Dataflexor: An Efficient Data Management Application using Fire Base and Advanced AI Models
Year: 2024
Type: conference-paper
Abstract: This paper presents the development and evaluation of Dataflexor, a streamlined data management application designed to enhance efficiency in professional environments [1]. The application integrates Firebase services for user authentication and real-time data synchronization, as well as advanced language models like ChatGPT and Gemini for enhanced functionality [2]. Several challenges were addressed during development, including API integration, latency issues, and user data privacy [3]. Experiments were conducted to evaluate the performance of Firebase Cloud Functions and the response times of integrated APIs under varying traffic conditions [4]. The results revealed that while the application performs well under normal usage, significant performance drops occur during peak loads, indicating areas for further optimization. The study concludes that, with improvements in customization options, workflow optimization, and backend scalability, Dataflexor has the potential to become a powerful tool for professionals, offering both efficiency and flexibility in data management tasks [5].
Authors: Weichuan Chen, Yu Sun
Institution: California State Polytechnic University
Language: English
Topic: Mobile and Web Applications
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404448620