Büyük Veri Şirketleri ve Açık Kaynak Hareketi

Yıl 2021, Sayı: 21, 680 - 689, 31.01.2021

Necmi Gürsakal , Sevda Gürsakal , Sadullah Çelik

Öz

Bu çalışmanın amacı, açık kaynak kodlu yazılımların büyük veri şirketleri tarafından amaçları dışında kötüye kullanılabileceğini tartışmaktır. Son yıllarda bilişim ve iletişim teknolojilerinde yaşanan gelişmeler Büyük Veri ve açık kaynak kodlu yazılımların kullanımını artırmıştır. R, Python, Hadoop, Spark, MapReduce gibi açık kaynak kodlu yazılımlar çok sayıda kişi tarafından geliştirilmekte ve bunlar Büyük Veri, Veri Bilimi, Yapay Zeka, Nesnelerin İnterneti ve Blok Zincir gibi birçok teknolojide kullanılmaktadır. Makine öğrenmesi ve derin öğrenme gibi Büyük Veri’ye değer katan yaklaşımlar açısından da, açık kaynak kodlu yazılımların önemi büyüktür. Bu yazılımların kaynak kodları herkese açıktır ve bunlara herkes katkıda bulunup istediği amaç doğrultusunda ücretsiz kullanabilir. Bugün Apple, Amazon, Google, Facebook, Microsoft, Samsung, Yahoo ve Qualcomm gibi birçok büyük veri şirketi, makine öğrenmesini hızlandırmak ve yazılıma uygun donanım geliştirmek için yoğun çalışmalar yapmaktadır. Ayrıca büyük veri şirketleri, TODO Group’u kurarak açık kaynak kodlu yazılım bilgilerini birbirleriyle paylaşmaya başlamışlardır. Ne yazık ki, paylaşımı, özveriyi amaçlayan açık kaynak hareketi; büyük veri şirketlerinin organize hareketleri karşısında onlara hizmet eden romantik bir çabaya dönüşmeye başlamıştır. Amacı ücretsiz, güvenilir ve kaliteli yazılımı herkese sunmak olan açık kaynak kodlu yazılım hareketi; büyük veri şirketleri tarafından (hareketin amaçları dışında) kâr amacıyla kullanılmaktadır. Diğer taraftan, açık kaynak kodlu yazılım hareketi bilginin hızla yayılımı, üretilen kodların herkes tarafından kullanımı ve paylaşılması açısından da büyük öneme sahiptir. Büyük veri şirketleri hareketi önce yazılım geliştirme amacıyla kullanmakta, daha sonra ise bu yazılımı ücretli hale getirmektedirler. Microsoft, ağların görselleştirilmesinde kullanılan NodeXL programında bunu yapmıştır.

Anahtar Kelimeler

Büyük Veri, Büyük Veri Şirketleri, Açık Kaynak Kodlu Yazılım

Big Data Companies and Open Source Movement

Öz

The purpose of this study is to discuss the misuse of open source software by big data companies for other reasons. Developments in information and communication technologies in recent years have increased the use of Big Data and open source software. Open source software such as R, Python, Hadoop, Spark, MapReduce are developed by many people and these are used in many technologies such as Big Data, Data Science, Artificial Intelligence, Internet of Things and Blockchain. Open source software is also of great importance in terms of approaches that add value to Big Data such as machine learning and deep learning. The source code of these software is open to everyone and everyone can contribute and use them for free for their desired purpose. Today, many big data companies such as Apple, Amazon, Google, Facebook, Microsoft, Samsung, Yahoo and Qualcomm are working hard to accelerate machine learning and develop hardware suitable for software. Also, big data companies have started to share their open source software information by establishing the TODO Group. Unfortunately, the open source movement aimed at sharing, devotion; has begun to turn into a romantic effort that serves big data companies in the face of organized movements. The open source software movement whose aim is to provide free, reliable and quality software to everyone; Used by big data companies for profit (other than the purposes of the movement). On the other hand, the open source software movement is of great importance in terms of the rapid spread of information, the use and sharing of the produced codes by everyone. Big data companies first use the movement for software development and then make this software for a fee. Microsoft has done this in the NodeXL program, which is used for visualizing networks.

Anahtar Kelimeler

Big Data, Big Data Companies, Open Source Software

Kaynakça

• Alanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017). A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. Journal of Medical Systems, 41(4), 69.
• Anthes, G. (2016). Open Source Software No Longer Optional. Communications of the ACM, 59(8), 15-17.
• Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep Machine Learning-a New Frontier in Artificial Intelligence Research [research frontier]. IEEE Computational Intelligence Magazine, 5(4), 13-18.
• Asay, M. (2016). Why Linux Creator Linus Torvalds Doesn't Really Care About Open Source. 2016, February 22. Retrieved from https://www.techrepublic.com/article/linux-creator-linus-torvalds-doesnt-really-care-about-open-source/. 12.03.2020.
• Asay, M. (2017, March 30). BIG Open-Source Love Microsoft and Google? You Still Won't Catch AWS. Retrieved from https://www.theregister.co.uk/2017/03/30/doe_open_sourcey_ness_in_cloud_matter/. 10.03.2020.
• AWS (2020). Open Source at Amazon. Retrieved from https://aws.amazon.com/tr/opensource/?opensource-all.sort-by=item.additionalFields.startDate&opensource-all.sort-order=asc. 15.04.2020.
• Baack, S. (2015). Datafication and Empowerment: How the Open Data Movement Rearticulates Notions of Democracy, Participation, and Journalism. Big Data & Society, 2(2), 2053951715594634.
• BrainyQuote (2020). Linus Torvalds Quotes. Retrieved from https://www.brainyquote.com/quotes/linus_torvalds_137861. 12.03.2020.
• Carillo, K., & Okoli, C. (2008). The Open Source Movement: a revolution in software development. Journal of Computer Information Systems, 49(2), 1-9.
• Celińska, D. (2016). Why Do Users Choose Open Source Software? Analysis of the Network Effect. Informatyka Ekonomiczna, 39(1), 9-22.
• Chambers, M., Doig, C., & Stokes-Rees, I. (2017). Breaking Data Science Open. O'Reilly Media, Incorporated.
• Charny, B. (2002, January 2). Microsoft Raps Open Source Approach. Retrieved from https://www.cnet.com/news/microsoft-raps-open-source-approach/. 12.05.2020.
• Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE access, 2, 514-525.
• Cimpanu, C. (2017, June 2). Hadoop Servers Expose over 5 Petabytes of Data. Retrieved from https://www.bleepingcomputer.com/news/security/hadoop-servers-expose-over-5-petabytes-of-data/. 12.05.2020.
• Crego, E., Munoz, G. & Islam, F. (2013, July 26). Big Data and Deep Learning: Big Deals or Big Delusions? Retrieved from https://www.huffpost.com/entry/big-data-and-deep-learnin_b_3325352. 01.09.2020.
• Çelik, S. (2018). Büyük Veri. Gece Kitaplığı. Ankara. ISBN: 978-605-288-811-7.
• Eghbal, N. (2016). Roads and Bridges: The Unseen Labor Behind our Digital Infrastructure. Ford Foundation.
• Finley, K. (2016, August 11). Open Source Won. So, Now What? Retrieved from https://www.wired.com/2016/08/open-source-won-now/. 12.05.2020.
• Friedman, T. L. (2007). The World is Flat 3.0: A Brief History of the Twenty-first Century/Thomas L. Friedman. NY.: Picador.
• Gonzalez-Barahona, J. M., Izquierdo-Cortazar, D., Maffulli, S., & Robles, G. (2012). Using Software Analytics to Understand How Companies Interact in Free Software Communities.
• IBM (2013). IBM White Paper. Big Data for the Intelligence Community. Retrieved from https://pdfs.semanticscholar.org/6bff/f82a993eab399a84dd82081977a8e1fcba57.pdf. 12.05.2020.
• Jackson, M. (2018, December 19). Quantum Computing Progress Will Speed up Thanks to Open Sourcing. Retrieved from https://singularityhub.com/2017/01/28/quantum-computing-progress-will-speed-up-thanks-to-open-sourcing/. 20.06.2020.
• Jani, K. (2016). The Promise and Prejudice of Big Data in Intelligence Community. arXiv preprint arXiv:1610.08629.
• Johnson, B. (2008). Cloud Computing is a Trap, Warns GNU Founder Richard Stallman. The guardian, 29.
• Kawamoto, D. (2016, March 11). Ballmer: Linux no Longer a Cancer. Retrieved from https://www.informationweek.com/software/ballmer-linux-no-longer-a-cancer--/d/d-id/1324661. 13.07.2020.
• Kepes, B. (2013). Open Source is Good and All, But Proprietary is Still Winning.
• Li, Y., Wu, F. X., & Ngom, A. (2018). A Review on Machine Learning Principles for Multi-view Biological Data Integration. Briefings in Bioinformatics, 19(2), 325-340.
• Linåker, J., Rempel, P., Regnell, B., & Mäder, P. (2016, March). How Firms Adapt and Interact in Open Source Ecosystems: Analyzing Stakeholder Influence and Collaboration Patterns. In International Working Conference on Requirements Engineering: Foundation for Software Quality (pp. 63-81). Springer, Cham.
• Manyika, J., et. al. (2011). Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute. Retrieved from http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation. 10.10.2020.
• McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60-68.
• Mockus, A., Fielding, R. T., & Herbsleb, J. (2000, June). A Case Study of Open Source Software Development: The Apache Server. In Proceedings of the 22nd International Conference on Software Engineering (pp. 263-272).
• Moontechnolabs (2018, September 14). Apple vs Android - A Comparative Study 2017. Retrieved from https://www.moontechnolabs.com/apple-vs-android-comparative-study-2017/. 22.07.2020.
• Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M. et al. (2015). Deep Learning Applications and Challenges in Big Data Analytics. Journal of Big Data 2. https://doi.org/10.1186/s40537-014-0007-7.
• Paul, J. (2017). Microsoft is Now Using Linus Torvalds' Open Source Tool for Windows Development. Retrieved from https://itsfoss.com/microsoft-using-git/. 20.07.2020.
• Piatetsky, G. (2017). KDnuggets, Machine Learning Overtaking Big Data?. Retrieved from https://www.kdnuggets.com/2017/05/machine-learning-overtaking-big-data.html. 22.07.2020.
• Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347-1358.
• Robinson, S. (2019, January 30). Open Source Technology Will Influence the Future of Cloud. Retrieved from https://www.businessinsider.com/sc/open-source-technology-future-of-cloud-2019-1. 06.09.2020.
• Robles, G., González-Barahona, J. M., Izquierdo-Cortazar, D., & Herraiz, I. (2009). Tools for the Study of the Usual Data Sources Found in Libre Software Projects. International Journal of Open Source Software and Processes (IJOSSP), 1(1), 24-45.
• Scott, T. (2016). (United States Chief Information Officer), Rung E. Anne United States Chief Acquisition Officer, “M-16-21 Memorandum for the Heads of Departments and Agencies”, https://sourcecode.cio.gov/. 12.02.2020.
• Smith, JT. (2001, June 1). Microsoft's Ballmer: Linux is a Cancer. 1 June 2001. Retrieved from https://www.linux.com/news/microsofts-ballmer-linux-cancer. 25.02.2020.
• Söderberg, J. (2008). Hacking Capitalism: The Free and Open Source Software Movement.
• Swanner, N. (2019, August 8). Big Tech Controls Many Major Open Source Projects. Is that a Problem? Retrieved from https://insights.dice.com/2019/08/05/open-source-google-microsoft-apple-github/. 12.04.2020.
• Talend (2020). What is Big Data? [Free guide & definition]. (2020, July 22). Retrieved from https://www.talend.com/resources/future-big-data/. 05.09.2020.
• Tirole, J., & Lerner, J. (2000). The Simple Economics of Open Source.
• Tutorial (2020). Choosing Python or R for Data Analysis? An Infographic. Retrieved from https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis#gs.G2t5njE. 15.06.2020.
• Wakabayashi, D. (2019, December 15). Prime Leverage: How Amazon Wields Power in the Technology World. Retrieved from https://www.nytimes.com/2019/12/15/technology/amazon-aws-cloud-competition.html. 17.05.2020.
• Weinberger, M. (2016, December 21). The Whole 'Mac vs. PC' Thing is so Over, and 'Android vs. iPhone' is Close Behind. Retrieved from https://www.businessinsider.com/apple-mac-vs-microsoft-windows-pc-is-over-2016-12. 18.06.2020.
• Williams, S. (2002). Free as in Freedom (2.0)-Richard Stallman and the Free Software Revolution. Boston: The Free Software Foundation.
• Woodie, A. (2018). Weighing Open Source's Worth for the Future of Big Data. (2018, February 26). Retrieved from https://www.datanami.com/2018/02/26/weighing-open-sources-worth-future-big-data/
• Yu, D., & Deng, L. (2010). Deep Learning and its Applications to Signal and Information Processing [exploratory dsp]. IEEE Signal Processing Magazine, 28(1), 145-154.
• Zuech, R., Khoshgoftaar, T.M. & Wald, R. Intrusion Detection and Big Heterogeneous Data: a Survey. Journal of Big Data 2, 3 (2015). https://doi.org/10.1186/s40537-015-0013-4.