## 1 Introduction  
In recent years, advances in information technology have increased worldwide; internet usage has exponentially accelerated the amount of data generated in all fields. The number of internet users was 16 million in 1995. This number reached 304 million in 2000, 888 million in 2005, 1.996 billion in 2010, 3.270 billion in 2015, and 3.885 billion in 2017 [1; 2; 3]. Every day, 2.5 exabytes (EB) of data are produced worldwide. Also, 90% of globally generated data has been produced since 2015. The data generated are in many different fields such as aviation, meteorology, IoT applications, health, and energy sectors. Likewise, the data produced through social media has reached enormous volumes. Not only does Facebook.com store 600 terabytes (TB) of data a day in 2014 but Google also processed hundreds of petabytes (PB) of data per day in the same year [4; 5]. Data production has also increased at a remarkable rate in the health sector widespread use of digital medical imaging peripherals has triggered this data production. Also, the data generated in the health sector has been reached a such a point that it cannot be managed easily with traditional data management tools and hardware. Healthcare has accumulated a big data volume by keeping patients' records, creating medical imaging that helps doctors with diagnosis, and through the output of various devices in digital format and storing the results of different surveys. Different types of data sources produce data in various structured and unstructured formats--such as patient information, laboratory results, X-ray devices, computed tomography (CT)s and magnetic resonance imaging (MRI). World population and average human lifespan is apparently increasing continuously, which means an exponential increase in the number of patients to be served. As the number of patients increases, the amount of collected data also increases dramatically. Besides, exhaustive digital healthcare devices make higher-density graphical outputs easy adds to the growing body of data. In 2011, the data amount of the healthcare sector in the U.S. reached 150 EB. In 2013, it appeared to have achieved 153 EB. In 2020, it is estimated that this number will reach 2.3 ZB. For example, in Electronic Medical Record (EMR) has increased 31% from 2001 to 2005 and more than 50% from 2005 to 2008 [6; 7]. While neuroimaging operation data sizes have reached approximately to 200 GB per year between 1985 and 1989, indeed it has risen to 5 PB annually between 2010 and 2014 which is an indicator of the data increase in the health sector [8]. In this way, new problems have emerged due to the increasing volume of data generated in all fields at the global level. Now there are substantial challenges to store and to analyze the data. The storage of data has become costlier than gathering it [9]. Thus, the amount of data that is produced, stored, and manipulated has increased dramatically, and because of this increase, 'big data' and data science/knowledge have begun to develop [10]. 'big data' consists of three concepts: variety, velocity, and volume; concerning healthcare records, finding an acceptable approach to cover these issues is particularly difficult to accomplish. Big data problems in healthcare and the objectives of the study according to the previous arguments are listed as follows:  
1. Increasing number of patients: the global population and average human lifespan are apparently increasing. For example, in Turkey, the number of visits to a physician has increased by about 4% per year since 2012 [11]. Moreover, the total number of per capita visits to a physician in Health Care Facilities in 2016 was 8.6 while this value was 8.2 in 2012. As the number of patients increases, the amount of collected data also increases dramatically which raises much more data to be managed. 2. Technological devices: exhaustive digital healthcare devices create high-resolution graphical outputs which means the huge amount of data to be stored. 3.