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Conference Object Citation - Scopus: 2Big Data on Cloud for Government Agencies: Benefits, Challenges, and Solutions(Assoc Computing Machinery, 2018) Rashed, Alaa Hussain; Karakaya, Ziya; Yazici, AliBig Data and Cloud computing are the most important technologies that give the opportunity for government agencies to gain a competitive advantage and improve their organizations. On one hand, Big Data implementation requires investing a significant amount of money in hardware, software, and workforce. On the other hand, Cloud Computing offers an unlimited, scalable and on-demand pool of resources which provide the ability to adopt Big Data technology without wasting on the financial resources of the organization and make the implementation of Big Data faster and easier. The aim of this study is to conduct a systematic literature review in order to collect data to identify the benefits and challenges of Big Data on Cloud for government agencies and to make a clear understanding of how combining Big Data and Cloud Computing help to overcome some of these challenges. The last objective of this study is to identify the solutions for related challenges of Big Data. Four research questions were designed to determine the information that is related to the objectives of this study. Data is collected using literature review method and the results are deduced from there.Conference Object Citation - WoS: 4Citation - Scopus: 3Systematic Mapping Study on Performance Scalability in Big Data on Cloud Using Vm and Container(Springer-verlag Berlin, 2016) Gokhan, Cansu; Karakaya, Ziya; Yazici, AliIn recent years, big data and cloud computing have gained importance in IT and business. These two technologies are becoming complementing in a way that the former requires large amount of storage and computation power, which are the key enabler technologies of Big Data; the latter, cloud computing, brings the opportunity to scale on-demand computation power and provides massive quantities of storage space. Until recently, the only technique used in computation resource utilization was based on the hypervisor, which is used to create the virtual machine. Nowadays, another technique, which claims better resource utilization, called "container" is becoming popular. This technique is otherwise known as "lightweight virtualization" since it creates completely isolated virtual environments on top of underlying operating systems. The main objective of this study is to clarify the research area concerned with performance issues using VM and container in big data on cloud, and to give a direction for future research.Article Citation - WoS: 12Citation - Scopus: 16Teaching Parallel Computing Concepts Using Real-Life Applications(Tempus Publications, 2016) Yazici, Ali; Mishra, Alok; Karakaya, Ziya; Computer Engineering; Software EngineeringThe need to promote parallel computing concepts is an important issue due to a rapid advance in multi-core architectures. This paper reports experiences in teaching parallel computing concepts to computer and software engineering undergraduates. By taking a practical approach in delivering the material, students are shown to grasp the essential concepts in an effective way. This has been demonstrated by implementing small projects during the course, such as computing the sum of the terms of a geometric series using pipelines, solving linear systems by parallel iterative methods, and computing Mandelbrot set (fractal). This study shows that, it is useful to provide real-life analogies to facilitate general understanding and to motivate students in their studies as early as possible via small project implementations. The paper also describes an overall approach used to develop students' parallel computing skills and provides examples of the analogies employed in conjunction with the approach described. This approach is also assessed by collecting questionnaires and learning outcome surveys.Article Citation - WoS: 3Citation - Scopus: 5Real-Time Anomaly Detection System Within the Scope of Smart Factories(Springer, 2023) Bayraktar, Cihan; Karakaya, Ziya; Gokcen, HadiAnomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting of interconnected devices. Synthetic data were preferred in the study because it has difficulties such as high cost and a long time to obtain real anomaly data naturally for learning and testing processes. In order to obtain the necessary synthetic data, a simulation was developed by taking the popcorn production systems as an example. Multi-class anomalies were defined in the obtained data set, and the analysis performances were tested by creating learning models with AutoML libraries. In the field of production systems, while studies on anomaly detection generally focus on whether there is an anomaly in the system, it is aimed to determine which type of anomaly occurs in which device, together with the detection of anomaly by using multi-class tags in the data of this study. As a result of the tests, the Auto-Sklearn library presented the learning models with the highest performance on all data sets. As a result of the study, a real-time anomaly detection system was developed on dynamic data by using the obtained learning models.Conference Object Citation - Scopus: 2Systematic Mapping for Big Data Stream Processing Frameworks(Institute of Electrical and Electronics Engineers Inc., 2016) Alayyoub,M.; Yazıcı, Ali; Yazici,A.; Karakaya,Z.; Karakaya, Ziya; Yazıcı, Ali; Karakaya, Ziya; Software Engineering; Computer Engineering; Software Engineering; Computer EngineeringThere has been lots of discussions about the choice of a stream processing framework (SPF) for Big Data. Each of the SPFs has different cutting edge technologies in their steps of processing the data in motion that gives them a better advantage over the others. Even though, the cutting edge technologies used in each stream processing framework might better them, it is still hard to say which framework bests the rest under different scenarios and conditions. In this study, we aim to show trends and differences about several SPFs for Big Data by using the Systematic Mapping (SM) approach. To achieve our objectives, we raise 6 research questions (RQs), in which 91 studies that conducted between 2010 and 2015 were evaluated. We present the trends by classifying the research on SPFs with respect to the proposed RQs which can help researchers to obtain an overview of the field. © 2016 IEEE.
