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Blood bank in network
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Bloodbank

  1. 1. Distributed Blood Bank Management System Database
  2. 2. Before I start my discussion Let us know What is Blood Bank? 2
  3. 3. 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. 3
  4. 4. 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. 4
  5. 5. Methodology I have incorporated several methodologies for creating this system, which is shown in next slide 5
  6. 6. Methodology (Cont.) 6 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
  7. 7. Existing Centralized Database ERD 7
  8. 8. Sample Tables with Dates 8 Figure: Branch Table
  9. 9. Sample Tables with Dates (Cont.) 9 Figure: Donor Table
  10. 10. Distribution among sites 10 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
  11. 11. Distribution among sites (Cont.) 11 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
  12. 12. Data Distribution techniques (Cont.) • The result of previous shown query is given below: • By this way we have also selected our other table values. 12
  13. 13. 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 13
  14. 14. 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_group,pho ne) – Donor_fragment1(d_id,br_id,area,sub_area,national_id,e mail) • Fragments of Blood_request – Bloodrequest_frg1(name,address,hospital,blood_gruop,bl ood_amount,phone) – Bloodrequest_frg2(id,name,delivery_confirmation,email) 14
  15. 15. Average Response Time at Centralized Database & Distributed Database 15 0 5 10 15 20 25 30 Centralized Distributed Figure: Average Response Time at Centralized Database & Distributed Database
  16. 16. System basic with graphical view • Required Tools Here we used Microsoft office Access to create the database and Microsoft Visual Studio .NET to design the interfaces.
  17. 17. Overview of Graphical Presentation • Donor Details • Blood Collection • Screening • Component preparation • Blood storage • Blood request • Compatibility • Blood issue • Monthly statistics
  18. 18. Compatibility Testing  Before a transfusion, compatibility testing between donor’s and patient’s blood must be done. Firstly they perform testing to determine the blood type of patients and perform cross matching test to identify compatible blood components for blood transfusions.
  19. 19. Compatibility testing interface
  20. 20. Blood issued  They issue the components to the patient according the compatibility report.  To other hospitals they issue RBC only.
  21. 21. Blood issue interface
  22. 22. Monthly Statistics Analysis  The following Statistical Analysis are maintained by the Hospital Blood Bank. a. Blood Collection b. Blood Collection Of Summary c. Collection Of Blood Group d. Analysis Of RCC
  23. 23. Monthly statistics analysis interface
  24. 24. Conclusion  As the donor’s data is saved in the system we can reject those who have HIV or any other infections.  As we have the contact number of the donor we can contact donor whenever necessary comes.  Since we have the expiry date of the packs we can have a proper storage management system.

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