it module 3

8/7/2019 IT MODULE 3

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DATA , INFORMATION & KNOWLEDGE

Data in everyday language is a synonym for information [1] . In the exact sciences there isa clear distinction between data and information, where data is a measurement that can bedisorganized and when the data becomes organized it becomes information. Data may

relate to reality, or to fiction as in a fictional movie . Data about reality consists of propositions . A large class of practically important propositions are measurements or observations of a variable . Such propositions may comprise numbers , words or images .

Uses of data in science and computing

Main article: Data (computing)

Raw data are numbers , characters , images or other outputs from devices to convertphysical quantities into symbols, in a very broad sense. Such data are typically further processed by a human or input into a computer , stored and processed there, or transmitted(output ) to another human or computer. Raw data is a relative term; data processingcommonly occurs by stages, and the "processed data" from one stage may be consideredthe "raw data" of the next.

Mechanical computing devices are classified according to the means by which theyrepresent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a datum as a sequence of symbolsdrawn from a fixed alphabet . The most common digital computers use a binary alphabet,that is, an alphabet of two characters, typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet.

Some special forms of data are distinguished. A computer program is a collection of data,which can be interpreted as instructions. Most computer languages make a distinctionbetween programs and the other data on which programs operate, but in some languages,notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata , that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata isthe library catalog, which is a description of the contents of books.

[edit ] Meaning of data, information and knowledge

The terms information and knowledge are frequently used for overlapping concepts.These three concepts are ill- or ambiguously defined in the subject matter literature .However, in recent interdisciplinary research a few independent specializations of theseterms have been proposed

Information is the result of processing, gathering, manipulating and organizing data in away that adds to the knowledge of the receiver. In other words, it is the context in whichdata is taken. [citation needed ]
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Information as a concept bears a diversity of meanings, from everyday usage to technicalsettings. Generally speaking, the concept of information is closely related to notions of constraint , communication , control , data , form , instruction , knowledge , meaning , mentalstimulus , pattern , perception , and representation .

Many people speak about the Information Age as the advent of the Knowledge Age[citation

needed ][Who says this? ] or knowledge society , the information society , and informationtechnologies , and even though informatics , information science and computer science areoften in the spotlight, the word "information" is often used without careful considerationof the various meanings it has acquired.

[edit ] Information as a message

Information is the state of a system of interest. Message is the information materialized.

Information is a quality of a message from a sender to one or more receivers. Informationis always about something (size of a parameter, occurrence of an event, etc). Viewed inthis manner, information does not have to be accurate. It may be a truth or a lie, or justthe sound of a kiss. Even a disruptive noise used to inhibit the flow of communicationand create misunderstanding would in this view be a form of information. However,generally speaking, if the amount of information in the received message increases, themessage is more accurate.

This model assumes there is a definite sender and at least one receiver. Many refinementsof the model assume the existence of a common language understood by the sender andat least one of the receivers. An important variation identifies information as that whichwould be communicated by a message if it were sent from a sender to a receiver capableof understanding the message. However, in requiring the existence of a definite sender,the "information as a message" model does not attach any significance to the idea thatinformation is something that can be extracted from an environment, e.g., throughobservation, reading or measurement.

Information is a term with many meanings depending on context, but is as a rule closelyrelated to such concepts as meaning, knowledge, instruction, communication,representation, and mental stimulus. Simply stated, information is a message received andunderstood. In terms of data, it can be defined as a collection of facts from whichconclusions may be drawn. There are many other aspects of information since it is theknowledge acquired through study or experience or instruction. But overall, informationis the result of processing, manipulating and organizing data in a way that adds to theknowledge of the person receiving it.

Communication theory is a numerical measure of the uncertainty of an outcome. For example, we can say that "the signal contained thousands of bits of information".Communication theory tends to use the concept of information entropy , generallyattributed to C.E. Shannon (see below).
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Another form of information is Fisher information , a concept of R.A. Fisher . This is usedin application of statistics to estimation theory and to science in general. Fisher information is thought of as the amount of information that a message carries about anunobservable parameter. It can be computed from knowledge of the likelihood function defining the system. For example, with a normal likelihood function, the Fisher

information is the reciprocal of the variance of the law. In the absence of knowledge of the likelihood law, the Fisher information may be computed from normally distributedscore data as the reciprocal of their second moment.

Even though information and data are often used interchangeably, they are actually verydifferent. Data is a set of unrelated information, and as such is of no use until it isproperly evaluated. Upon evaluation, once there is some significant relation betweendata, and they show some relevance, then they are converted into information. Now thissame data can be used for different purposes. Thus, till the data convey someinformation, they are not useful.

[edit ] Measuring information entropyThe view of information as a message came into prominence with the publication in 1948of an influential paper by Claude Shannon , "A Mathematical Theory of Communication ."This paper provides the foundations of information theory and endows the wordinformation not only with a technical meaning but also a measure. If the sending device isequally likely to send any one of a set of N messages, then the preferred measure of "theinformation produced when one message is chosen from the set" is the base twologarithm of N (This measure is called self-information ). In this paper, Shannoncontinues:

The choice of a logarithmic base corresponds to the choice of a unit for measuring information. If the base 2 is used the resulting units may be called binary digits, or more briefly bits , a wordsuggested by J. W. Tukey . A device with two stable positions, such as a relay or a flip-flopcircuit, can store one bit of information. N such devices can store N bits [1]

A complementary way of measuring information is provided by algorithmic informationtheory . In brief, this measures the information content of a list of symbols based on howpredictable they are, or more specifically how easy it is to compute the list through aprogram : the information content of a sequence is the number of bits of the shortestprogram that computes it. The sequence below would have a very low algorithmicinformation measurement since it is a very predictable pattern, and as the patterncontinues the measurement would not change. Shannon information would give the sameinformation measurement for each symbol, since they are statistically random , and eachnew symbol would increase the measurement.

123456789101112131415161718192021

It is important to recognize the limitations of traditional information theory andalgorithmic information theory from the perspective of human meaning. For example,when referring to the meaning content of a message Shannon noted Frequently the
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messages have meaning these semantic aspects of communication are irrelevant to theengineering problem. The significant aspect is that the actual message is one selectedfrom a set of possible messages (emphasis in original).

In information theory signals are part of a process, not a substance; they do something,

they do not contain any specific meaning. Combining algorithmic information theory andinformation theory we can conclude that the most random signal contains the mostinformation as it can be interpreted in any way and cannot be compressed. [citation needed ]

Micheal Reddy noted that "'signals' of the mathematical theory are 'patterns that can beexchanged'. There is no message contained in the signal, the signals convey the ability toselect from a set of possible messages." In information theory "the system must bedesigned to operate for each possible selection, not just the one which will actually bechosen since this is unknown at the time of design".

[edit ] Information as a pattern

Information is any represented pattern . This view assumes neither accuracy nor directlycommunicating parties, but instead assumes a separation between an object and itsrepresentation. Consider the following example: economic statistics represent aneconomy , however inaccurately. What are commonly referred to as data in computing ,statistics , and other fields, are forms of information in this sense. The electro-magneticpatterns in a computer network and connected devices are related to something other thanthe pattern itself, such as text characters to be displayed and keyboard input. Signals ,signs , and symbols are also in this category. On the other hand, according to semiotics , data is symbols with certain syntax and information is data with a certain semantic.Painting and drawing contain information to the extent that they represent somethingsuch as an assortment of objects on a table, a profile , or a landscape . In other words,when a pattern of something is transposed to a pattern of something else, the latter isinformation. This would be the case whether or not there was anyone to perceive it.

But if information can be defined merely as a pattern, does that mean that neither utility nor meaning are necessary components of information? Arguably a distinction must bemade between raw unprocessed data and information which possesses utility, value or some quantum of meaning. On this view, information may indeed be characterized as apattern; but this is a necessary condition, not a sufficient one.

An individual entry in a telephone book, which follows a specific pattern formed byname, address and telephone number, does not become "informative" in some senseunless and until it possesses some degree of utility, value or meaning. For example,someone might look up a girlfriend's number, might order a take away etc. The vastmajority of numbers will never be construed as "information" in any meaningful sense.The gap between data and information is only closed by a behavioral bridge wherebysome value, utility or meaning is added to transform mere data or pattern intoinformation.
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When one constructs a representation of an object, one can selectively extract from theobject ( sampling ) or use a system of signs to replace ( encoding ), or both. The samplingand encoding result in representation. An example of the former is a "sample" of aproduct; an example of the latter is "verbal description" of a product. Both containinformation of the product, however inaccurate. When one interprets representation, one

can predict a broader pattern from a limited number of observations (inference) or understand the relation between patterns of two different things ( decoding ). One exampleof the former is to sip a soup to know if it is spoiled; an example of the latter isexamining footprints to determine the animal and its condition. In both cases, informationsources are not constructed or presented by some "sender" of information. Regardless,information is dependent upon, but usually unrelated to and separate from, the medium or media used to express it. In other words, the position of a theoretical series of bits, or even the output once interpreted by a computer or similar device, is unimportant, exceptwhen someone or something is present to interpret the information. Therefore, a quantityof information is totally distinct from its medium.

[edit ] Information as sensory inputOften information is viewed as a type of input to an organism or designed device. Inputsare of two kinds. Some inputs are important to the function of the organism (for example,food) or device ( energy ) by themselves. In his book Sensory Ecology, Dusenbery calledthese causal inputs. Other inputs (information) are important only because they areassociated with causal inputs and can be used to predict the occurrence of a causal inputat a later time (and perhaps another place). Some information is important because of association with other information but eventually there must be a connection to a causalinput. In practice, information is usually carried by weak stimuli that must be detected byspecialized sensory systems and amplified by energy inputs before they can be functionalto the organism or device. For example, light is often a causal input to plants but providesinformation to animals. The colored light reflected from a flower is too weak to do muchphotosynthetic work but the visual system of the bee detects it and the bee's nervoussystem uses the information to guide the bee to the flower, where the bee often findsnectar or pollen, which are causal inputs, serving a nutritional function.

Information is any type of sensory input. When an organism with a nervous systemreceives an input, it transforms the input into an electrical signal. This is regardedinformation by some. The idea of representation is still relevant, but in a slightly differentmanner. That is, while abstract painting does not represent anything concretely, when theviewer sees the painting, it is nevertheless transformed into electrical signals that create a

representation of the painting. Defined this way, information does not have to be relatedto truth, communication, or representation of an object. Entertainment in general is notintended to be informative. Music , the performing arts , amusement parks , works of fiction and so on are thus forms of information in this sense, but they are not necessarilyforms of information according to some definitions given above. Consider another example: food supplies both nutrition and taste for those who eat it. If information isequated to sensory input, then nutrition is not information but taste is.
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[edit ] Information as an influence which leads to atransformation

Information is any type of pattern that influences the formation or transformation of other

patterns. In this sense, there is no need for a conscious mind to perceive, much lessappreciate, the pattern. Consider, for example, DNA . The sequence of nucleotides is apattern that influences the formation and development of an organism without any needfor a conscious mind. Systems theory at times seems to refer to information in this sense,assuming information does not necessarily involve any conscious mind, and patternscirculating (due to feedback ) in the system can be called information. In other words, itcan be said that information in this sense is something potentially perceived asrepresentation, though not created or presented for that purpose.

When Marshall McLuhan speaks of media and their effects on human cultures, he refersto the structure of artifacts that in turn shape our behaviors and mindsets. Also,

pheromones are often said to be "information" in this sense.

(See also Gregory Bateson .)

[edit ] Information as a property in physics

Main article: Physical information

In 2003, J. D. Bekenstein claimed there is a growing trend in physics to define thephysical world as being made of information itself (and thus information is defined in thisway). Information has a well defined meaning in physics. Examples of this include thephenomenon of quantum entanglement where particles can interact without reference totheir separation or the speed of light. Information itself cannot travel faster than lighteven if the information is transmitted indirectly. This could lead to the fact that allattempts at physically observing a particle with an "entangled" relationship to another areslowed down, even though the particles are not connected in any other way other than bythe information they carry.

Another link is demonstrated by the Maxwell's demon thought experiment. In thisexperiment, a direct relationship between information and another physical property,entropy , is demonstrated. A consequence is that it is impossible to destroy informationwithout increasing the entropy of a system; in practical terms this often means generatingheat. Thus, in the study of logic gates , the theoretical lower bound of thermal energyreleased by an AND gate is higher than for the NOT gate (because information isdestroyed in an AND gate and simply converted in a NOT gate ). Physical information isof particular importance in the theory of quantum computers .

[edit ] Information as records
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Records are a specialized form of information. Essentially, records are informationproduced consciously or as by-products of business activities or transactions and retainedbecause of their value. Primarily their value is as evidence of the activities of theorganization but they may also be retained for their informational value. Sound recordsmanagement ensures that the integrity of records is preserved for as long as they are

required.

The international standard on records management, ISO 15489, defines records as"information created, received, and maintained as evidence and information by anorganization or person, in pursuance of legal obligations or in the transaction of business". The International Committee on Archives (ICA) Committee on electronicrecords defined a record as, "a specific piece of recorded information generated, collectedor received in the initiation, conduct or completion of an activity and that comprisessufficient content, context and structure to provide proof or evidence of that activity".

Records may be retained because of their business value, as part of the corporate memory

of the organization or to meet legal, fiscal or accountability requirements imposed on theorganization. Willis (2005) expressed the view that sound management of businessrecords and information delivered "six key requirements for good corporategovernance transparency; accountability; due process; compliance; meeting statutoryand common law requirements; and security of personal and corporate information."

Defining knowledge

See also: epistemology

We suppose ourselves to possess unqualified scientific knowledge of a thing,

as opposed to knowing it in the accidental way in which the sophist knows,when we think that we know the cause on which the fact depends, as thecause of that fact and of no other, and, further, that the fact could not beother than it is. Now that scientific knowing is something of this sort isevident witness both those who falsely claim it and those who actuallypossess it, since the former merely imagine themselves to be, while the latter are also actually, in the condition described. Consequently the proper objectof unqualified scientific knowledge is something which cannot be other thanit is.

Aristotle , Posterior Analytics (Book 1 Part 2)

The definition of knowledge is a matter of on-going debate among philosophers . Theclassical definition is found in, but not ultimately endorsed by, Plato .[1], has it that inorder for there to be knowledge at least three criteria must be fulfilled; that in order tocount as knowledge, a statement must be justified , true , and believed . Some claim thatthese conditions are not sufficient, as Gettier case examples allegedly demonstrate. Thereare a number of alternatives proposed, including Robert Nozick 's arguments for arequirement that knowledge 'tracks the truth' and Simon Blackburn's additionalrequirement that we do not want to say that those who meet any of these conditions
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'through a defect, flaw, or failure' have knowledge. Richard Kirkham suggests that our definition of knowledge requires that the believer's evidence is such that it logically necessitates the truth of the belief.

In contrast to this approach, Wittgenstein observed, following Moore's paradox , that one

can say "He believes it, but it isn't so", but not "He knows it, but it isn't so".[2]

He goes onto argue that these do not correspond to distinct mental states, but rather to distinct waysof talking about conviction. What is different here is not the mental state of the speaker,but the activity in which they are engaged. For example, on this account, to know that thekettle is boiling is not to be in a particular state of mind, but to perform a particular task with the statement that the kettle is boiling. Wittgenstein sought to bypass the difficultyof definition by looking to the way "knowledge" is used in natural languages. He sawknowledge as a case of a family resemblance .

[edit ] Reliable knowledge

In An Introduction to Logic and Scientific Method (1934), Morris R. Cohen and ErnestNagel reviewed the pursuit of truth as determined by logical considerations. Theyreviewed ways of eliminating doubt and arriving at stable beliefs or reliable knowledge,such as

The method of authority The method of intuition The methods of experimental inquiry:

o Types of invariant relationso The experimental method in generalo The method of agreemento The method of difference [citation needed ] o The joint method of agreement and differenceo The method of concomitant variationo The doctrine of the uniformity of natureo The plurality of causes

Their final conclusion was, "Scientific method we declare as the most assured techniqueman has yet devised for controlling the flux of things and establishing stable beliefs."

In an essay entitled "Inductive Method and Scientific Discovery," Marcello Pera said, "Inthe first place, the scientific method is a procedure , a general strategy that indicates anordered sequence of moves (or steps) which the scientist has to make (or go through) inorder to reach the goal of his research." (In On Scientific Discovery , edited by Grmek,Cohen, and Cimino [1977], published in the Boston Studies in the Philosophy of ScienceSeries.) The scientific method is not a method directly applied, but rather a guide to themental activity stages of originating, refining, extending, and applying knowledge. It issubject neutral and flexible in use; it is thus suitable for all domains.
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Statements about truth must be viewed skeptically. Rather than state something as "true,"the following phrase should be used: "On the evidence available today the balance of probability favors the view that..." (V. Gordon Childe, Man Makes Himself , 1936)

The literature contains hundreds of formulas for the scientific method. They are basically

the same but differ in length and terminology. In an article "Suggestions for Teaching theScientific Method" published in the March 1961 issue of American Biology Teacher , Dr.Kenneth B.M. Crooks suggested this one:

1. Curiosity2. Is there a problem?3. Get the evidence4. Attributes needed5. Weigh all evidence6. Make the educated guess (hypothesis)7. Challenge the hypothesis

8. Get a conclusion9. Suspend judgment10. Deductive reasoning

[edit ] Communicating knowledge

Symbolic representations can be used to store meaning. The mapping from the symbolicrepresentation to meaning can be thought of as a dynamic process. Hence also thetransfer of the symbolic representation can be viewed as an ascription process wherebyknowledge can be transferred. We talk of 'data' (the symbols) and 'information' (themeaning). Knowledge is contextual however, so explanation and investigation is usuallyneeded to transfer appropriate state information around which an appropriateinterpretation can be established in the case of two entities, a 'conversation' ensuesduring which 'understanding' can be developed.

[edit ] Situated knowledge

Situated knowledge is knowledge specific to a particular situation. Imagine two verysimilar breeds of mushroom, which grow on either side of a mountain, one nutritious, onepoisonous. Relying on knowledge from one side of an ecological boundary, after crossingto the other, may lead to starving rather than eating perfectly healthy food near at hand, or

to poisoning oneself by mistake.Some methods of generating knowledge, such as trial and error , or learning fromexperience , tend to create highly situational knowledge. One of the main benefits of thescientific method is that the theories it generates are much less situational thanknowledge gained by other methods.

Situational knowledge is often embedded in language, culture, or traditions.
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Knowledge generated through experience is called knowledge "a posteriori", meaningafterwards. The pure existence of a term like "a posteriori" means this also has acounterpart. In this case that is knowledge "a priori", meaning before. The knowledgeprior to any experience means that there are certain "assumptions" that one takes for granted. For example if one is being told about a chair it is clear to him that the chair is in

space , that it is 3D. This knowledge is not knowledge that one can "forget", evensomeone suffering from amnesia experiences the world in 3D. See also: A priori and aposteriori .

[edit ] Partial knowledge

One discipline of epistemology focusses on partial knowledge. In most realistic cases, itis not possible to have an exhaustive understanding of an information domain, so then wehave to live with the fact that our knowledge is always not complete , that is, partial. Mostreal problems have to be solved by taking advantage of a partial understanding of theproblem context and problem data. That is very different from the typical simple mathproblems that we solve at school, where all data are given and we have a perfectunderstanding of formulas necessary to solve them.

[edit ] Knowledge management

Main article: Knowledge management

Knowledge management is a management theory which emerged in the 1990s. It seeks tounderstand the way in which knowledge is created, used and shared within organizations.A significant part of Knowledge Management theory and practice aligns two models: (i)

the DIKW model, which places data, information, knowledge and wisdom into anincreasingly useful pyramid. (ii) Nonaka's reformulation of Polanyi's distinction betweentacit and explicit knowledge . Both of these models are increasingly under challenge withdifferent schools of thought emerging which are more fully described and referenced inthe main article.

An objective of mainstream knowledge management is to ensure that the right information is delivered to the right person just in time, in order to take the most

appropriate decision. In that sense, knowledge management is not interested in managingknowledge per se , but to relate knowledge and its usage. This leads to Organizational

Memory Systems . More recent developments have focused on managing networks (the

flow of knowledge rather than knowledge itself) and narrative forms of knowledgeexchange.

DATA BASE MANAGEMENT SYSTEM

A database management system (DBMS ) is computer software designed for thepurpose of managing databases . Typical examples of DBMSs include Oracle , DB2 ,
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Microsoft Access , Microsoft SQL Server , PostgreSQL , MySQL and FileMaker . DBMSsare typically used by Database administrators in the creation of Database systems .

[edit ] Description

A DBMS is a complex set of software programs that controls the organization , storage , management, and retrieval of data in a database . A DBMS includes:

1. A modeling language to define the schema of each database hosted in the DBMS,according to the DBMS data model .

o The four most common types of organizations are the hierarchical ,network , relational and object models. Inverted lists and other methods arealso used. A given database management system may provide one or moreof the four models. The optimal structure depends on the naturalorganization of the application's data, and on the application'srequirements (which include transaction rate (speed), reliability,maintainability, scalability, and cost).

o The dominant model in use today is the ad hoc one embedded in SQL ,despite the objections of purists who believe this model is a corruption of the relational model, since it violates several of its fundamental principlesfor the sake of practicality and performance. Many DBMSs also supportthe Open Database Connectivity API that supports a standard way for programmers to access the DBMS.

2. Data structures (fields, records, files and objects) optimized to deal with verylarge amounts of data stored on a permanent data storage device (which impliesrelatively slow access compared to volatile main memory ).

3. A database query language and report writer to allow users to interactivelyinterrogate the database, analyze its data and update it according to the usersprivileges on data.

o It also controls the security of the database.o Data security prevents unauthorized users from viewing or updating the

database. Using passwords, users are allowed access to the entire databaseor subsets of it called subschemas . For example, an employee database cancontain all the data about an individual employee, but one group of usersmay be authorized to view only payroll data, while others are allowedaccess to only work history and medical data.

o If the DBMS provides a way to interactively enter and update thedatabase, as well as interrogate it, this capability allows for managingpersonal databases. However, it may not leave an audit trail of actions or provide the kinds of controls necessary in a multi-user organization. Thesecontrols are only available when a set of application programs arecustomized for each data entry and updating function.

4. A transaction mechanism, that ideally would guarantee the ACID properties, inorder to ensure data integrity , despite concurrent user accesses (concurrencycontrol ), and faults ( fault tolerance ).

o It also maintains the integrity of the data in the database.
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o The DBMS can maintain the integrity of the database by not allowingmore than one user to update the same record at the same time. TheDBMS can help prevent duplicate records via unique index constraints; for example, no two customers with the same customer numbers (key fields)can be entered into the database. See ACID properties for more

information (Redundancy avoidance).

The DBMS accepts requests for data from the application program and instructs theoperating system to transfer the appropriate data.

When a DBMS is used, information systems can be changed much more easily as theorganization's information requirements change. New categories of data can be added tothe database without disruption to the existing system.

Organizations may use one kind of DBMS for daily transaction processing and thenmove the detail onto another computer that uses another DBMS better suited for random

inquiries and analysis. Overall systems design decisions are performed by dataadministrators and systems analysts. Detailed database design is performed by databaseadministrators.

Database servers are specially designed computers that hold the actual databases and runonly the DBMS and related software. Database servers are usually multiprocessor computers, with RAID disk arrays used for stable storage. Connected to one or moreservers via a high-speed channel, hardware database accelerators are also used in largevolume transaction processing environments.

DBMS's are found at the heart of most database applications . Sometimes DBMSs are

built around a private multitasking kernel with built-in networking support althoughnowadays these functions are left to the operating system .

[edit ] Features and Abilities Of DBMS

One can characterize a DBMS as an "attribute management system" where attributes aresmall chunks of information that describe something. For example, "color" is an attributeof a car. The value of the attribute may be a color such as "red", "blue", "silver", etc.Lately databases have been modified to accept large or unstructured (pre-digested or pre-categorized) information as well, such as images and text documents. However, the mainfocus is still on descriptive attributes.

DBMS roll together frequently-needed services or features of attribute management. Thisallows one to get powerful functionality "out of the box" rather than program each fromscratch or add and integrate them incrementally. Such features include:
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[edit ] Query ability

Querying is the process of requesting attribute information from various perspectives andcombinations of factors. Example: "How many 2-door cars in Texas are green?"

A database query language and report writer to allow users to interactively interrogate thedatabase, analyze its data and update it according to the users privileges on data. It alsocontrols the security of the database. Data security prevents unauthorized users fromviewing or updating the database. Using passwords, users are allowed access to the entiredatabase or subsets of it called subschemas. For example, an employee database cancontain all the data about an individual employee, but one group of users may beauthorized to view only payroll data, while others are allowed access to only work historyand medical data. If the DBMS provides a way to interactively enter and update thedatabase, as well as interrogate it, this capability allows for managing personal databases.However, it may not leave an audit trail of actions or provide the kinds of controlsnecessary in a multi-user organization. These controls are only available when a set of

application programs are customized for each data entry and updating function.

[edit ] Backup and replication

Copies of attributes need to be made regularly in case primary disks or other equipmentfails. A periodic copy of attributes may also be created for a distant organization thatcannot readily access the original. DBMS usually provide utilities to facilitate the processof extracting and disseminating attribute sets.

When data is replicated between database servers, so that the information remainsconsistent throughout the database system and users cannot tell or even know which

server in the DBMS they are using, the system is said to exhibit replication transparency.

[edit ] Rule enforcement

Often one wants to apply rules to attributes so that the attributes are clean and reliable.For example, we may have a rule that says each car can have only one engine associatedwith it (identified by Engine Number). If somebody tries to associate a second enginewith a given car, we want the DBMS to deny such a request and display an error message. However, with changes in the model specification such as, in this example,hybrid gas-electric cars, rules may need to change. Ideally such rules should be able to beadded and removed as needed without significant data layout redesign.

[edit ] Security

Often it is desirable to limit who can see or change which attributes or groups of attributes. This may be managed directly by individual, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through theassignment of individuals and groups to roles which are then granted entitlements.
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[edit ] Computation

There are common computations requested on attributes such as counting, summing,averaging, sorting, grouping, cross-referencing, etc. Rather than have each computer application implement these from scratch, they can rely on the DBMS to supply such

calculations.

[edit ] Change and access logging

Often one wants to know who accessed what attributes, what was changed, and when itwas changed. Logging services allow this by keeping a record of access occurrences andchanges.

[edit ] Automated optimization

If there are frequently occurring usage patterns or requests, some DBMS can adjust

themselves to improve the speed of those interactions. In some cases the DBMS willmerely provide tools to monitor performance, allowing a human expert to make thenecessary adjustments after reviewing the statistics collected.....

[edit ] Meta-data repositoryMain article: Metadata

Metadata (also spelled meta-data ) is information about information. For example, alisting that describes what attributes are allowed to be in data sets is called "meta-information".

[edit ] History

Databases have been in use since the earliest days of electronic computing. Unlikemodern systems which can be applied to widely different databases and needs, the vastmajority of older systems were tightly linked to the custom databases in order to gainspeed at the expense of flexibility. Originally DBMSs were found only in largeorganizations with the computer hardware needed to support large data sets.

[edit ] Navigational DBMS

As computers grew in capability, this trade-off became increasingly unnecessary and anumber of general-purpose database systems emerged; by the mid- 1960s there were anumber of such systems in commercial use. Interest in a standard began to grow, andCharles Bachman , author of one such product, IDS , founded the Database Task Group within CODASYL , the group responsible for the creation and standardization of COBOL . In 1971 they delivered their standard, which generally became known as theCodasyl approach , and soon there were a number of commercial products based on itavailable.
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The Codasyl approach was based on the "manual" navigation of a linked data set whichwas formed into a large network. When the database was first opened, the program washanded back a link to the first record in the database, which also contained pointers toother pieces of data. To find any particular record the programmer had to step throughthese pointers one at a time until the required record was returned. Simple queries like

"find all the people in Sweden" required the program to walk the entire data set andcollect the matching results. There was, essentially, no concept of "find" or "search". Thismight sound like a serious limitation today, but in an era when the data was most oftenstored on magnetic tape such operations were too expensive to contemplate anyway.

IBM also had their own DBMS system in 1968, known as IMS . IMS was a developmentof 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 insteadof 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 award presentation was The Programmer as Navigator .

IMS is classified as a hierarchical database . IDS and IDMS (both CODASYL databases)as well as CINCOMs TOTAL database are classified as network databases .

[edit ] Relational DBMS

Edgar Codd worked at IBM in San Jose, California , in one of their offshoot offices thatwas primarily involved in the development of hard disk systems. He was unhappy withthe navigational model of the Codasyl approach, notably the lack of a "search" facility

which was becoming increasingly useful. In 1970, he wrote a number of papers thatoutlined a new approach to database construction that eventually culminated in thegroundbreaking A Relational Model of Data for Large Shared Data Banks .[1]

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 inCodasyl, Codd's idea was to use a " table " of fixed-length records. A linked-list systemwould be very inefficient when storing "sparse" databases where some of the data for anyone record could be left empty. The relational model solved this by splitting the data intoa series of normalized tables, with optional elements being moved out of the main table towhere they would take up room only if needed.
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In the relational model, related records are linked together with a "key".

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 navigationalapproach all of these data would be placed in a single record, and unused items wouldsimply not be placed in the database. In the relational approach, the data would benormalized 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 numberswere 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. Wheninformation was being collected about a user, information stored in the optional (or related ) tables would be found by searching for this key. For instance, if the login nameof a user is unique, addresses and phone numbers for that user would be recorded withthe login name as its key. This "re-linking" of related data back into a single collection issomething that traditional computer languages are not designed for.

Just as the navigational approach would require programs to loop in order to collectrecords, the relational approach would require loops to collect information about any onerecord. Codd's solution to the necessary looping was a set-oriented language, asuggestion that would later spawn the ubiquitous SQL . Using a branch of mathematicsknown as tuple calculus , he demonstrated that such a system could support all theoperations of normal databases (inserting, updating etc.) as well as providing a simplesystem 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 MichaelStonebraker . They started a project known as INGRES using funding that had alreadybeen allocated for a geographical database project, using student programmers to producecode. Beginning in 1973, INGRES delivered its first test products which were generallyready for widespread use in 1979. During this time, a number of people had moved"through" the group perhaps as many as 30 people worked on the project, about five at
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a time. INGRES was similar to System R in a number of ways, including the use of a"language" for data access, known as QUEL QUEL was in fact relational, having beenbased on Codd's own Alpha language, but has since been corrupted to follow SQL, thusviolating much the same concepts of the relational model as SQL itself.

IBM itself did only one test implementation of the relational model, PRTV , and aproduction one, Business System 12 , both now discontinued. Honeywell did MRDS for Multics , and now there are two new implementations: Alphora Dataphor and Rel . Allother DBMS implementations usually called relational are actually SQL DBMSs.

In 1968, the [2] University of Michigan began development of the Micro DBMS relational database management system. It was used to manage very large data sets by theUS Department of Labor, the Environmental Protection Agency and researchers fromUniversity of Alberta, the University of Michigan and Wayne State University. It ran onmainframe computers using Michigan Terminal System . The system remained inproduction until 1996.

[edit ] SQL DBMS

IBM started working on a prototype system loosely based on Codd's concepts as SystemR in the early 1970s unfortunately, System R was conceived as a way of provingCodd's ideas unimplementable [citation needed ], and thus the project was delivered to a group of programmers who were not under Codd's supervision, never understood his ideas fullyand ended up violating several fundamentals of the relational model. The first "quickie"version was ready in 1974 /5, and work then started on multi-table systems in which thedata could be broken down so that all of the data for a record (much of which is oftenoptional) 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 querylanguage , SQL , had been added. Codd's ideas were establishing themselves as bothworkable and superior to Codasyl, pushing IBM to develop a true production version of System R, known as SQL/DS , and, later, Database 2 (DB2 ).

Many of the people involved with INGRES became convinced of the future commercialsuccess of such systems, and formed their own companies to commercialize the work butwith an SQL interface. Sybase , Informix , NonStop SQL and eventually Ingres itself wereall being sold as offshoots to the original INGRES product in the 1980s. Even MicrosoftSQL Server is actually a re-built version of Sybase, and thus, INGRES. Only LarryEllison 's Oracle started from a different chain, based on IBM's papers on System R, by

beating them 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 primarily used for globalmission critical applications (the .org and .info domain name registries use it as their primary data store, as do many large companies and financial institutions).
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In Sweden , Codd's paper was also read and Mimer SQL was developed from the mid-70sat Uppsala University . In 1984, this project was consolidated into an independententerprise. In the early 1980s, Mimer introduced transaction handling for high robustnessin applications, an idea that was subsequently implemented on most other DBMS.

Data processingData processing is any computer process that converts data into information or knowledge . The processing is usually assumed to be automated and running on acomputer . Because data are most useful when well-presented and actually informative ,data-processing systems are often referred to as information systems to emphasize their practicality. Nevertheless, both terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, andlikewise information systems typically take raw data as input to produce information asoutput.

To better market their profession, a computer programmer or a systems analyst that mightonce have referred, such as during the 1970s, to the computer systems that they produceas data-processing systems more often than not nowadays refers to the computer systemsthat they produce by some other term that includes the word information , such asinformation systems, information technology systems, or management informationsystems .

In the context of data processing, data are defined as numbers or characters that representmeasurements from observable phenomena. A single datum is a single measurement fromobservable phenomena. Measured information is then algorithmically derived and/or

logically deduced and/or statistically calculated from multiple data. ( evidence ).Information is defined as either a meaningful answer to a query or a meaningful stimulusthat can cascade into further queries.

More generally, the term data processing can apply to any process that converts datafrom one format to another, although data conversion would be the more logical andcorrect term. From this perspective, data processing becomes the process of convertinginformation into data and also the converting of data back into information. Thedistinction is that conversion doesn't require a question (query) to be answered. For example, information in the form of a string of characters forming a sentence in Englishis converted or encoded from a keyboard's key-presses as represented by hardware-oriented integer codes into ASCII integer codes after which it may be more easilyprocessed by a computernot as merely raw, amorphous integer data, but as ameaningful character in a natural language 's set of graphemes and finally converted or decoded to be displayed as characters, represented by a font on the computer display. Inthat example we can see the stage-by-stage conversion of the presence of and thenabsence of electrical conductivity in the key-press and subsequent release at the keyboardfrom raw substantia

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