data warehousing lecture 1

8/17/2019 Data Warehousing Lecture 1

1/14

Lecture 1

Mr. Suleiman M Yussuf

1


2/14

Before the 1970’s Computers were thought toprovide only computational power

During the 70’s, people started expectingcomputers to also generate and maintain

processes that support the decision-makingcapability of human beings

he primary reason for this change was theinformation overload pro!lem

"ots of data was starting to get generated ona daily !asis# $edia, %nternet, &nterprisetransactions etc'

(lso, the development of the relational data

model facilitated data!ase storage and retrieval'2


3/14

)igh*level &nterprise employees needed to siftthrough voluminous data in order to extractuseful information for ma+ing decisions &'g', a 1*year record of sales for a particular

product, a freuency distri!ution of customers

for the past - months etc' .ot all the managerial needs could !e satisfied

through traditional systems Decision /upport /ystems D// were developed

that specially prepared, separated, and staged the data that was specifically needed for decisionsupport &asy access to the needed data %mproves system response time &nhances data integrity and security' 3


4/14

D// 2utput example# Comparative sales and3ro4ected 5evenue figures

Draw!ac+# $anagers couldn’t operate D//s

autonomously

&xecutive %nformation /ystems &%/# $oresimplified systems in which the manager

instantly knew what was happening

&mphasis is on graphical displays and easy*

to*use user interfaces 6inancials, production history, current

application status, plans, external events

competitor information, emails etc'

Being referred to as Business %ntelligence8 ' 4


5/14

3ros# &ase of use for upper*level executives 3rovides timely and efficient delivery %nformation can !e !etter understood

Cons# /ystem may !ecome slow and large )igh implementation costs "imited functionality

"ess relia!ility and security %nitial &%/s didn’t have analytical capa!ility of

D//s# both are used in conjunction &%/s are used to find pro!lems, while D//s

study them and offer alternative solutions' 5


6/14

ate (ssignment Display /ystem (D/

Developed !y exas %nstruments in 19:7for ;nited (irlines'

/ignificantly reduced travel delays !y aidingthe management of ground operations atvarious airports

ate (ssignment is complex# /ecurity, ateCapacity, some airplanes will fit at only some

gates etc'


7/14

Clinical decision support systems for medicaldiagnosis oogle for more details

$=C%.# Diagnoses of !acterial diseases

C(D;C&;/# Diagnoses 1000 diseases'

%liad# ;ses Bayesian reasoning to diagnose 1>00diseases

"ifecom# rac+s, processes and automatically

presents all relevant clinical considerations to the

physician, nurse practitioner, physician?s assistant,nurse, or medical assistant at the exact moment that

the +nowledge can do the most good

52D%(# 5elative 2ptical Density %mage (nalysis used

in medical imaging, medical diagnostics, orthopedic

and other medical disciplines' 7


8/14

"ac+ of a strong Data!ase component for most&%/s and D//s 2nline ransaction 3rocessing 2"3# the

technology that facilitates and managestransaction*oriented applications, e'g', ($s

Data!ase or Business transactions %nitial 2"3 Data!ases# he stored

organi@ational information was directed tomaintaining current i'e', online information

a!out individual transactions and customers $anagerial information reuires past as well as

future information Companies developed their own Data!ases, !ut

suffered from a lac+ of techniue and resources' 8


9/14

ypically used to capture new data or updateexisting data, specifically in high throughput,insertAupdate*intensive systems 2rder*entry, airline reservation, ($ etc

Characteristics# ransactions that involve small amounts of

data %ndexed and fast access to data $any users and 6reuent ueries

2"3 systems support transactions that spana networ+ and may include more than onecompany ransaction /ervers of $icrosoft and %B$

Data!ase uery optimi@ation techniues' 9


10/14

3ros# /implicity# 5educed paper trails

&fficiency# 6aster, more accurate forecasts

for revenues and expenses

Cons# /ecurity# 2nline transaction systems are

generally more suscepti!le to direct attac+

and a!use than their offline counterparts

5elia!ility# 2perations can !e severelyimpacted if the data!ase is unavaila!le due

to data corruption, systems failure, or

networ+ availa!ility issues

Cost# 2ffline maintenance is difficult 10


11/14

Developing and maintaining data!ases was!ecoming a pro!lem for each individual

enterprise

he Data!ases developed concentrated on

storing only online information 2nly analysis of the current situation was

availa!le to the managers

( wider management scope called for the

storage of past and future information he introduction of new software development

methodsAapplications called for shorter

decision support life cycles as compared to

&%/s' 11


12/14

1990’s# 2rgani@ations started developing datawarehouses to serve the decision support needs

Different from traditional systems ;se of special*purpose software that facilitates

the extraction, cleaning, and loading of data multi*dimensional data!ases, variety ofserver softwares etc'

&asy storage of past, present and future data

&nhanced data access tools facilitate theautonomous access, analysis and display ofdecision*support information e'g', withoutusing /"

ime*!ased decision support support when

reuired' 12


13/14

%nitial Data!ases were designed specifically for

operational transactional use !ased on 2"3

%n mid 19:0’s Data!ase developers reali@ed that

/toring historical data was important for themanagers

Complicated analysis*!ased ueries could

hang up large transactional data!ases, thus

slowing the response and decision*ma+ingtimes

2nline (nalytical 3rocessing 2"(3 data!ases

were developed specifically for analysis

2"(3 is at the heart of data warehouses' 13


14/14

(n approach to uic+ly answer multi*dimensional analytical ueries

$ultidimensional Data!ases uses a variation of

the relational model that exploit

multidimensional structures to organi@e data andexpress the relationships !etween data

6irst standard (3%# 2"& DB for 2"(3

%ntroduced the $D uery language

/econd (3%# $" for (nalysis $"( Business reporting for sales, mar+eting,

management reporting , !usiness process

management B3$, !udgeting and forecasting,

financial reporting' 14

data warehousing lecture 1

Documents