Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis tools require technical expertise which most of the organizations don't yet possess. Recently, the trend in the IT industry is towards developing prebuilt libraries and dataflow based programming models to abstract users from low-level complexities of these tools. After briefly analyzing trends in the literature and industry, this paper presents a conceptual framework which offers a higher level of abstraction to increase adoption of big data techniques as part of Industry 4.0 vision in future enterprises.


Joint Virtual Machine Embedding and Wireless Data Center Topology Management
Bütün, Beyza; Onur, Ertan; Department of Computer Engineering (2022-5-10)
With emerging technologies such as the Internet of Things and 5G, generated data grows enormously. Hence, Data Center Networks (DCNs) have an important duty to store and process a significant amount of data, which makes them a critical component of the network. To meet the massive amount of traffic demands, wired DCNs need to deploy large numbers of servers and power-hungry switches, and huge lengths of wires. An enormous increase in the usage of cables causes high cabling complexity and cost while deployin...
Data Science Roadmapping: Towards an Architectural Framework
KAYABAY, KEREM; Gökalp, Mert Onuralp; Gökalp, Ebru; Eren, Pekin Erhan; Koçyiğit, Altan (2020-11-24)
The availability of big data and related technologies enables businesses to exploit data for competitive advantage. Still, many industries face obstacles while leveraging data science to overcome business problems. This paper explores the development of a roadmapping approach to address data science challenges. Towards this goal, we customize technology roadmapping by synthesizing roadmapping, big data, data science, and data-driven organization literature. The resulting data science roadmapping approach li...
Spatio-temporal pattern and trend extraction on Turkish meteorological data
Goler, Isil; Karagöz, Pınar; Yazıcı, Adnan (2012-12-01)
Due to increasing amount of spatio-temporal data collected from various applications, spatio-temporal data mining has become a demanding and challenging research field requiring development of novel algorithms and techniques for successful analysis of large spatio-temporal databases. In this study, we propose a spatio-temporal mining technique and apply it on meteorological data, which has been collected from various weather stations in Turkey. In addition, we introduce one more mining level on the extracte...
Big data maturity models for the public sector: a review of state and organizational level models
OKUYUCU, ARAS; Yavuz, Nilay (Emerald, 2020-07-01)
Purpose Despite several big data maturity models developed for businesses, assessment of big data maturity in the public sector is an under-explored yet important area. Accordingly, the purpose of this study is to identify the big data maturity models developed specifically for the public sector and evaluate two major big data maturity models in that respect: one at the state level and the other at the organizational level. Design/methodology/approach A literature search is conducted using Web of Science an...
Oltulu, Orçun; Gökalp Yavuz, Fulya; Department of Statistics (2022-8-17)
Cost-efficient data collection and storage methods enable scientists, companies, and even regular computer users to reach high-dimensional data sets faster and cheaper. Even though personal computers are getting more powerful and efficient, some algorithms, tasks, and problems still require too much computational power and time to run on a personal computer. For a few decades, parallelization in statistical computing had an increasing trend, and researchers put significant effort into converting or adjustin...
Citation Formats
M. O. Gökalp, K. Kayabay, P. E. Eren, and A. Koçyiğit, “BIG DATA FOR INDUSTRY 4.0: A CONCEPTUAL FRAMEWORK,” 2016, Accessed: 00, 2020. [Online]. Available: