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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.
FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)
FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |