حمید حسنی

Data is considered the most crucial element in open banking processes and services. Therefore, it is necessary to pay attention to various aspects of the quality of this data in order to provide appropriate and expected services to customers. In this research, various dimensions representing different aspects of data quality were investigated in the field of open banking. This research has been conducted in two main steps: The Delphi method and the pairwise comparisons method. In the first step, various dimensions of data quality in open banking were extracted based on the Delphi method. In the next step, the importance of each of these dimensions was assessed relative to each other using the pairwise comparisons method, and the most crucial dimensions were determined. Based on the results obtained from these two methods, the significance of eleven dimensions of data quality in this field was determined. The best overall weighted averages were related to dimensions such as accuracy, accessibility, relevancy, timeliness, consistency, security, interpretability, reputation, believability, ease of understanding, and value-added, respectively. Banks and fintech companies offering open banking services can consider these dimensions when evaluating the quality of their data to enhance the provision of superior services

Organizing a large volume of scientific multimedia data requires the use of appropriate indexing methods as one of the processes of information organization. Appropriate methods and algorithms are those that lead to the improvement of various aspects of quality in the process of organizing and retrieving information. For this reason, the purpose of this research is to identify the most important dimensions of data quality in the field of scientific multimedia indexing. In order to achieve this goal, a comparison of different dimensions of data quality has been made based on different criteria and the most important dimensions have been identified using Shannon entropy weighting approach and TOPSIS group ranking method. Also, using the correlation matrix, the intensity and direction of the relationship and correlation between the different dimensions of data quality have been evaluated. Based on the results of the first part of the research, the best ranks (priorities) were related to the data quality dimensions of recall, precision, completeness, appropriate amount of data, accuracy, relevancy, concise 1, consistency, concise 2, interpretability, value-added and accessibility, respectively. The results obtained from the second part of the research showed that the data quality dimensions of interpretability and relevancy had the highest correlation with the most important dimensions, i.e. recall and precision. As one of the implications of this research, it is possible to consider the measurement and evaluation of scientific multimedia data indexing methods based on different aspects of data quality and their importance.