Operational databases store detailed data about the operations of an
organization. They typically process relatively high volumes of updates
using transactions. Examples include customer databases that record
contact, credit, and demographic information about a business'
customers, personnel databases that hold information such as salary,
benefits, skills data about employees, enterprise resource planning
systems that record details about product components, parts
inventory, and financial databases that keep track of the organization's
money, accounting and financial dealings.
A parallel database seeks to improve performance through
parallelization for tasks such as loading data, building indexes and
evaluating queries. The major parallel DBMS architectures which are
induced by the underlying hardware architecture are:

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o Shared memory architecture, where multiple processors share
the main memory space, as well as other data storage.
o Shared disk architecture, where each processing unit (typically
consisting of multiple processors) has its own main memory, but
all units share the other storage.
o Shared nothing architecture, where each processing unit has its
own main memory and other storage.
Probabilistic databases employ fuzzy logic to draw inferences from
imprecise data.
Real-time databases process transactions fast enough for the result to
come back and be acted on right away.
A spatial database can store the data with multidimensional features.
The queries on such data include location based queries, like "Where is
the closest hotel in my area?".
A temporal database has built-in time aspects, for example a temporal
data model and a temporal version of SQL. More specifically the
temporal aspects usually include valid-time and transaction-time.
A terminology-oriented database builds upon an object-oriented
database, often customized for a specific field.
An unstructured data database is intended to store in a manageable
and protected way diverse objects that do not fit naturally and
conveniently in common databases. It may include email messages,
documents, journals, multimedia objects, etc. The name may be
misleading since some objects can be highly structured. However, the
entire possible object collection does not fit into a predefined
structured framework. Most established DBMSs now support
unstructured data in various ways, and new dedicated DBMSs are
emerging.

Design and modeling
The first task of a database designer is to produce a conceptual data
model that reflects the structure of the information to be held in the
database. A common approach to this is to develop an entity-relationship
model, often with the aid of drawing tools. Another popular approach is the
Unified Modeling Language. A successful data model will accurately reflect
the possible state of the external world being modeled: for example, if
people can have more than one phone number, it will allow this information
to be captured. Designing a good conceptual data model requires a good
understanding of the application domain; it typically involves asking deep
questions about the things of interest to an organisation, like "can a

customer also be a supplier?", or "if a product is sold with two different forms
of packaging, are those the same product or different products?", or "if a
plane flies from New York to Dubai via Frankfurt, is that one flight or two (or
maybe even three)?". The answers to these questions establish definitions of
the terminology used for entities (customers, products, flights, flight
segments) and their relationships and attributes.
Producing the conceptual data model sometimes involves input from
business processes, or the analysis of workflow in the organization. This can
help to establish what information is needed in the database, and what can
be left out. For example, it can help when deciding whether the database
needs to hold historic data as well as current data.
Having produced a conceptual data model that users are happy with,
the next stage is to translate this into a schema that implements the
relevant data structures within the database. This process is often called
logical database design, and the output is a logical data model expressed in
the form of a schema. Whereas the conceptual data model is (in theory at
least) independent of the choice of database technology, the logical data
model will be expressed in terms of a particular database model supported
by the chosen DBMS. (The terms data model and database model are often
used interchangeably, but in this article we use data model for the design of
a specific database, and database model for the modelling notation used to
express that design.)
The most popular database model for general-purpose databases is the
relational model, or more precisely, the relational model as represented by
the SQL language. The process of creating a logical database design using
this model uses a methodical approach known as normalization. The goal of
normalization is to ensure that each elementary "fact" is only recorded in
one place, so that insertions, updates, and deletions automatically maintain
consistency.
The final stage of database design is to make the decisions that affect
performance, scalability, recovery, security, and the like. This is often called
physical database design. A key goal during this stage is data independence,
meaning that the decisions made for performance optimization purposes
should be invisible to end-users and applications. Physical design is driven
mainly by performance requirements, and requires a good knowledge of the
expected workload and access patterns, and a deep understanding of the
features offered by the chosen DBMS.

Another aspect of physical database design is security. It involves both
defining access control to database objects as well as defining security levels
and methods for the data itself.
Models
A database model is a type of data model that determines the logical
structure of a database and fundamentally determines in which manner data
can be stored, organized, and manipulated. The most popular example of a
database model is the relational model (or the SQL approximation of
relational), which uses a table-based format.
Common logical data models for databases include:
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Navigational databases
o Hierarchical database model
o Network model
o Graph database
Relational model
Entity–relationship model
Enhanced entity–relationship model
Object model
Document model
Entity–attribute–value model
Star schema

An object-relational database combines the two related structures.
Physical data models include:
 Inverted index
 Flat file
Other models include:
 Associative model
 Multidimensional model
 Array model
 Multivalue model
Specialized models are optimized for particular types of data:
 XML database
 Semantic model
 Content store
 Event store
 Time series model

External, conceptual, and internal views
A database management system provides three views of the database
data:
 The external level defines how each group of end-users sees the
organization of data in the database. A single database can have any
number of views at the external level.
 The conceptual level unifies the various external views into a
compatible global view.[32] It provides the synthesis of all the external
views. It is out of the scope of the various database end-users, and is
rather of interest to database application developers and database
administrators.
 The internal level (or physical level) is the internal organization of data
inside a DBMS (see Implementation section below). It is concerned with
cost, performance, scalability and other operational matters. It deals
with storage layout of the data, using storage structures such as
indexes to enhance performance. Occasionally it stores data of
individual views (materialized views), computed from generic data, if
performance justification exists for such redundancy. It balances all the
external views' performance requirements, possibly conflicting, in an
attempt to optimize overall performance across all activities.
While there is typically only one conceptual (or logical) and physical (or
internal) view of the data, there can be any number of different external
views. This allows users to see database information in a more businessrelated way rather than from a technical, processing viewpoint. For example,
a financial department of a company needs the payment details of all
employees as part of the company's expenses, but does not need details
about employees that are the interest of the human resources department.
Thus different departments need different views of the company's database.
The three-level database architecture relates to the concept of data
independence which was one of the major initial driving forces of the
relational model. The idea is that changes made at a certain level do not
affect the view at a higher level. For example, changes in the internal level
do not affect application programs written using conceptual level interfaces,
which reduces the impact of making physical changes to improve
performance.
The conceptual view provides a level of indirection between internal
and external. On one hand it provides a common view of the database,
independent of different external view structures, and on the other hand it

abstracts away details of how the data is stored or managed (internal level).
In principle every level, and even every external view, can be presented by a
different data model. In practice usually a given DBMS uses the same data
model for both the external and the conceptual levels (e.g., relational
model). The internal level, which is hidden inside the DBMS and depends on
its implementation (see Implementation section below), requires a different
level of detail and uses its own types of data structure types.
Separating the external, conceptual and internal levels was a major
feature of the relational database model implementations that dominate 21st
century databases.

Languages
Database languages are special-purpose languages, which do one or
more of the following:
 Data definition language – defines data types and the relationships
among them
 Data manipulation language – performs tasks such as inserting,
updating, or deleting data occurrences
 Query language – allows searching for information and computing
derived information
Database languages are specific to a particular data model. Notable
examples include:
o SQL combines the roles of data definition, data manipulation, and
query in a single language. It was one of the first commercial
languages for the relational model, although it departs in some
respects from the relational model as described by Codd (for example,
the rows and columns of a table can be ordered). SQL became a
standard of the American National Standards Institute (ANSI) in 1986,
and of the International Organization for Standardization (ISO) in 1987.
The standards have been regularly enhanced since and is supported
(with varying degrees of conformance) by all mainstream commercial
relational DBMSs.
o OQL is an object model language standard (from the Object Data
Management Group). It has influenced the design of some of the newer
query languages like JDOQL and EJB QL.
o XQuery is a standard XML query language implemented by XML
database systems such as MarkLogic and eXist, by relational databases

with XML capability such as Oracle and DB2, and also by in-memory
XML processors such as Saxon.
o SQL/XML combines XQuery with SQL.[35]
A database language may also incorporate features like:
 DBMS-specific Configuration and storage engine management
 Computations to modify query results, like counting, summing,
averaging, sorting, grouping, and cross-referencing
 Constraint enforcement (e.g. in an automotive database, only allowing
one engine type per car)
 Application programming interface version of the query language, for
programmer convenience

Performance, security, and availability
Because of the critical importance of database technology to the
smooth running of an enterprise, database systems include complex
mechanisms to deliver the required performance, security, and availability,
and allow database administrators to control the use of these features.
Storage
Database storage is the container of the physical materialization of a
database. It comprises the internal (physical) level in the database
architecture. It also contains all the information needed (e.g., metadata,
"data about the data", and internal data structures) to reconstruct the
conceptual level and external level from the internal level when needed.
Putting data into permanent storage is generally the responsibility of the
database engine a.k.a. "storage engine". Though typically accessed by a
DBMS through the underlying operating system (and often utilizing the
operating systems' file systems as intermediates for storage layout), storage
properties and configuration setting are extremely important for the efficient
operation of the DBMS, and thus are closely maintained by database
administrators. A DBMS, while in operation, always has its database residing
in several types of storage (e.g., memory and external storage). The
database data and the additional needed information, possibly in very large
amounts, are coded into bits. Data typically reside in the storage in
structures that look completely different from the way the data look in the
conceptual and external levels, but in ways that attempt to optimize (the
best possible) these levels' reconstruction when needed by users and

programs, as well as for computing additional types of needed information
from the data (e.g., when querying the database).
Some DBMSs support specifying which character encoding was used to
store data, so multiple encodings can be used in the same database.
Various low-level database storage structures are used by the storage engine
to serialize the data model so it can be written to the medium of choice.
Techniques such as indexing may be used to improve performance.
Conventional storage is row-oriented, but there are also column-oriented and
correlation databases.

Materialized views
Often storage redundancy is employed to increase performance. A
common example is storing materialized views, which consist of frequently
needed external views or query results. Storing such views saves the
expensive computing of them each time they are needed. The downsides of
materialized views are the overhead incurred when updating them to keep
them synchronized with their original updated database data, and the cost of
storage redundancy.
Replication
Occasionally a database employs storage redundancy by database
objects replication (with one or more copies) to increase data availability
(both to improve performance of simultaneous multiple end-user accesses to
a same database object, and to provide resiliency in a case of partial failure
of a distributed database). Updates of a replicated object need to be
synchronized across the object copies. In many cases the entire database is
replicated.
Security
Database security deals with all various aspects of protecting the
database content, its owners, and its users. It ranges from protection from
intentional unauthorized database uses to unintentional database accesses
by unauthorized entities (e.g., a person or a computer program).
Database access control deals with controlling who (a person or a
certain computer program) is allowed to access what information in the
database. The information may comprise specific database objects (e.g.,

record types, specific records, data structures), certain computations over
certain objects (e.g., query types, or specific queries), or utilizing specific
access paths to the former (e.g., using specific indexes or other data
structures to access information). Database access controls are set by
special authorized (by the database owner) personnel that uses dedicated
protected security DBMS interfaces.
This may be managed directly on an individual basis, or by the
assignment of individuals and privileges to groups, or (in the most elaborate
models) through the assignment of individuals and groups to roles which are
then granted entitlements. Data security prevents unauthorized users from
viewing or updating the database. Using passwords, users are allowed
access to the entire database or subsets of it called "subschemas". For
example, an employee database can contain all the data about an individual
employee, but one group of users may be authorized to view only payroll
data, while others are allowed access to only work history and medical data.
If the DBMS provides a way to interactively enter and update the database,
as well as interrogate it, this capability allows for managing personal
databases.
Data security in general deals with protecting specific chunks of data,
both physically (i.e., from corruption, or destruction, or removal; e.g., see
physical security), or the interpretation of them, or parts of them to
meaningful information (e.g., by looking at the strings of bits that they
comprise, concluding specific valid credit-card numbers; e.g., see data
encryption).
Change and access logging records who accessed which attributes,
what was changed, and when it was changed. Logging services allow for a
forensic database audit later by keeping a record of access occurrences and
changes. Sometimes application-level code is used to record changes rather
than leaving this to the database. Monitoring can be set up to attempt to
detect security breaches.

Transactions and concurrency
Database transactions can be used to introduce some level of fault
tolerance and data integrity after recovery from a crash. A database
transaction is a unit of work, typically encapsulating a number of operations
over a database (e.g., reading a database object, writing, acquiring lock,
etc.), an abstraction supported in database and also other systems. Each

transaction has well defined boundaries in terms of which program/code
executions are included in that transaction (determined by the transaction's
programmer via special transaction commands).
The acronym ACID describes some ideal properties of a database
transaction: Atomicity, Consistency, Isolation, and Durability.

Migration
A database built with one DBMS is not portable to another DBMS (i.e.,
the other DBMS cannot run it). However, in some situations it is desirable to
move, migrate a database from one DBMS to another. The reasons are
primarily economical (different DBMSs may have different total costs of
ownership or TCOs), functional, and operational (different DBMSs may have
different capabilities). The migration involves the database's transformation
from one DBMS type to another. The transformation should maintain (if
possible) the database related application (i.e., all related application
programs) intact. Thus, the database's conceptual and external architectural
levels should be maintained in the transformation. It may be desired that
also some aspects of the architecture internal level are maintained. A
complex or large database migration may be a complicated and costly (onetime) project by itself, which should be factored into the decision to migrate.
This in spite of the fact that tools may exist to help migration between
specific DBMSs. Typically a DBMS vendor provides tools to help importing
databases from other popular DBMSs.
Building, maintaining, and tuning
After designing a database for an application, the next stage is building
the database. Typically an appropriate general-purpose DBMS can be
selected to be utilized for this purpose. A DBMS provides the needed user
interfaces to be utilized by database administrators to define the needed
application's data structures within the DBMS's respective data model. Other
user interfaces are used to select needed DBMS parameters (like security
related, storage allocation parameters, etc.).
When the database is ready (all its data structures and other needed
components are defined) it is typically populated with initial application's
data (database initialization, which is typically a distinct project; in many
cases using specialized DBMS interfaces that support bulk insertion) before
making it operational. In some cases the database becomes operational

while empty of application data, and data is accumulated during its
operation.
After the database is created, initialised and populated it needs to be
maintained. Various database parameters may need changing and the
database may need to be tuned (tuning) for better performance;
application's data structures may be changed or added, new related
application programs may be written to add to the application's functionality,
etc.

Backup and restore
Sometimes it is desired to bring a database back to a previous state
(for many reasons, e.g., cases when the database is found corrupted due to a
software error, or if it has been updated with erroneous data). To achieve this
a backup operation is done occasionally or continuously, where each desired
database state (i.e., the values of its data and their embedding in database's
data structures) is kept within dedicated backup files (many techniques exist
to do this effectively). When this state is needed, i.e., when it is decided by a
database administrator to bring the database back to this state (e.g., by
specifying this state by a desired point in time when the database was in this
state), these files are utilized to restore that state.
Static Analysis
Static analysis techniques for software verification can be applied also
in the scenario of query languages. In particular, the *Abstract interpretation
framework has been extended to the field of query languages for relational
databases as a way to support sound approximation techniques.[36] The
semantics of query languages can be tuned according to suitable
abstractions of the concrete domain of data. The abstraction of relational
database system has many interesting applications, in particular, for security
purposes, such as fine grained access control, watermarking, etc.
Other
Other DBMS features might include:
 Database logs
 Graphics component for producing graphs and charts, especially in a
data warehouse system
 Query optimizer – Performs query optimization on every query to
choose for it the most efficient query plan (a partial order (tree) of



operations) to be executed to compute the query result. May be
specific to a particular storage engine.
Tools or hooks for database design, application programming,
application program maintenance, database performance analysis and
monitoring, database configuration monitoring, DBMS hardware
configuration (a DBMS and related database may span computers,
networks, and storage units) and related database mapping (especially
for a distributed DBMS), storage allocation and database layout
monitoring, storage migration, etc.

References:
https://en.wikipedia.org/wiki/Database