Merge pull request #436 from kavicastelo/docs

add release notes

Docs/release notes v-1-0-0.md

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+# Release Notes v1.0.0
+## Overview
+We are excited to announce the initial release of our Air Quality Monitoring Application, version 1.0.0. This release lays the foundation for our platform, focusing on Spring Boot backend functionality and basic CRUD operations for the following entities:
+- Air Quality
+- Land Use
+- Meteorological
+
+## Key Features
+### Backend Structure
+The backend is built on the Spring Boot framework (version 3.2.0), utilizing Java 17. The MongoDB database is employed to store and manage data efficiently.
+
+### File Structure
+The application follows a modular file structure for enhanced organization:
+```text
+com.api.air_quality
+    - controller
+        - airQualityController
+        - landUseController
+        - metrologicalController
+    - model
+        - airQualityModel
+        - landUseModel
+        - metrologicalModel
+    - repository
+        - airQualityRepository
+        - landUseRepository
+        - metrologicalRepository
+    - service
+        - airQualityService
+        - landUseService
+        - metrologicalService
+    - dto
+        - ApiResponse
+    - AirQualityApplication
+    - CorsConfig
+    - MongoConfig
+```
+### CRUD Operations
+In this version, basic CRUD operations are implemented for Air Quality, Land Use, and Meteorological entities. Repository methods are utilized to handle operations, and service files remain unimplemented.
+
+### Technology Stack
+The application is built using the following technologies:
+- **Spring Boot**: version 3.2.0
+- **Java**: Version 17
+- **Database**: MongoDB
+
+### Configuration
+The application has disabled Spring Security, and route configurations are managed using the CorsConfig file. Responses are formatted into JSON using the ApiResponse class.
+
+### Next Steps
+While v1.0.0 provides a solid foundation for our platform, upcoming releases will focus on implementing service logic, adding authentication, and expanding CRUD functionalities. We appreciate your support and look forward to delivering more features in future releases.
+
+Thank you for being part of our journey!
+
+[- Kavindu Kokila(Fullstack developer)](mailto:[email protected])

Docs/release notes v-1-1-0.md

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+# Release Notes v1.1.0
+### Overview
+We are thrilled to introduce version 1.2.0 of our Air Quality Monitoring Application. This release focuses on implementing essential statistical analysis methods and enhancing data management capabilities.
+
+### New Features
+#### Statistical Analysis
+- **Get All Data by Date Range:** Introduces a method to retrieve data within a specified date range.
+- **Get Means for Each Factor by Date Range:** Adds a method to calculate means for each factor within a given date range.
+- **Get Median for Each Factor:** Implements a method to calculate the median for each factor.
+- **Get Mode for Each Factor:** Introduces a method to find the mode for each factor.
+- **Find Correlations Between Factors:** Adds a method to determine correlations between each pair of factors.
+
+#### Bulk Data Import
+- **Bulk Import Data from CSV File:** Implements a method to bulk import data from a CSV file into the database.
+
+#### New Dependencies
+- **opencsv:** Upgraded to version 5.7.1 for enhanced CSV processing.
+- **commons-math3:** Introduced version 3.6.1 to leverage advanced mathematical functions.
+
+#### DTO Enhancements
+- **CorrelationDTO:** A new DTO (Data Transfer Object) specifically for converting correlation values to double values.
+
+### Next Steps
+This release lays the groundwork for advanced data analysis and management. In future versions, we plan to build upon these features, introducing more sophisticated statistical methods and expanding the application's capabilities.
+
+Thank you for your ongoing support, and we look forward to delivering more powerful functionalities in the next release.
+
+[- Kavindu Kokila(Fullstack developer)](mailto:[email protected])

Docs/release notes v-1-2-0.md

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+# Release Notes v1.2.0
+
+## Overview
+
+Version 1.2.0 marks a significant milestone as we introduce AI prediction capabilities using pre-trained Python models.
+Leveraging the power of Py4J gateway, our application seamlessly communicates between Python and Java to provide
+accurate predictions for various factors. This release also includes the implementation of Linear Regression for
+training models, extensive documentation, and the introduction of essential dependencies.
+
+## New Features
+
+#### AI Prediction Methods
+
+- **Pre-trained Python Models:** Integrated AI prediction methods using pre-trained Python models for each factor.
+- **Py4J Gateway Communication:** Established communication between Python and Java using Py4J gateway.
+- **Linear Regression Models:** Implemented Linear Regression to train models for accurate predictions.
+
+#### Create and Train Models
+
+```python
+from faker import Faker
+import csv
+import random
+from datetime import datetime, timedelta
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import pickle
+
+fake = Faker()
+
+
+# Function to generate improved dummy air quality data
+def generate_improved_air_quality_data(locations, start_time, end_time, frequency):
+  fieldnames = ['timestamp', 'location', 'pm25', 'pm10', 'co2', 'ozone', 'no2', 'airTemperature', 'airHumidity',
+                'airWind_speed']
+  with open('improved_air_quality_data.csv', 'w', newline='') as csvfile:
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+    writer.writeheader()
+
+    current_time = start_time
+    while current_time <= end_time:
+      for location in locations:
+        temperature = random.uniform(20, 30)
+        humidity = random.uniform(15, 20)
+
+        # Simulate rush hour increase in pollution
+        if 8 <= current_time.hour <= 10 or 17 <= current_time.hour <= 19:
+          pollution_increase = random.uniform(5, 15)
+        else:
+          pollution_increase = 0
+
+        writer.writerow({
+          'timestamp': current_time.strftime('%Y-%m-%d %H:%M:%S'),
+          'location': location,
+          'pm25': temperature + humidity + pollution_increase + random.uniform(-2, 2),
+          'pm10': temperature + humidity + pollution_increase + random.uniform(-2, 2),
+          'co2': temperature + humidity + pollution_increase + random.uniform(-3, 3),
+          'ozone': temperature + humidity + pollution_increase + random.uniform(-2, 2),
+          'no2': temperature + humidity + pollution_increase + random.uniform(-1, 1),
+          'airTemperature': temperature,
+          'airHumidity': humidity,
+          'airWind_speed': random.uniform(3, 8)
+        })
+
+      # Sleep for 5 minutes before generating data for the next interval
+      # time.sleep(30)
+      current_time += frequency
+
+
+# Function to generate improved dummy meteorological data
+def generate_improved_meteorological_data(locations, start_time, end_time, frequency):
+  with open('improved_meteorological_data.csv', 'w', newline='') as csvfile:
+    fieldnames = ['timestamp', 'location', 'temperature', 'humidity', 'wind_speed', 'precipitation']
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+    writer.writeheader()
+
+    current_time = start_time
+    while current_time <= end_time:
+      for location in locations:
+        temperature = random.uniform(20, 30)
+        humidity = random.uniform(15, 20)
+
+        # Simulate higher precipitation during colder months
+        precipitation = random.uniform(0, 0.5) + max(0, (25 - temperature) / 25)
+
+        writer.writerow({
+          'timestamp': current_time.strftime('%Y-%m-%d %H:%M:%S'),
+          'location': location,
+          'temperature': temperature,
+          'humidity': humidity,
+          'wind_speed': random.uniform(3, 8),
+          'precipitation': precipitation
+        })
+      # time.sleep(30)
+      current_time += frequency
+
+
+# Function to generate improved dummy land use information
+def generate_improved_land_use_data(locations):
+  with open('improved_land_use_data.csv', 'w', newline='') as csvfile:
+    fieldnames = ['location', 'land_type']
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+    writer.writeheader()
+
+    for location in locations:
+      # Simulate variations in land use types
+      land_type = random.choice(['Residential', 'Commercial', 'Industrial', 'Park'])
+      writer.writerow({
+        'location': location,
+        'land_type': land_type
+      })
+
+
+# Train and save a model for a given feature
+def train_and_save_model(df, feature_name):
+  X = df.drop(['timestamp', 'location', feature_name], axis=1)
+  y = df[feature_name]
+
+  # Split the dataset into training and testing sets
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+  # Train a model (example: Random Forest Regressor)
+  model = LinearRegression()
+  model.fit(X_train, y_train)
+
+  # Make predictions
+  predictions = model.predict(X_test)
+
+  # Evaluate the model
+  mse = mean_squared_error(y_test, predictions)
+  print(f'Mean Squared Error for {feature_name}: {mse}')
+
+  # Save the trained model
+  model_filename = f'{feature_name}_model.pkl'
+  with open(model_filename, 'wb') as f:
+    pickle.dump(model, f)
+
+  print(f'Model saved as {model_filename}')
+
+
+if __name__ == "__main__":
+  # Define parameters
+  locations = ['CentralPark', 'Downtown', 'SuburbA', 'SuburbB']
+  start_time = datetime.now()
+  end_time = start_time + timedelta(days=1)  # Run for 1 day
+  frequency = timedelta(minutes=15)
+
+  # Generate dummy datasets continuously
+  generate_improved_air_quality_data(locations, start_time, end_time, frequency)
+  generate_improved_meteorological_data(locations, start_time, end_time, frequency)
+  generate_improved_land_use_data(locations)
+
+  # Load datasets for each factor
+  df_air_quality = pd.read_csv('improved_air_quality_data.csv')
+  df_meteorological = pd.read_csv('improved_meteorological_data.csv')
+
+  # Train and save models for each factor
+  for feature in ['pm25', 'pm10', 'co2', 'ozone', 'no2', 'temperature', 'humidity', 'airHumidity', 'wind_speed',
+                  'precipitation', 'airWind_speed', 'airTemperature']:
+    if feature in df_meteorological.columns:
+      train_and_save_model(df_meteorological, feature)
+    else:
+      print(f"Warning: {feature} not found in meteorological dataset. Skipping...")
+
+    if feature in df_air_quality.columns:
+      train_and_save_model(df_air_quality, feature)
+    else:
+      print(f"Warning: {feature} not found in air quality dataset. Skipping...")
+```
+
+#### Documentation
+- **Code Documentation:** Extensively documented AI prediction methods, Py4J gateway usage, and model training.
+- **Statistical API Tests:** Rigorously tested all statistical APIs and documented test cases.
+- **API Documentation:** Comprehensive documentation for all APIs, providing usage guidelines and examples.
+
+#### New Dependencies
+- **py4j:** Version 0.10.9.7 for seamless Python-Java integration.
+- **junit:** Used for testing purposes.
+- **pmml-sklearn:** Version 1.7.45 for working with PMML models.
+- **slf4j-jdk14:** Version 2.0.9 for logging in the JDK 14 environment.
+
+### Next Steps
+In upcoming releases, we plan to enhance the AI prediction capabilities, introduce more advanced machine learning models, and further refine the statistical analysis methods. Your feedback is invaluable as we continue to improve and expand the features of our Air Quality Monitoring Application.
+
+Thank you for your continued support.
+
+[- Kavindu Kokila(Fullstack developer)](mailto:[email protected])