Distributed blood bank management system database

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In this project we are trying to implement a distributed database from a centralized database
of Blood Bank Management System. Typically, 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 (to reduce the risk of transfusion related adverse events). However, 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.
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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Distributed blood bank management system database

  1. 1. Distributed Blood Bank Management System Database An Overview 1
  2. 2. Prepared By: Saimunur Rahman Dept. Computer Science & Engineering International Islamic University Chittagong 2
  3. 3. Before I start my discussion Let us know What is Blood Bank? 3
  4. 4. 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. 4
  5. 5. 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. 5
  6. 6. Basic Project Overview  In 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. 6
  7. 7. Methodology I have incorporated several methodologies for creating this system, which is shown in next slide 7
  8. 8. Methodology (Cont.) 8 Existing System Review i.e. Centralized DB Data distribution based on horizontal fragmentation Getting Query Statistics Vertical Fragmentation based on Query statistics Figure: Developing Methodology
  9. 9. 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. 9
  10. 10. 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. 10
  11. 11. 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. 11
  12. 12. Existing Centralized Database ERD 12
  13. 13. Sample Tables with Dates 13 Figure: Branch Table
  14. 14. Sample Tables with Dates (Cont.) 14 Figure: Donor Table
  15. 15. Distribution among sites 15 Site 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
  16. 16. Distribution among sites (Cont.) 16 Chwakbazar Halishohor CEPZ New Market Agrabad Anderkilla Chandgaon Prabortok Circle Fig: Distributed Sites are connected with each Other where each of them has their own data
  17. 17. 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. 17
  18. 18. 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. 18
  19. 19. Data Distribution techniques (Cont.)  The result of previous shown query is given below:  By this way we have also selected our other table values. 19
  20. 20. Query Statistics Collection  We 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 20
  21. 21. Query Statistics Collection (Cont.)  Branch ◦ Find branch_name,address phone via sub_area  Blood_Request ◦ Find Name,address,hospital,blood_group,blood_a mount,phone via branch_id ◦ Find Name,hospital,blood_group,blood_amount,d elivery_confirmation via branch_id ◦ Etc. 21
  22. 22. 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 22
  23. 23. 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. 23
  24. 24. 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. 24
  25. 25. Vertical Fragmentation based on Query statistics  We have fragmented the database vertically based on the query written above.  Fragments of Donor ◦ Donor_fragment1(d_id,d_name,address,blood_g roup,phone) ◦ Donor_fragment1(d_id,br_id,area,sub_area,natio nal_id,email)  Fragments of Blood_request ◦ Bloodrequest_frg1(name,address,hospital,blood_ gruop,blood_amount,phone) ◦ Bloodrequest_frg2(id,name,delivery_confirmation ,email) 25
  26. 26. Vertical Fragmentation based on Query statistics (Cont.)  Fragments of Employee ◦ Employee_frag_1(emp_id,emp_name,em p_address,emp_role,emp_sal,phone,emai l) ◦ Employee_frag_2(emp_id,branch_id,emp _area) 26
  27. 27. 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. 27
  28. 28. 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 28
  29. 29. Query Hit & Query Miss Statistics (Cont.) 29 0 20 40 60 80 100 120 Query Hit Query Miss Figure: Query hit & Query miss statistics
  30. 30. Minimum Response Time at Centralized Database & Distributed Database  Minimum 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 30
  31. 31. Minimum Response Time at Centralized Database & Distributed Database (Cont.) 31 0 5 10 15 20 25 30 Centralized Distributed Figure: Minimum Response Time at Centralized Database & Distributed Database
  32. 32. 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. 32
  33. 33. Maximum Response Time at Centralized Database & Distributed Database (Cont.) 33 0 5 10 15 20 25 30 Centralized Distributed Figure: Maximum Response Time at Centralized Database & Distributed Database
  34. 34. 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. 34
  35. 35. Average Response Time at Centralized Database & Distributed Database 35 0 5 10 15 20 25 30 Centralized Distributed Figure: Average Response Time at Centralized Database & Distributed Database
  36. 36. Conclusion  The 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. 36
  37. 37. 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. 37
  38. 38. 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). 38
  39. 39. 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. 39
  40. 40. 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. 40
  41. 41. Any Question?? Fell free to ask me I would love to ans. Them. 41
  42. 42. Thank You All 42

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