Browsing by Author "Aydin, M.N."

Now showing 1 - 6 of 6

Big data analytics and models
(IGI Global, 2019) Sönmez, F.; Perdahçi, Z.N.; Aydin, M.N.; 01. Kadir Has University
When uncertainty is regarded as a surprise and an event in the minds, it can be said that individuals can change the future view. Market, financial, operational, social, environmental, institutional and humanitarian risks and uncertainties are the inherent realities of the modern world. Life is suffused with randomness and volatility; everything momentous that occurs in the illustrious sweep of history, or in our individual lives, is an outcome of uncertainty. An important implication of such uncertainty is the financial instability engendered to the victims of different sorts of perils. This chapter is intended to explore big data analytics as a comprehensive technique for processing large amounts of data to uncover insights. Several techniques before big data analytics like financial econometrics and optimization models have been used. Therefore, initially these techniques are mentioned. Then, how big data analytics has altered the methods of analysis is mentioned. Lastly, cases promoting big data analytics are mentioned. © 2020, IGI Global.
Citation - Scopus: 3
Cloud-Based Development Environments: Paas
(Wiley, 2016) Aydin, M.N.; Perdahci, N.Z.; Odevci, B.; 01. Kadir Has University
In this chapter we elaborate the fundamentals of platform as a service (PaaS) and outline core components of a typical PaaS. We focus on a specific cloud-computing layer, which is called application platform as a service (aPaaS). Emergence of this layer raises several questions regarding the existence of traditional development environments, tools, and the viability of alternative ecosystems along with new positions in the market. It is this questioning that shows the need for making sense of what underpins the very idea of aPaaS and how the idea is manifested in the market, and finally what implications can be drawn for academics and practitioners We articulate basic approaches to aPaaS and discuss metadata aPaaS by using our industry experience as well as a review of recent studies on this subject. This leads to a concise comparison of leading PaaS solutions. © 2016 John Wiley & Sons, Ltd.
An Empirical Study on Performance Comparisons of Different Types of DevOps Team Formations
(Frontiers Media SA, 2025) Korkmaz, H.E.; Aydin, M.N.; 01. Kadir Has University
Introduction: Despite all the efforts to successfully implement DevOps practices, principles, and cultural change, there is still a lack of understanding on how DevOps team structure formation and performance differences are related. The lack of a ground truth for DevOps team structure formation and performance has become a persistent and relevant problem for companies and researchers. Methods: In this study, we propose a framework for DevOps team Formation–Performance and conduct a survey to examine the relationships between team formations and performance with the five metrics we identified, two of which are novel. We conducted an empirical study using a survey to gather data. We employed targeted outreach on a social media platform along via a snowball sampling and sent 380 messages to DevOps professionals worldwide. This approach resulted in 122 positive responses and 105 completed surveys, achieving a 69.7% response rate from those who agreed to participate. Results: The research shows that implementing the DevOps methodology enhances team efficiency across various team structures, with the sole exception of “Separate Development and Operation teams with limited collaboration”. Moreover, the study reveals that all teams experienced improvements in Repair/Recovery performance metric following DevOps adoption. Notably, the “Separate Development and Operation teams with high collaboration” formation emerged as the top performer in the key metrics, including Deployment Frequency, Number of Incidents, and Number of Failures/Service Interruptions. The analysis further indicates that different DevOps organizational formations do not significantly impact Lead Time, Repair/Recovery, and Number of Failures/Service Interruptions in terms of goal achievement. However, a statistically significant disparity was observed between “Separate Development and Operation teams with high collaboration” and “A single team formation” regarding the Deployment Frequency goal achievement percentage. Discussion: The analysis confirms that DevOps adoption improves performance across most team formations, with the exception of “Separate Development and Operation teams with limited collaboration” (TeamType1), which shows significant improvement only in Mean Time to Recovery (MTTR). Standardized effect size calculations (Cohen’s d) reveal that TeamType2 (“Separate Development and Operation teams with high collaboration”) consistently achieves large effects in Deployment Frequency (DF), Number of Incidents (NoI), and Number of Failures/Service Interruptions (NoF/NoSI), while TeamType3 shows strong results for Lead Time (LT) and NoF/NoSI. MTTR improvements are large across all formations, with TeamType4 performing best in this metric. These findings suggest that collaboration intensity is a critical determinant of performance gains. While team formation type does not significantly influence LT, MTTR, or NoF/NoSI goal achievement, DF goal achievement is significantly higher for TeamType2 compared to TeamType4, highlighting the potential competitive advantage of high-collaboration structures. © 2025 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Forecasting the Short-Term Electricity in Steel Manufacturing for Purchase Accuracy on Day-Ahead Market
(Institute of Electrical and Electronics Engineers Inc., 2022) Koca, A.; Erdem, Z.; Aydin, M.N.; 01. Kadir Has University
Forecasting electricity consumption in the most accurate way is crucial for purchase on the day-ahead market in steel manufacturing. This study is aimed to predict short-term electricity consumption regarding the day-ahead market purchase by employing important features of electricity consumption time-series data. We utilize Random Forest (RF), Gradient-Boosted Trees (GBT), and Generalized Linear Models (GLM), as they are appropriate for the given problem and widely used regression algorithms for prediction purposes. This study leverages the regression algorithms in the Apache Spark Machine Learning library. The performance of the prediction models is evaluated based on the standard deviation of the residuals (RMSE) and the proportion of variance explained (R-squared). We additionally discuss the distribution of prediction errors of the models. Experiments show that the RF model outperforms the GBT and GLM. It is considered that the results can contribute to accurate forecasting of short-term electricity demand for purchasing on the day-ahead. © 2022 IEEE.
Issues Regarding Deployment of Ipv6 and Business Model Canvas for Ipv6
(Peter Lang AG, 2019) Aydin, M.N.; Dilan, E.; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
[No abstract available]
A Machine Learning Approach To Steel Sheet Production Surface Quality
(Institute of Electrical and Electronics Engineers Inc., 2024) Öztürk, A.; Aydin, M.N.; 01. Kadir Has University
This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE.