## 2 Big Data Architecture in Medicine
It seems that the operating system alone is not suitable for solving big data problems. The third method is Distributed File System (DFS), or 'distributed file systems'. These systems are the most up-to-date systems that suppose the machines in various locations as a single framework and provide the most appropriate solution to the big data problem. Hadoop DFS and MapReduce are primarily used for big data storage and analytics. Hybrid solutions including Hadoop and NoSQL also is used and criticized in the literature. However, there are some drawbacks to using Hadoop ecosystem in the healthcare. The first one is the small files problem which means Hadoop cannot store and manage this type of files efficiently [15]. Because Hadoop is primarily designed to manage large files greater than or equal to 64 MB, this size also default block size of Hadoop clusters [15]. For example, a file that is 1 GB in size consisting of 16 blocks of 64 MB Hadoop blocks occupies 2.4 KB space in a name node. However, 100,000 files of 100 KB occupies 1 GB space in data nodes, 1.5 MB in name node. This means more MapReduce operations required when processing small files. The healthcare sector average medical image file size is 10 MB, and when this situation is taken into consideration, a new DFS system is needed to embrace systems that have large numbers of small files. This study proposes a new methodology for this issue by small block size and no name node structure. [PERSON] at al. has identified five different strategies for how big data analytics can be effectively used to create business value for healthcare providers. This work was carried out for a total of 26 health institutions that uses Hadoop/MapReduce from Europe and the U.S. [16]. However, it is also stated in this study that with a growing amount of unstructured data, more comprehensive analytic techniques are required to be satisfied like deep learning algorithms. A significant analysis and discussion on Hadoop/MapReduce, Storm frameworks for big data in health care was presented by [PERSON] and [PERSON]. It stated that Hadoop/MapReduce cannot be used in real-time systems due to a performance gap. Therefore, they have proposed a novel health service model called BDeHS architecture which has three key benefits. Whereas, Spark and Storm can be used more effectively for real-time data analytics of large databases according to MapReduce [17]. One study provided detailed information about the architectural design of a personal health record system called \"MedCloud\" constructed on Hadoop's ecosystem [18]. Another study by [PERSON] and [PERSON] looked at big data in healthcare, a new file structure and achieving system has been developed to store regions of interest (ROIs) from MRIs. In other words, they extracted the ROI portions, which contained vital information about the patient, from the image, discarded the rest--called non-ROIs--and stored the ROIs in the newly designed file structure with a success ratio of approximately 30%. However, this work was done only to decrease the image sizes, not to effectively store big data on DFS [7]. The other study conducted by [PERSON] and [PERSON] showed that the Hadoop ecosystem has significant drawbacks for medium or big size healthcare providers: (a) it requires a great deal of programming skills for data analytics tasks using MapReduce; and (b) it is almost difficult to install, to configure, to manage Hadoop ecosystem completely, so it does not seem to be a feasible solution for medium- or large-scale healthcare providers [12]. Today, Hadoop is one of the enterprise-scaled open source solutions that makes it possible to store big data with thousands of data nodes, as well as analyze the data with MapReduce. However, there are three disadvantages to Hadoop. The first one is: Hadoop's default block size is 64 MB which presents an obstacle in managing numerous small files [15]. When a file smaller than 64 MB will be embedded in a 64 MB Hadoop block, it will cause a gap which is called internal fragmentation.