VERİ, BÜYÜK VERİ VE İŞLETMECİLİK

Yıl 2016, , 137 - 154, 01.06.2016

Suat Atan

https://doi.org/10.31795/baunsobed.645312

Cited By: 7

Öz

Büyük veri verinin miktarının çokluğu ve çeşitliliği çağrışımını yapsa da özünde verinin değerinin yeniden keşfi sonrasında geleneksel veri analizi perspektifi yerine yeni araç ve yaklaşımlarla aslında verinin yeniden keşfedilmesini ifade eden trendin adıdır. Büyük veri işletmeler için çok ciddi bir kaynak teşkil edebilir hatta işletmelerin bizzat işi haline dönüşebilir. Bu çalışmada Büyük veri trendine kadar gerçekleşen son gelişmeler ve işletmeler ile ilişkisi ele alınacaktır

Anahtar Kelimeler

Veri Madenciliği, Makine Öğrenmesi, Dev Veri

Data, Big Data and Business Administration

Öz

The term of big data connotates the abundance of data and properties of it. However, after re-discovering the value of data, the term of big data reflects the new trend which includes new approaches and tools rather than the sophisticated methods. On the other words, big data is the constant rediscovering of data. The big data can provide fruitful resources for the corporates. Even it can be the core task of a corporate. By the way, in this study, the recent advances in the field and case studies have been discussed. The perspectives of corporates to data have also been evaluated

Anahtar Kelimeler

Data Mining, Machine Learning, Big Data

Kaynakça

• Aksoy, C. (Ocak-Şubat 2014). Müşteriye Daha Yakın Olmak. Harvard Business Review Türkiye, 96-101.
• Dieck, R. (2007). Measurement Uncertainty Methods and Applications, the Instru- mentation. (4. Bs.). New York: Systems and Automation Society (ISA).
• Ganji, V. R. (2012). Credit Card Fraud Detection Using Anti k-Nearest Algorit- hm. International Journal on Computer Science and Engineering, 4(6), 1035- 1039.
• Grimes, S. (2005). Structure, Models and Meaning. InformationWeek. 20 Mart 2016 tarihinde http://informationweek.com/software/business-intelli- gence/structure-models-and- meaning/59301538 adresinden erişildi.
• Gigerenzer, G. (2014). Risk Savvy: How to Make Good Decisions. (1. B.s). New York: Penguin.
• Hand, D. J. (1999). Statistics and Data Mining: Intersecting Disciplines. ACM SIGKDD Explorations Newsletter, 1(1), 16–19.
• Hurwitz, J., Nugent, A., Halper, F. ve Kaufman, M. (2013). Big Data For Dum- mies. (1. Bs.). New Jersey: John Wiley & Sons.
• Lantz, B. (2013). Machine Learning with R. (1. Bs.). Birmingham: Packt Publis- hing Ltd.
• Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Va- riety. META Group Araştırma Raporu, 20 Mayıs 2016 tarihinde https:// blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Manage- ment-Controlling-Data-Volume-Velocity-and-Variety.pdf adresinden erişildi.
• Mayer-Schönberger, V., ve Cukier, K. (2013). Big data: A Revolution that will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
• Mayo, E. (1933). The Human Problems of an Industrial Civilization,. New York: Routledge Taylor&Francis Group.
• O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C. ve Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183– 188.
• Phua, C., Lee, V., Smith, K. ve Gayler, R. (2012). A Comprehensive Survey of Data Mining-based Fraud Detection Research. Computers in Human Be- havior, 28(3), 1002–1013.
• Salminen, J. ve Kaartemo, V. (Ed.). (2014). Big Data: Definitions, Business Logics, and Best Practices to Apply in Your Business. New York: Amazon
• Varmuza, K. ve Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics (1.Bs.). Florida: CRC Press.
• Ward, J. S., ve Barker, A. (2013). Undefined by data: a survey of big data defi- nitions. arXiv preprint arXiv:1309.5821.