Makrufa Sh. Hajirahimova

Work place: Institute of Information Technology of Azerbaijan National Academy of Sciences, B. Vahabzade str., Baku, AZ1141, Azerbaijan

E-mail: makrufa@science.az

Website: https://orcid.org/0000-0003-0786-5974

Research Interests: Data Structures and Algorithms, Network Security, Network Architecture, Information Security, Computer Architecture and Organization

Biography

Makrufa Sharif Hajirahimova is an associate professor, project chief engineer at the Institute of Information Technology of Azerbaijan National Academy of Sciences. She teaches at the Training Innovation Center of the institute.  She defended the thesis on the “Development of models and algorithms for intelligent management of text document in e-government” at the Institute of Information Technology of Azerbaijan National Academy of Sciences and received PhD in Technical sciences in 2013. Currently she conducts research on the "Big Data", and is actively involved in the development of the "e-Science" software project under the "E-Azerbaijan" program.  

Author Articles
Evaluation of Oil Viscosity Based Various Empirical Correlations for Azerbaijan Crude Oils

By Yadigar N. Imamverdiyev Makrufa Sh. Hajirahimova

DOI: https://doi.org/10.5815/ijitcs.2019.06.03, Pub. Date: 8 Jun. 2019

In the oil industry, the evaluation of oil viscosity is one of the important issues. Generally, the viscosity of crude oil depends on pressure and temperature. In this study, the prediction issue of oil viscosity has been viewed applying empirical correlations as Beggs-Robinson, Labedi, modified Kartoatmodjo, Elsharkawy and Alikhan, Al-Khafaji. Original field data reports have been obtained from Guneshli oil field of Azerbaijan sector of Caspian Basin. The correlation models used in the evaluation of viscosity of Azerbaijan oil have been implemented in the Python software environment. The obtained values on empirical correlations have been compared to experimental data obtained from Guneshli oil field. Statistical analysis in terms of percent absolute deviation (% AD) and the percent absolute average deviation (% AAD), mean absolute error (% MAE), correlation coefficient (% ), root mean square error (% RMSE) are used to subject the evaluation of the viscosity correlations. According to statistical analysis, it has been known that the Beggs-Robinson model has shown the lowest value on AAD (10.5614%), MAE (12.4427 %), RMSE (20.0853 %). The Labedi model has presented the worst result on every four criterions. Even though the Elsharkawy-Alikhan model has presented the highest result (99.9272%) on correlation coefficient, in the evaluation of viscosity of Azerbaijan crude oil, the Beggs-Robinson model can be considered more acceptable.

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About Big Data Measurement Methodologies and Indicators

By Makrufa Sh. Hajirahimova Aybeniz S. Aliyeva

DOI: https://doi.org/10.5815/ijmecs.2017.10.01, Pub. Date: 8 Oct. 2017

The digitization of nearly all media and the increasing migration of social and economic activities to the ─░nternet, the development of social networking technologies, the ─░nternet of Things and cloud computing caused rapid increase in the volume of data and the formation of Big Data paradigm. Big Data involves technologies and tools for collecting, processing, analyzing and extracting useful knowledge from structured and unstructured data of large volumes generated at high speed by different sources. Increasing the volume, speed, diversity and value of Big Data began to play an important role in the creation of social relationships, competitive advantage and innovative fields. The development of the information society, the formation of digital economy, and the application Big Data technologies in different spheres of human activity required the quantitative and qualitative assessment of Big Data. In this article some approaches relate to the definition of Big Data have been reviewed. Methodological approaches and indicators for measuring Big Data have been researched. At the end, the indicators have been proposed for the measurement of factors that affected the growth and development of Big Data.

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