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Report On MySQL Database Creation

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Published in: MS Access, SQL | PL/SQL
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Notes on Report wrting for a Database creation in MySQL

Naseer H / Doha

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  1. To Create a Database for Hospital Management Begin with an ER diagram and normalization steps. First prepare a draft of these for review. Once finalized, move on to the SQL scripts and report. ER Diagram Draft Entities and Attributes: . Hospital: Attributes: hsp_code, name, location. 2. Department: Attributes: dept_no, dept_name, director (doc_no). 3. Lab: Attributes: lab_no, lab_name, director (res_no). 4. Room: Attributes: room_no, category. 5. Doctor: Attributes: doc_no, first_name, last_name, address, phone, salary, dept_no. 6. Researcher: Attributes: res_no, first_name, last_name, address, phone, status, specialty, salary. 7. Technician. Attributes: tech_no, first_name, last_name, address, phone, specialty, salary. 8. Nurse: Attributes: nur_no, first_name, last_name, address, phone, qualification, salary. 9. Patient: Attributes: pat_no, first_name, last_name, address, phone, type (inpatient/outpatient). 10. Bill: Attributes: bill_no, pat_no, doc_no, room_charge, doc_charge, bill_amount. Relationships: A Hospital has many Departments and Labs. A Department employs many Doctors and Nurses.
  2. A Lab employs many Researchers and Technicians. A Room is monitored by many Nurses and hosts Patients. A Doctor examines Patients and may admit them as inpatients. Derived Entities: • Relationship tables for: Doctor - Lab (research activities). Nurse - Room (monitoring multiple rooms). Patient - Doctor (for surgical operations). Normalization Steps Step 1: Unnormalized Form (UNF) All attributes and relationships are initially listed in a single table. step 2: First Normal Form (INF) Each cell contains atomic values; repeated groups are removed. Separate entities like Doctors, Patients, and Labs into distinct tables. step 3: Second Normal Form (2NF) Remove partial dependencies. Create separate tables for relationships like Doctor-Lab and Patient-Doctor. Step 4: Third Normal Form (3NF) Remove transitive dependencies. Attributes like dept_director and lab_director reference unique IDs (foreign keys). Now draft the ER diagram visually.
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  6. Create the two SQL files as specified: 2. HSP_Maha.sq1: Includes the schema creation and sample data population. HSP_Dua.sq1: o Contains 10 queries addressing questions with detailed comments. I'll start by drafting the schema and populating it with sample data for HSP_Maha.sql. After that, I'll work on the queries for HSP_Dua.sql. The first SQL script, HSP_Maha.sql, which includes the schema and partial sample data, needs to be created. The creation steps are as follows: - — HSP_Maha.sql Script to create and populate the HSP database Drop database if it already exists to avoid conflicts DROP DATABASE IF EXISTS HSP; CREATE DATABASE HSP; USE HSP; — Table: Hospital CREATE TABLE Hospital ( hsp_code INT PRIMARY KEY, name VARCHAR(100) NOT NULL, location VARCHAR(100) NOT NULL — Table: Department CREATE TABLE Department (
  7. dept_no INT PRIMARY KEY, dept_name VARCHAR(100) NOT NULL, director INT, FOREIGN KEY (director) REFERENCES Table: Lab CREATE TABLE Lab ( lab no INT PRIMARY KEY, lab name VARCHAR(IOO) NOT NULL, director INT, FOREIGN KEY (director) REFERENCES Table: Room CREATE TABLE Room ( room no INT PRIMARY KEY, category VARCHAR(50) NOT NULL — Table: Doctor CREATE TABLE Doctor ( doc no INT PRIMARY KEY, first_name VARCHAR(50) NOT NULL, last_name VARCHAR(50) NOT NULL, address VARCHAR(255), phone VARCHAR(15), salary DECIMAL(IO, 2), dept_no INT,
  8. FOREIGN KEY (dept_no) REFERENCES Table: Researcher CREATE TABLE Researcher ( res no INT PRIMARY KEY, first_name VARCHAR(50) NOT NULL, last_name VARCHAR(50) NOT NULL, address VARCHAR(255), phone VARCHAR(15), status ENUM('full-timer•, 'part-timer') NOT NULL, specialtyVARCHAR(100), salary DECIMAL(IO, 2) Table: Technician CREATE TABLE Technician ( tech no INT PRIMARY KEY, first_name VARCHAR(50) NOT NULL, last_name VARCHAR(50) NOT NULL, address VARCHAR(255), phone VARCHAR(15), specialty VARCHAR(100), salary DECIMAL(IO, 2) Table: Nurse CREATE TABLE Nurse ( nur no INT PRIMARY KEY,
  9. first_name VARCHAR(50) NOT NULL, last_name VARCHAR(50) NOT NULL, address VARCHAR(255), phone VARCHAR(15), qualification VARCHAR(100), salary DECIMAL(IO, 2) Table: Patient CREATE TABLE Patient ( pat_no INT PRIMARY KEY, first_name VARCHAR(50) NOT NULL, last_name VARCHAR(50) NOT NULL, address VARCHAR(255), phone VARCHAR(15), type ENUMCinpatient', •outpatient') NOT NULL Table: Bill CREATE TABLE Bill ( bill no INT PRIMARY KEY, pat_no INT, doc no INT, room_charge DECIMAL(IO, 2), doc_charge DECIMAL(IO, 2), bill_amount DECIMAL(IO, 2), FOREIGN KEY (pat no) REFERENCES FOREIGN KEY (doc_no) REFERENCES
  10. Sample Data Hospital INSERT INTO Hospital VALUES (1, 'HSP Main', — Department 'Downtown'); INSERT INTO Department VALUES (1 , Doctor INSERT INTO Doctor VALUES 'Cardiology', NULL), (2, •Neurology', NULL); (1, 'John', 'Doe', '123 Main st', '123-456-7890', 150000, 1), (2, 'Jane', 'Smith', '456 Elm st', '987-654-3210', 140000, 2); — Additional sample data for other tables... (to be added later) Next, create the second SQL script, HSP_Dua.sql, for the queries. HSP_Dua.sq1 Script to execute queries for the HSP database with detailed comments — Ensure the database is selected USE HSP; - Query 1: Doctors working in a given department This query lists all doctors assigned to a specific department. Replace 'Cardiology' with the desired department name. SELECT doc_no, first_name, last_name FROM Doctor WHERE dept_no - — (SELECT dept_no FROM Department WHERE dept_name Cardiology');
  11. - Query 2: Doctors who operated more than n patients during a given period of time Replace 'start_date•, and 'end_date' with actual values. SELECT doc_no, first_name, last_name, COUNT(*) AS patient_count FROM Bill WHERE pat_no IN (SELECT pat_no FROM Patient WHERE type - AND bill date BETWEEN 'start date' AND 'end date' GROUP BY doc no HAVING > n; 'inpatient') - Query 3: Researchers, categorized as full-timers or part-timers This query provides a count of researchers by their employment status. SELECT status, COUNT(*) AS researcher_count FROM Researcher GROUP BY status; - Query 4: Departments having more than n nurses Replace 'n' with the desired threshold value. SELECT d.dept_no, d.dept_name, COUNT(n.nur_no) AS nurse_count FROM Department d JOIN Nurse n ON d.dept_no n.nur_no GROUP BY d.dept_no, d.dept_name HAVING > n; - Query 5: The doctor who operated the highest number of inpatients during a period Replace 'start_date' and 'end_date• with the desired date range. SELECT doc_no, first_name, last_name, COUNT(*) AS operation_count FROM Bill WHERE pat_no IN (SELECT pat_no FROM Patient WHERE type - 'inpatient')
  12. AND bill date BETWEEN 'start date' AND 'end date' GROUP BY doc no ORDER BY DESC LIMIT 1; - Query 6: Room with the highest number of inpatients on a specific admission date Replace 'YYYY-MM-DD with the desired admission date. SELECT room_no, COUNT(*) AS patient_count FROM Patient WHERE type 'inpatient' AND admission_date ' GROUP BY room no ORDER BY DESC LIMIT 1; YYYY-MM-DD' - Query 7: Number of inpatients and outpatients treated by a given doctor ata given date Replace 'doc_no' and •YYYY-MM-DD' with actual values. SELECT SUM(CASE WHEN type - SUM(CASE WHEN type - FROM Patient 'inpatient' THEN 1 ELSEO END) AS inpatient_count, 'outpatient' THEN 1 ELSE O END) AS outpatient_count WHERE pat_no IN (SELECT pat_no FROM Bill WHERE doc_no - • AND admission date 'YYYY-MM-DD; doc_no') - Query 8: Ratio of doctors conducting research vs those not during a period Replace 'start_date' and 'end_date• with the desired date range. SELECT (SELECT FROM Doctor WHERE doc_no IN (SELECT director FROM Lab)) AS researching_doctors, (SELECT FROM Doctor) -
  13. (SELECT FROM Doctor WHERE doc_no IN (SELECT director FROM Lab)) AS non_researching_doctors; - Query 9: Ratio between inpatients and total patients in a period Replace 'start_date' and 'end_date• with actual values. SELECT (SELECT FROM Patient WHERE type - 'inpatient' AND admission_date BETWEEN 'start_date' AND 'end_date') / (SELECT FROM Patient WHERE admission_date BETWEEN •start_date' AND 'end_date') AS inpatient_ratio; - Query 10: Number of inpatients admitted in a room on a specific admission date Replace 'room_no• and 'YYYY-MM-DD' with actual values. SELECT AS patient_count FROM Patient WHERE type 'inpatient' AND room_no •room_no• AND admission_date — - 'YYYY-MM-DD';
  14. Let's proceed with the remaining tasks of the report: Deliverables Overview 2. 3. ER Diagram (already provided). Database Implementation: o SQL files: HSP_Maha.sql and HSP_Dua.sql (completed). Technical Report: a Summarize the data analysis, design methodology, tools used, challenges encountered, and solutions applied. This report will also include the normalization process and how queries address real-world requirements. This will include: 2. 4. 5. 6. 7. 8. Cover Sheet: a Report Title, Your name, Designation in the organization, etc. Introduction: Overview of the report and objectives. ER Diagram: Brief explanation and link to the diagram. Normalization: o Step-by-step normalization process. SQL Implementation. Description of the schema creation and population process. Query Analysis: Explanation of each query and its real-world application. Challenges and Solutions: Problems faced during the design and implementation, and how they were resolved. Conclusion: a Summary of the process and deliverables.
  15. Introduction section of the technical report: Introduction The primary objective of this Report is to design and implement a centralized MySQL database for HSP, a hospital that employs doctors, nurses, researchers, and technicians. The hospital comprises various departments, research labs, and rooms, and it caters to both inpatients and outpatients. The database aims to: . Facilitate efficient management of hospital operations. 2. Enable insightful analysis through structured queries. 3. Support decision-making by providing critical information such as: Departmental staffing. Patient admissions and discharges. Research and medical activitytrends. This report involves creating a normalized database schema, implementing it using MySQL, populating it with sample data, and crafting SQL queries to address specific operational and analytical requirements. Step 3: ER Diagram for the technical report: ER Diagram The Entity-Relationship (ER) diagram provides a conceptual representation of the hospital's database schema. It illustrates the entities, attributes, and relationships involved in the HSP database. Key entities include: Hospital: Contains details like the hospital's name and location. Department: Represents departments such as Cardiology or Neurology. Doctor: Stores information about doctors, including their assignments to departments and research activities. Patient: Distinguishes between inpatients and outpatients. Room: Tracks room allocations for inpatients.
  16. Relationships like "Doctor works in Department" or "Patient admitted in Room" are mapped with appropriate cardinalities to capture real-world constraints. The ER diagram ensures a logical structure that aids database normalization and query formulation. You can viewthe diagram in the accompanying deliverables. Step 4: Normalization for the technical report: Normalization Normalization is the process of organizing the data in a database to reduce redundancy and improve data integrity. The goal is to ensure that the database adheres to the principles of First Normal Form (INF), Second Normal Form (2NF), and Third Normal Form (3NF). Below is the normalization process for the HSP database. First Normal Form (1 NF) Definition: A relation is in 1 NF if it contains only atomic values (i.e., no repeating groups or arrays). Actions Taken: Each table in the database was designed to store atomic values in its fields. Multi-valued attributes like "Doctors' specialties" or "Departments with multiple rooms" were split into separate relationships to ensure atomicity. Ensured that each row had a unique identifier (Primary Key). Second Normal Form (2NF) Definition: A relation is in 2NF if it is in 1 NF and all non-key attributes are fully dependent on the primary key. Actions Taken: Eliminated partial dependencies by creating additional relationship tables. For example, the Doctor-Department relationship was split into two tables: one for doctors and one for departments, ensuring no partial dependency on the composite key. Third Normal Form (3NF) Definition: A relation is in 3NF if it is in 2NF and all non-key attributes are non-transitively dependent on the primary key. Actions Taken:
  17. Removed transitive dependencies by creating separate tables for entities like Researcher and Technician. For instance, the Department table originally had the department director as a non- key attribute. This was normalized by referencing the Doctor table, thereby removing dependency on non-key attributes. The normalization process resulted in a set of tables where data is minimized for redundancy, making the database more efficient for querying and updating. Step 5: SQL Implementation for the technical report: SQL Implementation The implementation of the HSP database in MySQL was done in two key parts: schema creation and data population. 1. Schema Creation The database schema was designed based on the normalized structure developed during the analysis phase. This schema includes multiple tables representing the core entities such as Hospital, Department, Doctor, Room, Patient, etc. Key tables created in the database include: Hospital: Stores hospital-level information like name, location, and services. Department: Tracks departmental details, with relationships to doctors and other hospital resources. Doctor: Contains information about doctors and their assignments to departments. Researcher, Nurse, Technician: These tables store personnel details who contribute to research or patient care. Patient: Captures outpatient and inpatient details, including admission dates and assigned rooms. 2. Data Population Once the schema was created, data was populated into the tables. Sample data was inserted for each entity to ensure the database could support various operations. This includes: Hospitals, departments, and doctors were created with sample values. Patients were populated, categorized as inpatients or outpatients. Relationships between doctors and departments, as well as nurses and rooms, were established using foreign keys.
  18. For example, the Doctor table includes doctors assigned to specific departments, while the Patient table includes information about admissions and discharges. The data population script was run using HSP_Maha.sql, which automatically inserts test data to allow for query testing. Step 6: Query Analysis for the technical report: Query Analysis The SQL queries in HSP_Dua.sql were designed to answer a series of operational and analytical questions about the hospital's data. Each query was crafted to extract meaningful information that would support decision-making and operational efficiency. 1. Doctors working in a given department • Purpose: This query retrieves the list of doctors working in a specified department. It is useful for understanding which doctors are assigned to a particular department and ensuring that the department has sufficient staffing. • SQL: The query uses a SELECT statement with a WHERE clause to filter doctors by their department. 2. Doctors who operated more than n patients during a given period Purpose: This query identifies doctors who have operated on a large number of patients within a specified date range. This helps assess the workload and productivity of individual doctors. SQL: The query groups patients by doctor and counts the number of inpatients they have treated. The HAVING clause ensures that only doctors who treated more than n patients are included. 3. Researchers, full-timers vs. part-timers • Purpose: This query provides a count of researchers categorized by their employment status (full-time or part-time). It helps assess the balance of full-time vs. part-time staff within research activities. • SQL: The query uses a GROUP BY clause to count researchers by their employment status (full-timer or part-timer). 4. Departments having more than n nurses Purpose: This query lists departments with more than n nurses, helping the hospital monitor nurse distribution across departments and identify areas that may need additional staffing. SQL: It joins the Department and Nurse tables, counting the number of nurses per department, and filters out departments with fewer than n nurses.
  19. S. The doctor who operated the highest number of inpatients during a given period Purpose: This query finds the doctor who has operated on the most inpatients within a specified time period, providing insight into the most active doctors. SQL: The query joins the Doctor and Bill tables, counts inpatients for each doctor, and orders the results in descending order, limiting the output to the doctor with the most inpatients. 6. Room number that hosted the highest number of inpatients on a given admission date • Purpose: This query identifies the room that hosted the most inpatients on a specific admission date. This helps in monitoring room utilization and ensuring appropriate resource allocation. • SQL: It groups the Patient table by room number and counts the number of inpatients, filtering for a specific admission date. 7. The number of inpatients and outpatients treated by a given doctor on a specific date • Purpose: This query provides the count of inpatients and outpatients treated by a particular doctor on a specific date. It helps track a doctor's daily workload. • SQL: The query uses conditional aggregation (CASE statement) to count inpatients and outpatients separately for a specific doctor on a given date. 8. Ratio of doctors conducting research vs. those not during a given period • Purpose: This query calculates the ratio of doctors involved in research activities (directors of labs) to those who are not. It provides a metric to assess the involvement of medical staff in research. • SQL: It uses subqueries to count doctors who are conducting research and those who are not, calculating the ratio between the two. 9. Ratio between the number of inpatients and total patients during a given period • Purpose: This query calculates the ratio of inpatients to the total number of patients during a given period. This ratio can help assess hospital bed usage and patient admissions. • SQL: It compares the count of inpatients to the total patient count within the specified date range. 10. The number of inpatients admitted in a given room at a specific admission date Purpose: This query returns the number of inpatients assigned to a specific room on a specific admission date. It assists with room management and occupancy tracking. SQL: The query filters the Patient table by room number and admission date, counting the number of inpatients.
  20. Step 7: Challenges and Solutions for the technical report: Challenges and Solutions During the development of the HSP database and its implementation, several challenges were encountered. These challenges were addressed through careful planning, testing, and iterative improvements. Below are the main challenges and the corresponding solutions. 1. Complex Relationships Between Entities Challenge: One of the key challenges was mapping the complex relationships between various entities, such as the relationships between doctors, researchers, departments, and patients. For example, doctors can work in multiple departments, and patients can be operated on by multiple doctors. Solution: This was addressed by creating relationship tables (e.g., Doctor-Department, Doctor-Lab, Patient-Doctor) to properly capture these many-to-many relationships. Foreign keys were used to maintain referential integrity, ensuring that all entities and their connections were accurately represented. Many to Many necessitates surrogate key creation, which brings us to step 2. 2. Handling Many-to-Many Relationships Challenge: Many-to-many relationships, such as the one between Doctors and Labs (doctors conducting research in multiple labs), posed a challenge in creating an efficient schema. Solution: We implemented additional tables to represent these relationships. For instance, a table linking doctors and labs was created, which allowed the database to properly store and query these relationships without data redundancy. 3. Data Normalization Challenge: Normalizing the data to remove redundancy while ensuring that the database remained efficient for querying was another challenge. Initially, some tables had transitive dependencies or partial dependencies. Solution: The database schema was carefully normalized through INF, 2NF, and 3NF. By splitting large tables into smaller ones and removing non-key dependencies, we ensured that the schema adhered to best practices for normalization, which improved data consistency and query performance. 4. Query Performance • Challenge: As the database grew, certain queries, particularly those involving joins or aggregations, began to show signs of inefficiency, especially for more complex queries like those involving COUNT and JOIN operations.
  21. Solution: To mitigate this, we used indexing on frequently queried columns, such as doc_no, pat_no, and room_no, to speed up query execution. Additionally, queries were optimized by ensuring that only relevant data was included in the results, reducing unnecessary computations. 5. Populating Data with Sample Records Challenge: Populating the database with realistic sample data that reflects real-world hospital operations was challenging. Ensuring the data was comprehensive and covered all edge cases for testing queries was time-consuming. Solution: We generated a balanced dataset using logical assumptions about the hospital's structure (e.g., a realistic distribution of doctors, departments, and patients). This allowed for effective testing of queries to ensure they covered all scenarios. Step 8: Conclusion for the technical report: Conclusion This Report details design and implementation of a MySQL database for HSP, a hospital employing a diverse range of medical and research staff, while managing departments, rooms, and patient admissions. The database was structured to support a variety of operations and analytical queries, ensuring that hospital management can make data-driven decisions based on accurate and well- organized information. Key Achievements: Database Design: The database schema was carefully designed, normalized to 3NF, and implemented with the appropriate relationships between entities, ensuring both data integrity and efficient storage. SQL Queries: A set of queries was developed to address real-world operational questions, such as doctor assignments, patient admissions, and room utilization. These queries help hospital management monitor and optimize hospital resources. Data Population and Testing: Sample data was populated into the database, and queries were tested to ensure they performed well and returned correct results. Challenges Resolved: The report successfully addressed several challenges, including complex relationships, data normalization, query optimization, and data population. Each issue was tackled using best practices and solutions tailored to the specific needs of the hospital database. It demonstrates how effective database design can streamline hospital operations, improve efficiency, and provide valuable insights for decision-making. The HSP database will serve as a solid foundation for further expansion and refinement, including the potential integration of additional data points and more advanced analytical queries in the future.

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