High performance computing for big data analytics: solution environments and coding

Year 2021, Volume: 2 Issue: 2, 66 - 71, 29.06.2021

Prof. Dr. Hamza Erol

Abstract

Stream or multilayered big data, size and complexity is defined in this study. High performance computing systems used in big data analytics; Single processor - single core structure: standard computing architecture; Single processor - multi core structure: parallel computing architecture and Multi processor - multi core structure: distributed computing architecture are explained. Computing environments used in these systems have been examined. Hardware computing or hardware acceralated computing and software computing or software optimized computing are emphasized. Learning methods applied in big data analytics: statistical learning, machine learning and deep learning were expressed. Artificial intelligence has been explained as the result or product-oriented applications of these learning methods. Google Colabratory as a web-based solution environment in stream or multilayered big data analytics and Python applications for code development in this environment have been given.

Keywords

Big data analytics, High performance computing, Learning methods

Büyük veri analitiği için yüksek performans hesaplama: çözüm ortamları ve kodlama

Abstract

Bu çalışmada akan veya katmanlı büyük veri, hacmi ve karmaşıklığı tanımlanmıştır. Büyük veri analitiğinde kullanılan; Tek işlemci - tek çekirdek yapısı: standart hesaplama mimarisi; Tek işlemci - çok çekirdek yapısı: paralel hesaplama mimarisi ve Çok işlemci - çok çekirdek yapısı: dağıtık hesaplama mimarisi biçimlerindeki yüksek performans hesaplama sistemleri açıklanmıştır. Bu sistemlerde kullanılan hesaplama ortamları incelenmiştir. Donanım hesaplama ya da donanım hızlandırılmış hesaplama ve Yazılım hesaplama ya da yazılım optimize edilmiş hesaplama konuları vurgulanmıştır. Büyük veri analitiğinde uygulanan öğrenme yöntemleri: istatistiksel öğrenme, makine öğrenme ve derin öğrenme ifade edilmiştir. Bu öğrenme yöntemlerinin sonuç veya ürün odaklı uygulamaları olarak yapay zeka açıklanmıştır. Akan veya katmanlı büyük veri analitiğinde web tabanlı çözüm ortamı olarak Google Colabratory ve bu ortamda kod geliştirmede Python uygulamaları verilmiştir.

Keywords

Büyük veri analitiği, Yüksek performans hesaplama, Öğrenme yöntemleri

References

• Gökalp MO, Kayabay K, Akyol MA, Eren PE and Koçyiğit A. (2016). Bıg Data For Industry 4.0: A Conceptual Framework. 2016 International Conference on Computational Science and Computational Intelligence. 978-1-5090-5510-4/16 10.1109/CSCI.2016.87.
• Hamza Erol ve Recep Erol (2018). Determining Big Data Complexity Using Hierarchical Structure of Groups and Clusters in Decision Tree. 2018 3rd International Conference on Computer Science and Engineering (UBMK).
• Hamza EROL ve Timuçin KORKMAZ (2020). Büyük Veri Analitiği İçin Yüksek Performans Hesaplama Sistemi Gibi Davranan Bir Dağıtık Bilgisayar Sistemi Mimarisi. Bilgisayar Bilimleri ve Teknolojileri Dergisi - DergiPark. Yıl 2020, Cilt 1, Sayı 2, Sayfalar: 74-81.
• Vassakis K, Petrakis E and Kopanakis I (2018). Big Data Analytics: Applications, Prospects and Challenges. Mobile Big Data, Lecture Notes on Data Engineering and Communications Technologies 10, Springer International Publishing AG, https://doi.org/10.1007/978-3-319-67925-9_1.
• Thelin R (2020). What is Big Data? Characteristics, Types, and Technologies. https://www.educative.io/blog/what-is-big-data.
• İnternet Kaynağı 1 (2018). Yuval Noah HARARİ 48. Dünya Ekonomik Formu. https://www.weforum.org/events/world-economic-forum-annual-meeting-2018/sessions/will-the-future-be-human.
• İnternet Kaynağı 2 (2020). https://ec.europa.eu/esco/portal/escopedia/List_of_sectors_of_economic_activities_for_the_development_of_ESCO_v1.
• Hamza Erol, Bala Mikat Tyoden, Recep Erol (2018). Classification Performances Of Data Mining Clustering Algorithms For Remotely Sensed Multispectral Image Data. 2018 Innovations in Intelligent Systems and Applications (INISTA).
• Hamza Erol, Celaleddin Barutçular, Ayman El Sabagh, Recep Erol (2017). Data Mining Models for Selection of the Best Spectral Reflectance Indices in Estimation of Crop Yields and Classification of Maize Hybrid Types Using SpectroRadiometer Data. 2017 European Conference on Electrical Engineering and Computer Science (EECS).
• İnternet Kaynağı 3 (2019). https://www.weforum.org/events/world-economic-forum-annual-meeting-2019.
• İnternet Kaynağı 4 (2018). https://eab.com/insights/daily-briefing/workplace/the-top-10-emerging-jobs-for-2022/
• İnternet Kaynağı 5 (2020). https://www.weforum.org/reports/the-future-of-jobs-report-2020.
• Timuçin Korkmaz, Hamza Erol (2020). Classification Of Human Facial Expressions For Emotion Recognition Using A Distributed Computer System. 2020 5th International Conference on Computer Science and Engineering (UBMK).
• Osman Doğuş GÜLGÜN and Hamza EROL (2020) (a). Classification Performance Comparisons Of Deep Learning Models In Pneumonia Diagnosis Using Chest X-Ray Images. Yayın Bilgisi: 2020, Turkish Journal of Engineering. DOI: 10.31127/tuje.652358.
• Osman Doğuş GÜLGÜN and Hamza EROL (2020) (b). Medical image classification with hybrid convolutional neural network models. Bilgisayar Bilimleri ve Teknolojileri Dergisi - DergiPark. Yıl 2020, Cilt 1, Sayı 1, Sayfalar: 28-41.
• Hamza EROL, Recep EROL (2020). Reliability And Chaotic Risk Modeling For Real Time Data Driven Smart Systems. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT).
• Hamza Erol and Recep Erol (2016). Logical circuit design using orientations of clusters in multivariate data for decision making predictions: A data mining and artificial intelligence algorithm approach. 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).
• İnternet Kaynağı 6 (2021). https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb#scrollTo=0XcQWHWPCZpc.
• İnternet Kaynağı 7 (2021). https://colab.research.google.com/github/mdai/ml-lessons/blob/master/lesson1-xray-images-classification.ipynb#scrollTo=Hk7H4FqrGCTM.
• İnternet Kaynağı 8 (2009). Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, https: //www.cs.toronto .edu / ~ kriz / cifar.html.
• İnternet Kaynağı 9 (2019). https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/cnn.ipynb#scrollTo=DSPCom-KmApV.
• İnternet Kaynağı 10. https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb#scrollTo=GU8C5qm_4vZb.
• İnternet Kaynağı 11 (2016). http://patrickgray.me/open-geo-tutorial/chapter_6_neural_networks.html.
• İnternet Kaynağı 12 (2015). https://doi.org/10.1038/nature14539.
• İnternet Kaynağı 13 (2017). https://doi.org/10.1109/MGRS.2017.2762307
• İnternet Kaynağı 14 (2018). https://doi.org/10.1109/TGRS.2018.2872509.
• İnternet Kaynağı 15 (2016). https://doi.org/10.1109/TGRS.2016.2612821.
• İnternet Kaynağı 16 (2019). https://doi.org/10.3390/rs11070768.