OlymPC: Utilizing Descriptive Analytics and Rule-Based Algorithms for Hardware Compatibility and Product Recommendations for Pc Builders & Tech Specialists

Abstract

This study presents OlymPC, a web-based application designed to streamline the process of assembling custom personal computers by integrating descriptive analytics and rule-based algorithms to evaluate hardware compatibility and generate tailored product recommendations. The system assists users by automatically validating selected components, identifying incompatibilities, and suggesting optimized alternatives based on performance requirements and budget considerations. By reducing the need for extensive manual research, OlymPC serves as a practical tool for beginners, hobbyists, students, technicians, and independent builders seeking a more guided and reliable approach to PC configuration. Data were gathered through survey questionnaires, interviews, and observational analysis of existing PC-building platforms to identify user needs, challenges, and system requirements.
Developed using PHP, hosted locally via XAMPP, and supported by a MySQL backend, the system utilizes accessible and adaptable technologies suited for web-based environments. To ensure its effectiveness and reliability, OlymPC is assessed using the ISO/IEC 25010:2011 software quality standards, focusing on functionality, usability, reliability, efficiency, and maintainability. A total of 80 users and 20 technical experts participated in the evaluation, collectively indicating that the platform performs strongly across all assessed quality attributes. Respondents highlighted its accurate compatibility checks, intuitive navigation flow, responsive interface behavior, stable operation, and structurally maintainable design.
The findings confirm that OlymPC successfully fulfills its primary purpose of guiding users toward building compatible and performance-optimized PC setups. User feedback emphasized the system’s clarity and ease of use, while technical respondents validated its robust architecture and dependability. These results affirm the system’s capacity to support both novice and experienced builders, increasing confidence in component selection and reducing configuration-related errors.
To enhance the application further, the study recommends expanding and continuously updating the hardware database, ideally through API-driven data integration with hardware suppliers. Future iterations may also benefit from ongoing evaluations, periodic updates, and the incorporation of adaptive or AI-driven algorithms to enable more dynamic and personalized recommendation outputs. Establishing a long-term maintenance plan focused on scalability, bug resolution, and system enhancements is also advised to preserve system quality over time. Addressing these recommendations will allow future developers to refine OlymPC into a more comprehensive, efficient, and user-centered decision-support tool for the PC-building community.