Optimization of the Analysis of Vital Events Using Threads

TL;DR

This paper demonstrates that using threaded Python code significantly improves data analysis times for vital events, especially as the number of attributes increases, making it a recommended approach.

Contribution

It introduces a Python implementation with threading for analyzing vital event data and shows its superior performance over non-threaded code.

Findings

Threaded Python code reduces analysis time by up to 16 times.

Performance improves with more attributes grouped together.

Threading is recommended for efficient data analysis in similar contexts.

Abstract

Objective: Optimize the time of data analysis of Vital Events (births, deaths and marriages) using Threads. Methodology: A code was created in python without threads and another with threads, after He performed 5 tests with a single attribute and with 1,2,3,4,5,6,7,8 and 9 attributes this was done in both codes with the same amount of data, to know if the python code with threads is optimal when parsing Vital Facts data. Results: The python code with Threads turned out to be the most optimal since it optimized the compilation time of the 5 tests with 1 attribute by 16attributes was obtained as a result that the more attributes you group, the more effective the use of threads. Conclusion: Python code with threads is more optimal than code without threads Therefore, it is concluded that the implementation of threads is recommended in the analysis of data in similar works.