Karakaya, Ziya

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Karakaya, Z
K.,Ziya
Ziya, Karakaya
K., Ziya
Z., Karakaya
Karakaya,Z.
Karakaya, Ziya
Z.,Karakaya
Job Title
Doktor Öğretim Üyesi
Email Address
ziya.karakaya@atilim.edu.tr
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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Scholarly Output

40

Articles

4

Citation Count

95

Supervised Theses

13

Scholarly Output Search Results

Now showing 1 - 10 of 40
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    Conference Object
    Citation Count: 22
    A Comparison of Stream Processing Frameworks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Yazıcı, Ali; Yazici,A.; Karakaya, Ziya; Software Engineering; Computer Engineering
    This 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. © 2017 IEEE.
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    Citation Count: 4
    Software engineering issues in big data application development
    (Institute of Electrical and Electronics Engineers Inc., 2017) Karakaya, Ziya; Computer Engineering
    Big 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. © 2017 IEEE.
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    Conference Object
    Citation Count: 3
    Software Engineering Issues in Big Data Application Development
    (Ieee, 2017) Karakaya, Ziya; Computer Engineering
    Big 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.
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    Citation Count: 0
    Informatics Engineering Education in Turkey and Expectations of Software Industry;
    (Institute of Electrical and Electronics Engineers Inc., 2018) Yazıcı, Ali; Mishra,A.; Karakaya, Ziya; Üstünkök, Tolga; Mıshra, Alok; Software Engineering; Computer Engineering
    In this study, using the ÖSYM 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. © 2018 IEEE.
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    Citation Count: 4
    Need for a Software Development Methodology for Research-Based Software Projects
    (Institute of Electrical and Electronics Engineers Inc., 2018) Cereci, İbrahim; Karakaya, Ziya; Computer Engineering
    Software 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. © 2018 IEEE.
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    Citation Count: 1
    Big Data on Cloud for Government Agencies: Benefits, Challenges, and Solutions
    (Assoc Computing Machinery, 2018) Yazıcı, Ali; Karakaya, Ziya; Karakaya, Ziya; Software Engineering; Computer Engineering
    Big 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.
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    Citation Count: 7
    Normalizing relational database schemas using mathematica
    (Springer Verlag, 2006) Yazıcı, Ali; Karakaya,Z.; Karakaya, Ziya; Software Engineering; Computer Engineering
    In this paper, basic relational database (DB) normalization algorithms are implemented efficiently as Mathematica modules. It was observed that, Mathematica provided a straightforward platform as opposed to previous ones, mainly Prolog based tools which required complex data structures such as linked list representations with pointers. A Java user interface called JMath-Norm was designed to execute the Mathematica modules in a systematic way. For this purpose, Mathematical Java link facility (JLink) is utilized to drive the Mathematica kernel. JMath-Norm provides an effective interactive tool in an educational setting for teaching DB normalization theory. © Springer-Verlag Berlin Heidelberg 2006.
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    Article
    Citation Count: 11
    Teaching Parallel Computing Concepts Using Real-Life Applications
    (Tempus Publications, 2016) Yazıcı, Ali; Mıshra, Alok; Karakaya, Ziya; Computer Engineering; Software Engineering
    The 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.
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    Citation Count: 2
    Systematic 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; Yazıcı, Ali; Karakaya, Ziya; Software Engineering; Computer Engineering; Computer Engineering; Software Engineering; Computer Engineering
    There 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.
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    Master Thesis
    Sosyal Medyada Duygu Analizi : Karşılaştırmalı Bir Çalışma
    (2018) Gebreyesus, Yasmın Tesfaldet; Karakaya, Ziya; Karakaya, Ziya; Yazıcı, Ali; Computer Engineering
    Duygu Analizi, sosyal medya gönderileri gibi metin içeriğinin polaritesini tespit etme ve sınıflandırma görevidir. Twitter için duyarlılık analizi, çalışmaların açık veri setlerini kullanarak yürütüldüğü akademik çevrelerde popüler bir konu olmuştur. Mevcut son teknoloji ürünü sonuçlar, Destek Vektör Makineleri (SVM) gibi klasik Makine Öğrenme sınıflandırıcıları ve Sinir Ağları, yani Derin Öğrenme modelleri gibi son gelişmeler dahil olmak üzere çok çeşitli tekniklerle sağlanmıştır. Bu tezde, Büyük Veri çerçevelerini kullanarak Sosyal Medya için büyük ölçekli Duygu Analizi çalıştık. Motivasyonumuz, büyük veri kriterlerinin sınıflandırıcıların performansı üzerindeki etkisini araştıran çalışmaların çok az olduğu gözleminden kaynaklanmaktadır. Amaç, sadece son teknoloji ürünü sonuçlardan daha iyi performans gösteren bir model oluşturmak değil, gerçek zamanlı ve yüksek hacimli veri akışları altında çeşitli sınıflandırıcı algoritmalarını incelemektir. Bu amaçla, büyük veri çerçeveleri olan ve içermeyen çeşitli Duygu Analizi Modelleri uygularız ve büyük veri yapılarını kullanarak performans faydalarını veya kayıplarını karşılaştırırız. Özellikle iki deneme senaryosu oluşturduk. Her iki senaryoda, aynı veri kümesini kullanıyoruz, ilgili sınıflandırıcılar için mümkün olan en iyi sonuçları elde etmek için uygun veri ön işlemlerini ve özellik mühendisliği tekniklerini uyguluyoruz. Anahtar Kelimeler: Algı Analizi, Büyük Veri, Spark, Spark ML, Twitter, Derin Öğrenme, Twitter