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The term “cloud computing (CC)” has been extensively studied and utilized by major corporations since its inception. Within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. This paper explores the intersection of data quality and business process management within the context of cloud computing. Specifically, it examines how Quality of Service (QoS)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. Cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. We need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. The purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. An attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. Our data reveals that 34% of researchers are focusing on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the Quality of Service (QoS) parameter contribute far less than the ones mentioned above. According to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. According to the available literature, 70% of the studies have utilized CloudSim as their simulation tool of choice. Our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with CloudSim being a popular simulation tool. Ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (QoS)-driven scheduling algorithms for cloud computing.  

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.

Record linkage is vital for consolidating data from different sources, particularly in Persian records where diverse data structures and formats present challenges. To tackle these complexities, an expert system with decision tree algorithms is crucial for ensuring precise record linkage and data aggregation. Adaptation operations are created based on predefined rules by incorporating decision trees into an expert system framework, simplifying the aggregation of disparate data sources. This method surpasses traditional approaches like IF-THEN rules in effectiveness and ease of use and improves accessibility for non-technical users due to its intuitive nature. Integrating probabilistic record linkage results into the decision tree model within the expert system automates the linkage process, allowing users to customize string metrics and thresholds for optimal outcomes. The model’s accuracy rate of over 95% on test data highlights its effectiveness in predicting and adjusting to data variations, confirming its reliability in various record linkage scenarios. The innovative utilization of machine learning decision trees alongside probabilistic record linkage in an expert system represents a significant advancement in the field, providing a robust solution for data aggregation in intricate environments and large-scale projects involving Persian records. Combining decision tree algorithms and probabilistic record linkage within an expert system offers a powerful tool for handling complex data integration tasks. This approach not only streamlines the process of consolidating diverse data sources but also enhances the accuracy and efficiency of record linkage operations By leveraging machine learning techniques and automated decision-making processes, organizations can achieve significant improvements in data quality and consistency, paving the way for more reliable and insightful analytical results in implementing statistical registers. In conclusion, integrating decision trees and probabilistic record linkage in an expert system represents a cutting-edge solution for addressing data aggregation challenges in Persian records and beyond.