2022 [ to top ]

1.
Gökalp, M.O., Gökalp, E., Kayabay, K., Gökalp, S., Koccyiugit, A., Eren, P.E.: A process assessment model for big data analytics. Computer Standards & Interfaces. 80, 103585 (2022).
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2.
Kayabay, K., Gökalp, M.O., Gökalp, E., Eren, P.E., Koçyiğit, A.: Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization. Technological Forecasting and Social Change. (2022).
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3.
Nazliel, K., Kayabay, K., Gokalp, M.O., Gokalp, E., Eren, E.: Data Science Technology Selection: Development of a Decision-Making Approach. 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE). IEEE (2022).
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4.
Kayabay, K., Sari, O., Demirtas, M., Bostan, S., Ortakaya, B.: Development of a Maturity Assessment Tool to Improve SME HPC Capabilities. Zenodo (2022).
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5.
Gökalp, M.O., Gökalp, E., Kayabay, K., Koçyiğit, A., Eren, P.E.: The development of the data science capability maturity model: a survey-based research. Online Information Review. (2022).
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2021 [ to top ]

1.
Gökalp, M.O., Kayabay, K., Gökalp, E., Koccyiugit, A., Eren, P.E.: Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach. IET Software. (2021).
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2.
Gökalp, M.O., Gökalp, E., Kayabay, K., Koccyiugit, A., Eren, P.E.: Data-driven manufacturing: An assessment model for data science maturity. Journal of Manufacturing Systems. 60, 527–546 (2021).
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3.
Gökalp, E., Kayabay, K., Gökalp, M.O.: Leveraging Digital Transformation Technologies to Tackle COVID-19: Proposing a Privacy-First Holistic Framework. In: Arpaci, I., Al-Emran, M., A. Al-Sharafi, M., und Marques, G. (Hrsg.) Emerging Technologies During the Era of COVID-19 Pandemic. S. 149–166. Springer International Publishing, Cham (2021).
[ Abstract ] [ URL ]
 
The COVID-19 outbreak has caused unprecedented shocks to public health and economies around the world. The emergent technologies creating digital transformation across all industries have started to transform businesses in the last years. Although utilized by the healthcare industry before COVID-19, some of these technologies gained significant attention since they can alleviate the pandemic crisis. A holistic framework can still help (1) understand the relationship between fragmented technology solutions, (2) figure out how to combine fragmented solutions, (3) integrate data sources, and (4) introduce integrated digital solutions. Correspondingly, we explore the use of emergent technologies by proposing a privacy-first holistic framework by integrating digital transformation technologies to tackle the COVID-19 pandemic. The framework consists of data sources, technologies, users, applications for diagnosis, treatment, and prevention, and users. We then discuss the benefits and challenges of the framework. The proposed framework aims to provide a more efficient and dynamic healthcare system to reduce the death toll, the risk of virus transmission, and healthcare givers’ workload and stress levels.

2020 [ to top ]

1.
Kayabay, K., Gokalp, M.O., Gokalp, E., Eren, P.E., Kocyigit, A.: Data Science Roadmapping: Towards an Architectural Framework. 2020 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE (2020).
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2.
Gökalp, M.O., Kayabay, K., Gökalp, E., Koccyiugit, A., Eren, P.E.: Towards a Model Based Process Assessment for Data Analytics: An Exploratory Case Study. Communications in Computer and Information Science. S. 617–628. Springer International Publishing (2020).
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2019 [ to top ]

1.
Kayabay, K., Gokalp, M., Eren, P., Kocyigit, A.: A Workflow and Cloud Based Service-Oriented Architecture for Distributed Manufacturing in Industry 4.0 Context. Proceedings - IEEE 11th International Conference on Service-Oriented Computing and Applications, SOCA 2018. 88–92 (2019).
 

2.
Gökalp, M.O., Kayabay, K., Zaki, M., Koçyiğit, A., Eren, P.E., Neely, A.: Open-Source Big Data Analytics Architecture for Businesses. 2019 1st International Informatics and Software Engineering Conference (UBMYK). S. 1–6 (2019).
[ Abstract ]
 
Unaware of existing big data technologies, organizations fail to develop a big data capability despite its disruptive impact on today's competitive business environment. To determine the shortcomings and strengths of developing a big data architecture with open-source tools from technical and managerial perspectives, this study (1) systematically reviews the available open-source big data technologies to present a comprehensive picture, and (2) proposes an open-source architecture for businesses to take as a reference while developing big data analytics capabilities. Lastly, we discuss technical, domain-specific, and firm-specific soft challenges related to establishing a big data architecture in an organization, and how these challenges are reshaping the big data research domain.

2017 [ to top ]

1.
Akyol, M., G"okalp, M., Kayabay, K., Eren, P., Ko?yi?it, A.: A context aware notification architecture based on distributed focused crawling in the big data era. Lecture Notes in Business Information Processing. 299, 29–39 (2017).
 

2.
Gokalp, M., Kayabay, K., Akyol, M., Eren, P., Kocyigit, A.: Big data for Industry 4.0: A conceptual framework. Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016. 431–434 (2017).