Pattern Search Analysis Tool

This project is a web-based tool for analyzing the performance of different pattern search algorithms on a given text file. It allows users to upload a text file, enter one or multiple patterns, and perform searches using various algorithms. Users can choose to auto-select the optimal algorithm based on pattern characteristics, manually select an algorithm, or conduct a randomized pattern search analysis to compare algorithm performance.

Features

• File Upload: Upload a text file to analyze.
• Multiple Pattern Search: Enter multiple patterns (comma-separated) for searching.
• Auto-Analyze: Automatically select the best algorithm based on pattern characteristics.
• Manual Selection: Choose an algorithm manually from Naive, Aho-Corasick, Boyer-Moore, KMP, Rabin-Karp, etc.
• Random Pattern Analysis: Generate random patterns to analyze algorithm performance across multiple runs.
• Performance Comparison Chart: Visualize the average time taken by each algorithm in random pattern analysis.

Technologies Used

• Front-End: HTML, JavaScript, Bootstrap, Chart.js
• Back-End: Python (Flask)
• Algorithms: Implemented in C++ (Naive, Boyer-Moore, KMP, Rabin-Karp, Aho-Corasick, Finite-Automata, Z-Algorithm)

Pre-Requisites

• Python 3.x and Flask
• C++ Compiler to compile the algorithm binaries
• Make for building C++ binaries

Installation and Setup

1. Clone the Repository

   git clone https://github.com/anuranjani23/pattern-search-analysis-tool.git
   cd pattern-search-analysis-tool

2. Compile the Algorithm Binaries

   Navigate to the root directory and run the make command to compile the C++ files in the src/ directory and create binaries in the bin/ folder.

   make

   This will generate the binaries naive_search, boyer_moore_search, kmp_search, and rabin_karp_search, aho_corasick_search, z_search, dfa_search in the bin/ directory.

3. Install Python Dependencies

   Install Flask and any other required dependencies using pip.

   pip install flask
   pip install werkzeug
   pip install requests
   pip install beautifulsoup4
   pip install lxml

4. Run the Flask Server

   Start the Flask server on port 5000.

   python app.py

   The application will be accessible at http://127.0.0.1:5000/.

Project Structure

.
├── bin/                 # Contains compiled binaries for the search algorithms
├── src/                 # C++ source code files for each search algorithm
│   ├── naive_search.cpp
│   ├── rabin_karp_search.cpp
│   ├── kmp_search.cpp
│   ├── boyer_moore_search.cpp
│   ├── dfa_search.cpp
│   ├── z_search.cpp
│   └── aho_corasick_search.cpp
├── templates/           # HTML template files for the Flask application
│   └── index.html       # Main front-end interface
├── uploads/             # Text files to search from
│   └── sample.txt       
├── app.py               # Flask server and application logic
├── Makefile             # Makefile to compile C++ binaries
├── README.md            # Project documentation
└── .gitignore           

Usage

1. Upload a Text File or a URL

   Start by uploading a text file through the "Choose File" button. Or enter a URL.

2. Enter Patterns

   Enter one or more patterns in the provided text area, separated by commas.

3. Choose Search Options

   • Auto-Analyze Search: Automatically chooses the best algorithm based on the characteristics of each pattern.
   • Manual Selection: Allows you to select a specific algorithm from the dropdown and perform the search manually.
   • Random Pattern Analysis: Generates random patterns and evaluates each algorithm’s performance over multiple runs, displaying the results in a bar chart.

4. View Results

   Results are displayed in the "Results" section, and performance data is visualized in a bar chart when performing random pattern analysis.

License

This project is licensed under the MIT License.