Historical Redundant Process Data Recovery based on Genetic Algorithm

In many other domains, the data may have such issues as conflicts, duplicates, and missing values, and it must be cleaned before utilization. For instance, historical data reports on numerous events for overlapping time intervals may have data conflicts caused by database redundancy. These conflicts...

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Bibliographic Details
Published in2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) pp. 280 - 285
Main Author Hsu, Ying-Feng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2023
Subjects
Data aggregation
Data conflicts
Data Redundancy
Estimation
Evolutionary computation
Genetic Algorithm
historical reports
Probabilistic logic
Redundancy
Statistical analysis
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Summary:In many other domains, the data may have such issues as conflicts, duplicates, and missing values, and it must be cleaned before utilization. For instance, historical data reports on numerous events for overlapping time intervals may have data conflicts caused by database redundancy. These conflicts can prevent researchers from obtaining the correct answers from data. In this paper, we investigated redundant process data recovery (RPDR) approaches to recover the individual values of time intervals from redundant process data. There are three major contributions to this study. First, we explore RPDR approaches from the areas of statistical analysis, evolutionary computation (Genetic Algorithm), and probabilistic value estimations (Bayesian method). Second, we explore the applicability of the proposed RPDR algorithms to the case of having additional information from redundant data. Third, we utilize the concept of optimal CD (Conflict Degree) further to reduce data aggregation error in the integrated historical database. In general, it is challenging to estimate an accurate individual value within a given time interval. With the help of optimal CD, our experimental results demonstrate the high efficiency of the proposed approach by using the genetic algorithm to minimize the misestimation of those sequence values in those individual time spans.
DOI:10.1109/COMPSAC57700.2023.00043