Anno unc e me nts FIT100 FIT100 FIT100 Quiz c a nc e le d fo r - - PowerPoint PPT Presentation

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Anno unc e me nts

• Quiz c a nc e le d fo r this we e k

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Anno unc e me nts

• Quic k Write this we e k o n Priva c y

∗ (F

irst ha lf o f c ha pte r 13 in F

lue nc y)

(F irst ha lf o f c ha pte r 13 in F

lue nc y)

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A T a b le with a Vie w A T a b le with a Vie w

Data S to rag e and T ransfe r with XML and Database s

Diffe re nc e s Be twe e n T a b le s a nd

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Diffe re nc e s Be twe e n T a b le s a nd Da ta b a se s

i f f i

• Whe n we think o f da ta b a se s, we o fte n think
• f ta b le s o f info rma tio n

C i T b l

• Co mpa ring T

a b le s

∗ Da ta b a se ta b le s

• Me ta da ta ta g ide ntifying e a c h o f the da ta fie lds
• Me ta da ta ta g ide ntifying e a c h o f the da ta fie lds

∗ Spre a dshe e t ta b le s

• Re ly o n po sitio n to ke e p the inte g rity o f the ir da ta

∗ HT

ML ta b le s

• Da ta a s ta b le e ntrie s with no uniq ue ide ntity a t a ll
• Co nc e rne d o nly with ho w to displa y the da ta no t with

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• Co nc e rne d o nly with ho w to displa y the da ta , no t with

its me a ning

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T he Da ta b a se Adva nta g e

• Me ta da ta is ke y a dva nta g e o f

da ta b a se s o ve r o the r syste ms y re c o rding da ta a s ta b le s

• T

wo o f the mo st impo rta nt ro le s in T wo o f the mo st impo rta nt ro le s in de fining me ta da ta

∗ I

de ntify the type o f da ta with a uniq ue

∗ I

de ntify the type o f da ta with a uniq ue ta g

∗ De fine the re la tio nships o f the da ta

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∗ De fine the re la tio nships o f the da ta

XML : A L a ng ua g e fo r

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XML : A L a ng ua g e fo r Me ta da ta T a g s

• E

xte nsib le Ma rkup L a ng ua g e

∗ T

a g g ing sc he me simila r to XHT ML T a g g ing sc he me simila r to XHT ML

∗ No sta nda rd ta g s to le a rn

• Se lf-de sc rib ing , think up the ta g s yo u ne e d

g , p g y

∗ Wo rks we ll with b ro wse rs a nd We b -

b a se d a pplic a tio ns pp

∗ Use a simple te xt e dito r ∗ XML

ta g na me s c a nno t c o nta in spa c e s

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g p

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Exte nsib le Ma rkup La ng ua g e

XML

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An E xa mple fro m T a hiti

• Are a in km2 fo r T

a hiti & ne ig hb o ring isla nds

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An E xa mple fro m T a hiti

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An E xa mple fro m T a hiti (c o nt'd)

• F

irst line

<? xml ve rsio n="1.0" e nc o ding ="I SO-8859- ? xml ve rsio n 1.0 e nc o ding I SO 8859 1" ? >

• F

ile sho uld b e ASCI I te xt F ile sho uld b e ASCI I te xt

• F

ile e xte nsio n sho uld b e .xml

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E xpa nding Use o f XML

• Co mb ine e nc o ding s o f two a rc hipe la g o s

– the Windwa rd a nd the Ga la pa g o s I sla nds

• Ro o t e le me nt is the ta g tha t e nc lo se s a ll

f th t t f th XML fil

• f the c o nte nt o f the XML

file

∗ <a rc hipe la g o > in F

ig . 16.1

∗ <g e o fe a ture > in F

ig 16 2

∗ <g e o _fe a ture > in F

ig . 16.2

• I

nde nting fo r re a da b ility a nd struc ture

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Attrib ute s in XML

• Use a ttrib ute s fo r a dditio na l

me ta da ta , no t fo r a dditio na l , c o nte nt

∗ No t g o o d, na me is c o nte nt:

No t g o o d, na me is c o nte nt:

<a rc hipe la g o na me ="Ga la pa g o s">

∗ Be tte r to g ive a lte rna te fo rm o f the

da ta

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<a _na me a c c e nts="Ga l&a a c ute ;pa g o s">Ga la pa g o s</ a _na me >

E ffe c tive

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E ffe c tive De sig n with XML T a g s

• I

de ntific a tio n Rule : L a b e l Da ta with T a g s Co nsiste ntly g y

∗ Yo u c a n c ho o se wha te ve r ta g na me s

yo u with to na me da ta , b ut o nc e y yo u've de c ide d o n a ta g fo r a pa rtic ula r kind o f da ta , yo u must l d it ith th t t a lwa ys surro und it with tha t ta g .

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E ffe c tive De sig n with XML T a g s

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E ffe c tive De sig n with XML T a g s (c o nt'd)

• Affinity Rule : Gro up Re la te d Da ta

∗ E

nc lo se in a pa ir o f ta g s a ll ta g g e d da ta re fe rring t th tit G i it k it ll to the sa me e ntity. Gro uping it ke e ps it a ll to g e the r, b ut the ide a is muc h mo re funda me nta l: Gro uping ma ke s a n a sso c ia tio n o f the ta g g e d da ta ite ms a s b e ing re la te d to e a c h

• the r, pro pe rtie s o f the sa me thing .

∗ Gro ups to g e the r da ta fo r a sing le thing

a n

∗ Gro ups to g e the r da ta fo r a sing le thing – a n

isla nd

• Asso c ia tio n is a mo ng pro pe rtie s o f a n o b je c t

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E ffe c tive De sig n with XML T a g s

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E ffe c tive De sig n with XML T a g s (c o nt'd)

• Co lle c tio n Rule : Gro up Re la te d I

nsta nc e s

∗ Whe n yo u ha ve se ve ra l insta nc e s o f the sa me

ki d f d t l th i t i it k kind o f da ta , e nc lo se the m in ta g s; a g a in, it ke e ps the m to g e the r a nd implie s tha t the y a re re la te d b y b e ing insta nc e s o f the sa me type .

∗ Gro ups to g e the r da ta o f se ve ra l insta nc e o f the

sa me thing – isla nds

Asso c ia tio n is a mo ng the o b je c ts the mse lve s (e ntitie s)

• Asso c ia tio n is a mo ng the o b je c ts the mse lve s (e ntitie s)

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T he XML T re e

• XML

e nc o ding s o f info rma tio n pro duc e hie ra rc hic a l de sc riptio ns p p tha t c a n b e tho ug ht o f a s tre e s

∗ Hie ra rc hy a c o nse q ue nc e o f ho w ta g s

Hie ra rc hy a c o nse q ue nc e o f ho w ta g s e nc lo se o ne a no the r a nd the da ta

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FIT100 FIT100 FIT100 DA T A BA SES DA T A BA SES

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Wha t is a Da ta b a se

• Any o rg a nize d c o lle c tio n o f da ta
• A c o lle c tio n o f simila r da ta

A c o lle c tio n o f simila r da ta

• E

xa mple s o f da ta b a se s:

T l h b k hit

∗ T

e le pho ne b o o k white pa g e s

∗ T

.V. Guide Ai li ti t

∗ Airline re se rva tio n syste m ∗ Mo to r ve hic le re g istra tio n re c o rds ∗ Pa pe rs in yo ur filing c a b ine t ∗ F

ile s o n yo ur c o mpute r ha rd drive

Da ta | I nfo rma tio n |

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K no wle dg e

• Da ta

∗ Ca n b e de fine d in ma ny wa ys ∗ I

S de fine s da ta a s unpro c e sse d info rma tio n

• I

nfo rma tio n

∗ Da ta tha t ha ve b e e n o rg a nize d a nd c o mmunic a te d in a

Da ta tha t ha ve b e e n o rg a nize d a nd c o mmunic a te d in a c o he re nt a nd me a ning ful ma nne r

• K

no wle dg e

K l d i f ti l t d d i d th t

∗ K

no wle dg e —info rma tio n e va lua te d a nd o rg a nize d so tha t it c a n b e use d purpo se fully

Data is converted into information, and information is converted into knowledge

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Da ta vs. I nfo rma tio n

• We c o lle c t da ta
• I

nfo rma tio n is ha rve ste d fro m da ta I nfo rma tio n is ha rve ste d fro m da ta

M i d t ll ti

• Ma ny c o mpa nie s a re g o o d a t c o lle c ting

da ta

• F

e we r a re g o o d a t ha rve sting info rma tio n

• F

e we r a re g o o d a t ha rve sting info rma tio n

Ultima te Purpo se o f a Da ta b a se

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Ultima te Purpo se o f a Da ta b a se Ma na g e me nt Syste m (DBMS)

To transform To transform

Data Information Knowledge

Action

Why do we ne e d a

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y da ta b a se ?

• K

e e p re c o rds o f o ur:

∗ Clie nts ∗ Sta ff ∗ Vo lunte e rs

• T
• ke e p a re c o rd o f

T

• ke e p a re c o rd o f

a c tivitie s a nd inte rve ntio ns

• K

e e p sa le s re c o rds

• De ve lo p re po rts
• De ve lo p re po rts
• Pe rfo rm re se a rc h

Da ta b a se T e rmino lo g y

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Da ta b a se T e rmino lo g y

Fields (columns)

Phone book:

Anderson Thomas A 123 Marine View Dr. 237-1234 Benson Karen C 1300 California Ave 237-1098 Casserly Rick W 12492 Rd 19 342-0502 Drummond Lynn M 12059 30th Ave W 931 1105 Records (rows)

Field

• Smallest unit of information in a table
• First name

Drummond Lynn M 12059 30th Ave W 931-1105

Table

Field (the columns in a table) Smallest unit of information in a table

• Sometime called “attributes”

First name

• Last name
• Middle initial
• Street address
• Phone number(s)

Record (the rows in a table)

• All related fields are collectively called

a record

• All fields for one person are a

record

Table

• A collection of records is a data table
• Collection of everyone’s

records Database Management System (DBMS)

• All the related tables, queries, data

entry and edit forms, reports, macros and VBA modules constitute a database

Da ta b a se

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Ma na g e me nt Syste m (DBMS)

• So ftwa re to o ls fo r wo rking with da ta
• De sig ne d to :

St (t b l )

∗ Sto re (ta b le s) ∗ Org a nize (so rt) ∗ Add mo dify o r de le te ∗ Add, mo dify o r de le te ∗ Ask q ue stio ns (q ue rie s) ∗ Pro duc e fo rms a nd re po rts

i i

• Summa rizing
• Displa ying de ta ils
• T
• o lb o x is a g o o d a na lo g y
• o b o s a g o o d a a o g y

DBMS E xa mple s

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DBMS E xa mple s

• Mic ro so ft Ac c e ss
• Struc ture d Que ry L

a ng ua g e (SQL ) Struc ture d Que ry L a ng ua g e (SQL )

∗ Mic ro so ft SQL

Se rve r

∗ Ora c le ∗ Ora c le ∗ MySQL

F il M k P

• F

ile Ma ke r Pro

• L
• tus No te s
• Ope n Offic e Ba se

F la t-F ile

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• vs. Re la tio na l Da ta b a se
• F

la t-F ile Da ta b a se

∗ All re le va nt da ta in a sing le ta b le , o r se rie s o f unre la te d

ta b le s

∗ Wo rk b e st fo r sma ll q ua ntitie s o f da ta ; whe re vie wing

a nd so rting the da ta in a sing le list do e s no t c re a te a time -c o nsuming ta sk

∗ T

ypic a lly a pe rso n’ s first da ta b a se s

∗ E

xa mple : E xc e l spre a dshe e t o r Wo rd da ta list file

• Re la tio na l Da ta b a se

∗ Pro vide a so lutio n to da ta e ntry re dunda nc y pro b le ms ∗ L

inke d thro ug h c o mmo n fie lds (c o lumns) with e xa c tly the sa me da ta

∗ T

a b le s linke d to g e the r c a n b e q ue rie d a s if o ne ta b le

F la t-F ile E xa mple

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F la t F ile E xa mple

• We a kne sse s c o mmo n to fla t-file syste ms

∗ Duplic a te info rma tio n in the ta b le ∗ I

nc o nsiste nc ie s in the wa y Supe rviso r Na me s a re

∗ I

nc o nsiste nc ie s in the wa y Supe rviso r Na me s a re e nte re d

Re la tio na l

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Da ta b a se E xa mple

Da ta b a se T a b le s

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Da ta b a se T a b le s

Que ry fro m T wo T a b le s

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Que ry fro m T wo T a b le s

F

• rms

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F

• rms

Re po rts

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Re po rts

FIT100 FIT100 FIT100 REL A T IO NA L DA T A BA SES REL A T IO NA L DA T A BA SES

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Vide o

• Re la tio na l da ta b a se s a nd ta b le s

Re la tio na l

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Re la tio na l Da ta b a se Mo de l

• I

ntro duc e d b y E . F . Co dd in 1970

• A L
• g ic a l Vie w o f Da ta
• A L
• g ic a l Vie w o f Da ta

∗ E

na b le s de ve lo pe r to vie w da ta lo g ic a lly ra the r tha n physic a lly ra the r tha n physic a lly

∗ Gre a te r lo g ic a l simplic ity te nds to yie ld

simple r a nd mo re e ffe c tive da ta b a se simple r a nd mo re e ffe c tive da ta b a se de sig n me tho do lo g ie s

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T a b le s

• Co rne rsto ne o f Re la tio na l DBMS
• Co rne rsto ne o f Re la tio na l DBMS
• Adva nta g e s – struc tura l a nd da ta

inde pe nde nc e

• Co nc e ptua lly Re se mb le s a file

∗ No te a file is a c tua lly a physic a l struc ture

E i t d t d th it hi hi l d

• E

a sie r to unde rsta nd tha n its hie ra rc hic a l a nd ne two rk da ta b a se pre de c e sso rs

T a b le Cha ra c te ristic s

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