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Web 2.0’ın hayatımıza girmesi ve makineleşmenin artmasıyla birlikte farklı türdeki verilerin bir arada üretilmesi, depolanması ve paylaşılması mümkün hale gelmiştir. Dijital dünyadaki giderek artan devasa miktardaki veriye ve bu verinin analiz sürecine büyük veri denilmektedir. Bu çalışmanın amacı büyük veri kavramına kavramsal bir çerçeve çizerek, akademik çalışmalarda büyük veri kullanımının durumunu, tarih içindeki gelişimini ve büyük veri yılı olarak adlandırılann 2012 yılı öncesindeki ve sonrasındaki farklılıkları incemektir. Ayrıca, büyük veri konusunundaki Türkiye’de ve dünyada yapılan çalışmaların paralellik gösterip göstermediğini ortaya koymaktır. Yöntem olarak tarama araştırması benimsenmiştir. Bu bağlamda EbscoHost ASC ve Yükseköğretim Kurulu (YÖK) tez veritabanlarında yapılan araştırmayla birlikte 2012 sonrasında büyük veriyle alakalı yapılan akademik çalışmaların 2012 öncesine oranla keskin bir biçimde arttığı görülmüştür. Bununla birlikte “veri madenciliği” ile ilgili akademik çalışmaların sayısındaki artışların nispi düşüşü ise veri madenciliği alt dallarının daha spesifikleşmesiyle açıklanabilmektedir. Ayrıca paralel işleme modellerinden Map Reduce ve doğal dil işleme uygulamalarından fikir madenciliği yöntemlerinin son yıllarda ivme kazandığı gözlemlenmiştir. Bu durum Web 2.0’dan Web 3.0’a yani Etkileşimli Web’den Semantik Web’e geçiş sürecinde olduğumuzu göstermektedir.
With the entry of Web 2.0 into our lives and increasing machinery, it has become possible to produce, store and share different types of data together. In the digital world, the growing enormous amounts of data and the process of analysis of this data are called big data. The objective of this study is to draw a conceptual framework to the big data concept, to study the status of big data use in academic studies, its historical development and the differences between the years before and after which the big data year is called. In addition, it is to show whether the work done in Turkey and the world on the big data subject shows parallelity. The method of research is adopted. In this context, along with the research done in the EbscoHost ASC and the Higher Education Board (YOK) thesis databases, the academic studies related to big data after 2012 have seen a sharp increase compared to before 2012. However, the relative decrease in the number of academic studies related to "data mining" can be explained by the more specification of the subdivisions of data mining. It has also been observed that from parallel processing models to Map Reduce and from natural language processing applications, ideas mining methods have gained impulsion in recent years. This situation shows that we are in the process of transition from Web 2.0 to Web 3.0, that is, from the Interactive Web to the Semantic Web.
With the introduction of Web 2.0 and the development of mechanization, it has become possible to produce, store and share different types of data together. The concept of big data has been used to define the increasing size of data, the increasing velocity at which it is produced and transmitted, the increasing variety of formats of these data and the analysis process of the data. The purpose of this study is to draw a conceptual framework for the concept of big data, to examine the use of big data in academic studies, its change in years and examine the change before and after the 2012 is called big data year. With the quantitative research that has been conducted in EBSCOhost ASC database designed by Academic Search™ Complete and Yükseköğretim Kurulu thesis database to see the state of big data and big data analysis in academic studies and the results of these databases have compared to each other. As a result of the research, it is obvios that big data and analysis techniques has severely increased after 2012 compared to before 2012 in academic studies. in the increase rate of academic studies about “data mining” can be explained with rise of the specification on sub-branches of data mining. Especially in recent years Map Reduce in artificial neural networks, sentiment analysis and naturel language processing methods gaining acceleration has been observed. This condition shows that we are in the way towards semantic web from interactive web which is prospering to web 3.0 from web2.0.
Journal Type : Uluslararası
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