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Distributed Blood Bank Management System DatabaseAn Overview

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Prepared By:

Saimunur RahmanDept. Computer Science & Engineering

International Islamic University Chittagong

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Before I start my discussionLet us know

What is Blood Bank?

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What is Blood Bank?A blood bank is a cache or bank

of blood or blood components, gathered as a result of blood donation or collection, stored and preserved for later use in blood transfusion.

The term "blood bank" typically refers to a division of a hospital where the storage of blood product occurs and where proper testing is performed.

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What is Blood Bank? (Cont.)It sometimes refers to a

collection center, and indeed some hospitals also perform collection.

The Blood Bank Management System has been created with a purpose of replacing all of paperwork done at the Blood Bank.

All aspects of blood banking is completely managed by the software.

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Basic Project OverviewIn this project we are trying to

implement a distributed database from a centralized database of Blood Bank Management System.

Here, we have designed a distributed database system for Blood Bank Management from a centralized database system.

Which will increase the system performance, reliability and throughput.

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Methodology

I have incorporated several methodologies for creating this system, which is shown in next

slide

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Methodology (Cont.)Existi

ng Syste

m Review i.e. Centralized

DB

Data distribution based

on horizontal

fragmentati

on

Getting Query

Statistics

Vertical

Fragmentati

on based

on Query statisti

csFigure: Developing Methodology

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Methodology (Cont.)Existing System Review: First

we reviewed the existing centralized database for Blood Bank Management System that was also created by us.

Data distribution based on horizontal fragmentation: We have distributed our data’s into several sites which is the main criteria of distributed database system.

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Methodology (Cont.)Getting Query Statistics: After

data distribution into sites we are then getting the query statistics and based upon that we go for vertical fragmentation.

Vertical Fragmentation based on Query statistics: Then we fragment our site database vertically based on site query statistics.

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Methodology (Cont.)Vertical Fragmentation based

on Query statistics (Cont.): Vertical fragmentation is actually for several site applications which used to access the data form database system.

Here, are also calculating query hit and miss for a general query generated from a site.

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Existing Centralized Database ERD

Branch Donor

id

b_id

b_name

address

sub_area area

phone

emailid

d_id d_name

address

sub_area

area

blood_ group

national_id

phone

email

Blood

idblood_group

blood_amount

Employee

id

emp_id

emp_name

emp_role

emp_salary

emp_areaemp_address

phone

emailBlood_request

id

name

address

hospital

area

blood_ group

blood_amount

phone

email

Has

Has

Has

1 M

1

M

1

M

paid_amount

Gives

donor_id

b_id

b_id

M

1

b_id

delivery_confirmation

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Sample Tables with Dates

Figure: Branch Table

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Sample Tables with Dates (Cont.)

Figure: Donor Table

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Distribution among sitesSite Name Site area

Blood Bank Chwakbazar Chwakbazar

Blood Bank CMCH Prabortok Circle

Blood Bank Bahaddarhat Chandgaon

Blood Bank Anderkilla Anderkilla

Blood Bank Agrabad Agrabad

Blood Bank New Market New Market

Blood Bank CEPZ CEPZ

Blood Bank Halishohor Halishohor

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Distribution among sites (Cont.)

ChwakbazarHalisho

hor

CEPZ

New MarketAgraba

d

Anderkilla

Chandgaon

Prabortok Circle

Fig: Distributed Sites are connected with each Other where each of them has their own data

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Data Distribution techniques

The data was distributed among the sites based on horizontal fragmentation technique.

In SQL we are using SELECT operation for horizontal fragmentation of data. We did also same things here.

We have used some simple predicates for fragmentation with SELECT operation for fragmentation.

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Data Distribution techniques (Cont.)SELECT * FROM distributed_blood_bank.donor

where sub_area='Chwakbazar’

which selects all the table values within that sub_area.

We just put that values into site located at Chwakbazar.

We will use separate sub_area values for different sites and after that we will put them into their related sites.

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Data Distribution techniques (Cont.)The result of previous shown

query is given below:

By this way we have also selected our other table values.

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Query Statistics CollectionWe have assumed some queries. Based

upon that we’ll make decision for vertical fragmentation.

Suppose we have some applications in our site which generate quires in following areas:

Donor◦Find d_name,blood_group,phone via

sub_area◦Update donor details◦Etc

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Query Statistics Collection (Cont.)Branch

◦Find branch_name,address phone via sub_area

Blood_Request◦Find

Name,address,hospital,blood_group,blood_amount,phone via branch_id

◦Find Name,hospital,blood_group,blood_amount,delivery_confirmation via branch_id

◦Etc.

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Query Statistics Collection (Cont.)Blood

◦Find donor_id,blood_amount via branch_id

◦Update donor_id,blood_amount via branch_id

◦Find Total_blood_amount_in_branch via branch_id

◦Find donor_id,paid_amount,blood_amount via branch_id

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Query Statistics Collection (Cont.)Employee

◦ Find emp_id,emp_name,emp_address_emp_role,emp_sal,phone,email via branch_id

◦ Find emp_id,emp_name, emp_role,emp_sal,phone via branch_id

◦ Find emp_id,emp_name, emp_role,emp_sal via branch_id

◦ Find emp_id, emp_sal, via branch_id◦ Update

emp_id,emp_name,emp_address_emp_role,emp_sal,phone via branch_id

◦ Etc.

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Query Statistics Collection (Cont.)These are our assumed quires

that can be generated from any of the 8 sites because all the sites have the same table which is necessary for every site operation.

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Vertical Fragmentation based on Query statisticsWe have fragmented the database

vertically based on the query written above.Fragments of Donor

◦ Donor_fragment1(d_id,d_name,address,blood_group,phone)

◦ Donor_fragment1(d_id,br_id,area,sub_area,national_id,email) 

Fragments of Blood_request◦ Bloodrequest_frg1(name,address,hospital,blood_

gruop,blood_amount,phone)◦ Bloodrequest_frg2(id,name,delivery_confirmatio

n,email)

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Vertical Fragmentation based on Query statistics (Cont.)Fragments of Employee

◦Employee_frag_1(emp_id,emp_name,emp_address,emp_role,emp_sal,phone,email)

◦Employee_frag_2(emp_id,branch_id,emp_area)

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Testing Query Response Time Using Centralized and Distributed Model

All models were developed by using My-SQL web-based version integrated on Xampp 1.7.7 server.

Everything were tested by using My-SQL server version.

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Query Hit & Query Miss Statistics

Sometimes it is necessary to view the blood availability around the area.

At that time only query miss will be occurred that means global query will be generated.

This possibility of query hit & query miss is shown in a chart in next slide

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Query Hit & Query Miss Statistics (Cont.)

Bloo

d

Bloo

d Req

uest

Donor

Empl

oyee

Bran

ch0

20

40

60

80

100

120

Query HitQuery Miss

Figure: Query hit & Query miss statistics

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Minimum Response Time at Centralized Database & Distributed DatabaseMinimum Response Time is

defined as the minimum time required for responding to a query in the Centralized Database or in the local site in case of distributed database.

Minimum Response time of centralized is more than the distributed database here

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Minimum Response Time at Centralized Database & Distributed Database (Cont.)

Bloo

d

Bloo

d_Req

uest

Bran

ch

Donor

Empl

oyee

0

5

10

15

20

25

30

CentralizedDistributed

Figure: Minimum Response Time at Centralized Database & Distributed Database

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Maximum Response Time at Centralized Database & Distributed Database Maximum Response Time is defined

as the maximum time required for responding to a query in the Centralized Database

Or in the local site in case of distributed database (when data is not reside in local site).

Maximum Response time of centralized is slightly more than the distributed database here.

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Maximum Response Time at Centralized Database & Distributed Database (Cont.)

Bloo

d

Bloo

d_Req

uest

Bran

ch

Donor

Empl

oyee

0

5

10

15

20

25

30

CentralizedDistributed

Figure: Maximum Response Time at Centralized Database & Distributed Database

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Average Response Time at Centralized Database & Distributed Database Average Response Time is

defined as the average time required for responding to a query in the Centralized Database or in the local site in case of distributed.

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Average Response Time at Centralized Database & Distributed Database

Bloo

d

Bloo

d_Req

uest

Bran

ch

Donor

Empl

oyee

0

5

10

15

20

25

30

CentralizedDistributed

Figure: Average Response Time at Centralized Database &

Distributed Database

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ConclusionThe purpose of conducting this

study & doing project is to know the conversion of Centralized DB to Distributed DB

And its impact on the response time while moving from centralized to distributed databases.

Distributed databases have many aspects and every organization has certain preferences.

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Conclusion (Cont.)For this sector, the response time

is prioritized.Our experiment showed that the

average response time is decreased if we switch from centralized database to distributed database.

In distribution we put the data to the site where it is used most frequently.

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Conclusion (Cont.)This locality of data reduces the

response time. In the distributed database, data

is fragmented. These fragments are short compared to the full database (centralized database contains maximum columns).

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Conclusion (Cont.)However, when we need data from

multiple sites for a query (report queries), the response time is increased.

Accessing data from multiple remote sites and then joining those takes long time.

But in the centralized database since data is at one place so, it is easy and fast to search it.

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Conclusion (Cont.)Experiment results showed that

the response time is decreased in distributed databases.

Due to fragmentation data set for single site contains less records than centralized database, so response time is less.

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Any Question??Fell free to ask me

I would love to ans. Them.

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Thank You All