## 2 Big Data Architecture in Medicine  
Big data solutions, in healthcare worldwide, primarily consist of three different solutions:  
The first is a database system which has two different popular application architectures; relational database management systems (RDBMS) and NoSQL database systems. RDBMSs, as the most widely known and used systems for this purpose, are storage methods in which data is stored in a structured format. The data to be processed must be the appropriate type and format. In these systems, a single database can serve multiple users and applications. Since these systems are built on vertical growth functionality, the data structure must be defined in advance, and they have a lot of constraints like atomicity, consistency, isolation, and durability. The strict rules that make these systems indispensable are beginning to be questioned today. However, due to the used hardware and software, the initial installation costs are high. Especially when the volume of data increases, the horizontal scalability feature will be quite unsatisfactory and difficult to manage, which is a major factor of their not being a part of big data problem resolution. Also, these systems are more complex than file systems, which most importantly is not suitable for big data. Due to the deficiency of managing RDBMSs' big data, NoSQL database systems have emerged as an alternative. The main purpose of these systems is to be able to store the increasing data volumes of the internet and to respond to the needs of high-traffic systems via unstructured or semi-structured formats. NoSQL databases are systems that provide high accessibility according to RDBMSs and in which data are easily scaled horizontally [13]. Reading and writing performances may be more acceptable than RDBMS. One of the most important features is that they are horizontally expandable. Thousands of servers can work together as a cluster and operate on big data. They are easy to program and manage due to their flexible structures. Another feature of these systems is that they must be doing grid computing in clusters that consist of many machines connected to a network, in this way, data process speeds have increased. Besides, it does not yet have as advanced features as RDBMS on data security. Some NoSQL projects are lacking in documentation and professional technical support. Also, the concept of transactions is not available in NoSQL database systems, loss of data may occur, so they are not suitable for use in banking and financial systems [14]. Basic file management functions of operating systems (OS) are used for the second solution which are called 'file servers' in literature. In this system, medical image data is stored in files and folders in underlying operating system file structure. Operating systems use a hierarchical file system. In this structure, the files are in a tree structure, called a 'directory'. File servers store the files in the way that is determined by HIMS according to image type, file creation date, Policlinic name, and patient information. HIMS executes read and write operations by invoking system-calls which provide low-level interface to storage devices with the help of the operating system. The advantages of using file servers are that they are simple to deliver, easy to implement, and have acceptable file operation performance. Writing, deleting, reading, and searching files on the operating system is a fairly fast process because the operating system has been specialized for file management. Especially, operating systems has more flexibility and performance RDBMS and NoSQL systems. However, the OS could not use these advantages to be a satisfactory solution model for big data because of the lack of horizontal scalability. The main task of OS file management is to serve system calls to other applications. So, the OS is a part of the solution rather the solution alone. Besides the storage of data not being as secure as other methods, data cannot be scaled according to data size, and backup and file transfer cannot be done safely.