ABSTRACT Methodology In this research, we conducted a case study to establish a foundation for size measurement and effort estimation in DWH projects. We first applied a productivity‐based estimation approach using linear regression with the ISBSG repository to assist organizations without historical data. We then evaluated various machine learning algorithms to improve estimation accuracy. Finally, we tested a combined model that integrates both approaches for estimating effort in external projects. Results Using the ISBSG dataset, linear regression models based on productivity achieved a Mean Magnitude of Relative Error (MMRE) of 0.285. Machine learning algorithms improved accuracy by 22.81%, reducing the MMRE to 0.220. The final model, applied to external projects, yielded MRE values between 0.010 and 0.245. Conclusion The ISBSG repository is a valuable resource for effort estimation in DWH projects. Combining productivity‐based estimation with machine learning enhances accuracy and predictive performance, making it a more reliable approach than traditional models.

Organizations may create a sustainable competitive advantage against competitors by using data warehouse systems with which they can assess the current status of their operations at any moment. They can analyze trends and connections using up-to-date data. However, data warehouse projects tend to fail more often than other projects as it can be tough to estimate the effort required to build a data warehouse system. Functional size measurement is one of the methods used as an input for estimating the amount of work in a software project. In this study, we formed a measurement basis for DWH projects in an organization based on the COSMIC Functional Size Measurement Method. We mapped COSMIC rules on two different architectures used for DWH projects in the organization and measured the size of the projects. We calculated the productivity of the projects and compared them with the organization’s previous projects and DWH projects in the ISBSG repository. We could not create an organization-wide effort estimation model as we had a limited number of projects. As an alternative, we evaluated the success of effort estimation using DWH projects in the ISBSG repository. We also reported the challenges we faced during the size measurement process.

The rapidly developing internet infrastructure together with the advances in software technology has enabled the development of cloud-based modern web applications that are much more responsive, flexible, and reliable compared to traditional monolithic applications. Such modern applications require new software design paradigms and architectures. Microservice-based architecture (MSbA), which aims to create small, isolated, loosely-coupled applications that work in cohesion, becoming widespread as one of these approaches. MSbA allows the developed applications to be deployed and maintained separately, as well as scaled on demand. However, there is no de facto method for the analysis and design of systems for these architectures. In this paper, we compared the usefulness of the object-oriented (OO) and event-oriented (EO) approaches for analyzing and designing MS-based systems. More specifically, we performed an exploratory case study to analyze, design, and implement a software application dealing with the ‘application and evaluation process of graduate students at IzTech’. This paper discusses the results of this case study. We observe that the EO approaches have significant advantages with respect to the OO approaches.

In this study, we propose a pricing based algorithm that assigns user terminals (UTs) to base stations (BSs) and optimizes the transmission powers in a way that minimizes the energy expenditure. The algorithm takes into account the fixed energy expenditure that occurs even if a BS does not transmit to any UT, therefore the proposed algorithm switches off the unnecessary BSs in order to minimize the total energy expenditure. We compare this algorithm with two benchmarks. One of them is a simple algorithm that connects each UT to the BS with best channel conditions. Transmission powers are then optimized iteratively. The second benchmark is the optimal solution that is found using a branch and bound technique. The numerical evaluations reveal that the pricing based algorithm performs very close to the optimal.