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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.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: 6Jmathnorm: a Database Normalization Tool Using Mathematica(Springer-verlag Berlin, 2007) Yazici, Ali; Karakaya, ZiyaThis paper is about designing a complete interactive tool, named JMathNorm, for relational database (RDB) normalization using Mathematica. It is an extension of the prototype developed by the same authors [1] with the inclusion of Second Normal Form (2NF), and Boyce-Codd Normal Form (BCNF) in addition to the existing Third normal Form (3NF) module. The tool developed in this study is complete and can be used for real-time database design as well as an aid in teaching fundamental concepts of DB normalization to students with limited mathematical background. JMathNorm also supports interactive use of modules for experimenting the fundamental set operations such as closure, and full closure together with modules to obtain the minimal cover of the functional dependency set and testing an attribute for a candidate key. JMathNorm's GUI interface is written in Java and utilizes Mathematica's Mink facility to drive the Mathematica kernel.Conference Object Citation - WoS: 2Need for a Software Development Methodology for Research-Based Software Projects(Ieee, 2018) Cereci, Ibrahim; Karakaya, ZiyaSoftware development is mostly carried by a group of individuals. Software development methodologies are heavily utilized to organize these individuals and keep track of the entire software development process. Although previously proposed software development methodologies meet the needs of the industry and the firms, they are not usually suitable for research-based software projects that are carried by universities and individual researchers. In this paper, we aim to show the necessity of a new software development methodology for research-based problems carried by universities. The literature review will show the differences between industry and university software projects from certain aspects. These findings will be supported by the authors own research on the area. This qualitative research involves collecting data through interviews and applying Grounded Theory to better understand the development process.Conference Object Informatics Engineering Education in Turkey and Expectations of Software Industry(Ieee, 2018) Yazici, Ali; Mishra, Alok; Karakaya, Ziya; Ustunkok, TolgaIn this study, using the OSYM data, the number of intakes in Informatics Engineering programs in Turkey, accreditation data and the medium of instruction of the program are summarized for the years 2016 and 2017. In addition, the software sector's expectations from the informatics engineering graduates are reassessed based on the academic studies. The developed knowledge-skill gap set was used to evaluate the situation in Turkish informatics engineering programs. Sector expectations are discussed in the context of 2017-2019 Turkey Software Sector Strategy and Action Plan prepared by the Ministry of Science, Industry and Technology of Turkey and some proposals are made for the academia. As a result, it was observed that the expectations of the software industry were similar in all studies. Additionally, the expectations were changed in the direction of developing technologies and this change should be reflected in the informatics engineering programs.Conference Object Perceptions, Expectations and Implementations of Big Data in Public Sector(Ieee, 2018) Dogdu, Erdogan; Ozbayoglu, Murat; Yazici, Ali; Karakaya, ZiyaBig Data is one of the most commonly encountered buzzwords among IT professionals nowadays. Technological advancements in data acquisition, storage, telecommunications, embedded systems and sensor technologies resulted in huge inflows of streaming data coming from variety of sources, ranging from financial streaming data to social media tweets, or wearable health gadgets to drone flight logs. The processing and analysis of such data is a difficult task, but as appointed by many IT experts, it is crucial to have a Big Data Implementation plan in today's challenging industry standards. In this study, we performed a survey among IT professionals working in the public sector and tried to address some of their implementation issues and their perception of Big Data today and their expectations about how the industry will evolve. The results indicate that most of the public sector professionals are aware of the current Big Data requirements, embrace the Big Data challenge and are optimistic about the future.Conference Object Citation - WoS: 3Software Engineering Issues in Big Data Application Development(Ieee, 2017) Karakaya, ZiyaBig Data has become one of the most important concepts that is being studied in Computer/Software Engineering. The data produced in recent years have increased rapidly and exponentially, necessitating the solution of major problems such as the collection, processing and storage of huge volume of data. Big Data Frameworks are developed specifically to solve these problems that facilitates application developers by providing opportunities to collect, process, manage, monitor and analyze these data. A few examples of these frameworks are Hadoop, Spark, Storm, and Flink, which are developed by Software Engineers as open source projects. Although the challenges raised from coordination of IT resources such as huge amounts of computation power, storage area, memory, and network bandwidth in a distributed manner solved by these frameworks, there still remains many Software Engineering problems in application development phase, even if they based on these frameworks. High scalability, fault tolerance, flexibility, reliability and testability can be listed as the main issues need to be carefully considered in terms of Software Engineering. In this paper, we first clarify the terms Framework-Application, and then the overview information about Big Data and related frameworks are given before emphasizing the problems arising in terms of Software Engineering. Nevertheless, we tried to provide guidance to the people who would develop software for Big Data and tried to give the further research guidance.Conference Object Citation - WoS: 28A Comparison of Stream Processing Frameworks(Ieee, 2017) Karakaya, Ziya; Yazici, Ali; Alayyoub, MohammedThis study compares the performance of Big Data Stream Processing frameworks including Apache Spark, Flink, and Storm. Also, it measures the resource usage and performance scalability of the frameworks against a varying number of cluster sizes. It has been observed that, Flink outperforms both Spark and Storm under equal constraints. However, Spark can be optimized to provide the higher throughput than Flink with the cost of higher latency.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.Conference Object Systematic Mapping for Big Data Stream Processing Frameworks(Ieee, 2016) Alayyoub, Mohammed; Yazici, Ali; Karakaya, ZiyaThere 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.
