COMPARATIVE ANALYSIS AND PERFORMANCE EVALUATION OF DISK SCHEDULING ALGORITHMS: FIRST COME FIRST SERVE (FCFS) AND SHORTEST SEEK TIME FIRST (SSTF)

This project presents a comprehensive web-based application for detecting and managing duplicate records in datasets using configurable rule-based algorithms. The system provides an intuitive interface for data quality management, enabling users to identify, analyze, and resolve data duplicates through multiple matching strategies. The application implements four distinct matching algorithms: exact matching for precise character-by-character comparison, fuzzy matching using Levenshtein distance for handling typographical errors, phonetic matching with Soundex algorithm for sound-based similarities, and partial matching using Jaccard similarity for subset comparisons. Users can configure custom detection rules with adjustable weights and thresholds, allowing fine-tuned control over the matching process. Built using React with TypeScript for type safety and maintainability, the system features a modular architecture with dedicated components for data management, rule configuration, results analysis, and reporting. The duplicate detection engine processes records in real-time, calculating confidence scores for potential matches and grouping duplicates based on configurable similarity thresholds. The system supports CSV data import and manual record entry, provides interactive duplicate group visualization with expandable details, offers merge and delete operations for data cleanup, and includes comprehensive reporting with field-level analysis and confidence distribution metrics. Export capabilities support multiple formats including JSON, CSV, XML, and plain text reports. The application features a modern, responsive design with sidebar navigation, real-time processing indicators, and intuitive data tables, incorporating visual elements such as color-coded confidence levels, highlighted matching fields, and interactive charts for statistical analysis. This tool is suitable for data quality management in customer databases, inventory systems, contact management, and any scenario requiring identification and resolution of duplicate records, with the configurable rule system making it adaptable to various data types and matching requirements. The system successfully identifies duplicate records with high accuracy, provides detailed analytics on data quality metrics, and enables efficient data cleanup workflows through real-time feedback and comprehensive result visualization.