Pekin Erhan Eren

E-mail

ereren@metu.edu.tr

Department

Graduate School of Informatics

ORCID

0000-0003-4354-3358

Scopus Author ID

6603471003

Web of Science Researcher ID

ABA-4438-2020

Data-driven alarm parameter optimization Eylen, Tayfun; Eren, Pekin Erhan; Koçyiğit, Altan (2025-05-01) Most manufacturing sector businesses utilize advanced control mechanisms to sustain their ongoing operations. An alarm management system is one of these control mechanisms that works as a safety barrier, and it contains al...
CLOUD-QM: a quality model for benchmarking cloud-based enterprise information systems Şener, Umut; GÖKALP AYDIN, EBRU; Eren, Pekin Erhan (2024-01-01) Organizations are increasingly migrating from on-premise enterprise information systems (EIS) to cloud products due to cloud computing benefits, such as flexibility, elasticity, and on-demand service. However, identifying ...
Developing a Strategic Data Science Roadmap through Trend Analysis and Digital Maturity Assessment Nazlıel, Kerem; Kayabay, Kerem; Gokalp, Mert Onuralp; Aydin, Ebru Gokalp; Eren, Pekin Erhan; Koçyiğit, Altan (2024-01-01) The rapid advancements in Data Science and Artificial Intelligence require organizations to create strategic roadmaps that align with both technological trends and business objectives. This paper introduces a data-driven a...
Increasing the collaboration of data science stakeholders with a knowledge management system Civelek, Utku; Eren, Pekin Erhan; Gökalp, Mert Onuralp (2024-01-01) Purpose: This paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise. Design/methodology/approa...
What Drives Success in Data Science Projects: A Taxonomy of Antecedents Gökay, Gonca Tokdemir; Nazlıel, Kerem; Şener, Umut; GÖKALP AYDIN, EBRU; Gökalp, Mert Onuralp; Gençal, Nergiz; Dağdaş, Gizemnur; Eren, Pekin Erhan (2023-01-01) Organizations have been trying to reshape their business processes and transform them into a smart environment to attain sustainable competitive advantage in their markets. Data science enables organizations to define inte...
Edge Computing Applications in Industrial IoT: A Literature Review Bayar, Alp; Şener, Umut; Kayabay, Kerem; Eren, Pekin Erhan (2023-01-01) The Industrial Internet of Things (IIoT) covers many computing devices and sensors constantly generating and exchanging data in a complicated network. Ongoing research tries to fit decentralized edge computing architecture...
Determinative Factors of Cloud Computing Adoption in Government Organizations Koç, Burcu; Şener, Umut; Eren, Pekin Erhan (2022-12-29) Digital transformation in government organizations has led to the spread of e-government applications. Accordingly, government institutions face many challenges, such as the lack of highly skilled IT personnel and resource...
A Comprehensive Big Data Framework for Energy Markets: Oil and Gas Demand Forecasting Bayar, Alp; Koç, Burcu; Gökalp, Mert Onuralp; Özden, Baran; Yeldan, Yiğit; Koçyiğit, Altan; Eren, Pekin Erhan (2022-12-29) Demand forecasting in the energy sector is essential for both countries and companies to plan their supply and demand. Agents in the highly volatile oil markets have to act fast and data-driven. In the literature, studies ...
A process assessment model for big data analytics Gökalp, Ebru; Gökalp, Mert Onuralp; Kayabay, Kerem; Gökalp, Selin; Koçyiğit, Altan; Eren, Pekin Erhan (2022-03-01) Big data analytics (BDA) grasp the potential of generating valuable insights and empowering businesses to support their strategic decision-making. However, although organizations are aware of BDAs’ potential opportunities,...
Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization Kayabay, Kerem; Gökalp, Mert Onuralp; Eren, Pekin Erhan; Koçyiğit, Altan (2022-01-01) Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, a...

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