Impact of Data Quality Types on Computational Time in Data Source Selection Using Ant Colony Optimization

Data quality varies dramatically from source to source, even within the same domain. Given these challenges, data source selection has emerged as a crucial step in information integration. It demands efficient and scalable approaches that can handle massive data volumes while ensuring the quality of results. Adapting the ACO algorithm to solve the data sources selection problems may lead to inconsistent computational time if the data sources provided are vary in quality. These challenges bring the issues of time consuming in selecting the required data sources. However, how much the computational time needed in solving the data sources selection is depending on the type of data quality. Hence, in this article, the impact of quality type of data towards computational time is examined in solving the data sources selection problems. For the methodology used, there are five steps need to be followed which are first collect data set, second import the data sources to the data sources selection model, third implement the ACO algorithm, fourth obtain the computational time and lastly compare the results. The experiment shows that low-quality data set achieve higher computational time compared to the high-quality data set which achieve the minimum computational time by 3.38 % faster. The results obtained in this experiment shown that the quality type of data has given an impact to the computational time of ACO algorithm. The results also clearly show the contribution of high-quality data set in minimizing computational time in the selection process. The validation on quality type of data with computational time is to clarify the importance of selecting a good quality data to save the computational time.