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==Introduction== | ==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.<ref name="ILS">{{cite web |url=http://www.internetlivestats.com/ |title=Internet Live Stats |work=InternetLiveStats.com |accessdate=16 July 2016}}</ref><ref name="KempDigital16">{{cite web |url=https://wearesocial.com/uk/special-reports/digital-in-2016 |title=Digital in 2016 |author=Kemp, S. |work=We Are Social |publisher=We Are Social Ltd |date=27 January 2016 |accessdate=27 June 2016}}</ref><ref name="IWS">{{cite web |url=https://www.internetworldstats.com/emarketing.htm |title=Internet Growth Statistics |work=Internet World Stats |publisher=Miniwatts Marketing Group |accessdate=21 May 2018}}</ref> 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 did 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.<ref name="VagataScaling14">{{cite web |url=https://code.facebook.com/posts/229861827208629/scaling-the-facebook-data-warehouse-to-300-pb/ |title=Scaling the Facebook data warehouse to 300 PB |author=Vagata, P.; Wilfong, K. |work=Facebook Code |publisher=Facebook |date=10 April 2014 |accessdate=27 June 2016}}</ref><ref name="DhavalchandraBig16">{{cite journal |title=Big data—A survey of big data technologies |journal=International Journal Of Science Research and Technology |author=Dhavalchandra, P.; Jignasu, M.; Amit, R. |volume=2 |issue=1 |pages=45–50 |year=2016 |url=http://www.ijsrt.us/vol2issue1.aspx}}</ref> Data production has also increased at a remarkable rate in the healthcare sector; widespread use of digital medical imaging peripherals has triggered this data production. Also, the data generated in the healthcare sector has reached 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 diagnoses, outputting digital files from various devices, and creating and storing the results of different surveys. Different types of data sources produce data in various structured and unstructured formats; examples include patient information, laboratory results, X-ray devices, computed tomography (CT) devices, 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. Additionally, exhaustive digital healthcare devices make higher-density graphical outputs easy additions to the growing body of data. In 2011, the amount of data in 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, [[electronic medical record]] (EMR) use has increased 31% from 2001 to 2005 and more than 50% from 2005 to 2008.<ref name="DeanReview09">{{cite journal |title=Review: Use of electronic medical records for health outcomes research: A literature review |journal=Medical Care Research and Review |author=Dean, B.B.; Lam, J.; Natoli, J.L. et al. |volume=66 |issue=6 |pages=611–38 |year=2009 |doi=10.1177/1077558709332440 |pmid=19279318}}</ref><ref name="ErgüzenMedical17">{{cite journal |title=Medical Image Archiving System Implementation with Lossless Region of Interest and Optical Character Recognition |journal=Journal of Medical Imaging and Health Informatics |author=Ergüzen, A.; Erdal, E. |volume=7 |issue=6 |pages=1246-1252 |year=2017 |doi=10.1166/jmihi.2017.2156}}</ref> While neuroimaging operation data sizes had reached approximately 200 GB per year between 1985 and 1989, it has risen to 5 PB annually between 2010 and 2014, yet another indicator of the increase in data in the healthcare sector.<ref name="DinovVolume16">{{cite journal |title=Volume and Value of Big Healthcare Data |journal=Journal of Medical Statistics and Informatics |author=Dinov, I.D. |volume=4 |page=3 |year=2016 |doi=10.7243/2053-7662-4-3 |pmid=26998309 |pmc=PMC4795481}}</ref> | |||
==References== | ==References== | ||
Revision as of 22:47, 6 June 2018
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| Full article title | Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system |
|---|---|
| Journal | Applied Sciences |
| Author(s) | Ergüzen, Atilla; Ünver, Mahmut |
| Author affiliation(s) | Kırıkkale University |
| Primary contact | Email: munver at kku dot edu dot tr |
| Year published | 2018 |
| Volume and issue | 8(6) |
| Page(s) | 913 |
| DOI | 10.3390/app8060913 |
| ISSN | 2076-3417 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://www.mdpi.com/2076-3417/8/6/913/htm |
| Download | http://www.mdpi.com/2076-3417/8/6/913/pdf (PDF) |
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Abstract
Recently, the use of the internet has become widespread, increasing the use of mobile phones, tablets, computers, internet of things (IoT) devices, and other digital sources. In the healthcare sector, with the help of next generation digital medical equipment, this digital world also has tended to grow in an unpredictable way such that nearly 10 percent of global data is healthcare-related, continuing to grow beyond what other sectors have. This progress has greatly enlarged the amount of produced data which cannot be resolved with conventional methods. In this work, an efficient model for the storage of medical images using a distributed file system structure has been developed. With this work, a robust, available, scalable, and serverless solution structure has been produced, especially for storing large amounts of data in the medical field. Furthermore, the security level of the system is extreme by use of static Internet Protocol (IP) addresses, user credentials, and synchronously encrypted file contents. One of the most important key features of the system is high performance and easy scalability. In this way, the system can work with fewer hardware elements and be more robust than others that use name node architecture. According to the test results, the performance of the designed system is better than 97% from a Not Only Structured Query Language (NoSQL) system, 80% from a relational database management system (RDBMS), and 74% from an operating system (OS).
Keywords: big data, distributed file system, health data, medical imaging
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 did 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 healthcare sector; widespread use of digital medical imaging peripherals has triggered this data production. Also, the data generated in the healthcare sector has reached 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 diagnoses, outputting digital files from various devices, and creating and storing the results of different surveys. Different types of data sources produce data in various structured and unstructured formats; examples include patient information, laboratory results, X-ray devices, computed tomography (CT) devices, 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. Additionally, exhaustive digital healthcare devices make higher-density graphical outputs easy additions to the growing body of data. In 2011, the amount of data in 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, electronic medical record (EMR) use has increased 31% from 2001 to 2005 and more than 50% from 2005 to 2008.[6][7] While neuroimaging operation data sizes had reached approximately 200 GB per year between 1985 and 1989, it has risen to 5 PB annually between 2010 and 2014, yet another indicator of the increase in data in the healthcare sector.[8]
References
- ↑ "Internet Live Stats". InternetLiveStats.com. http://www.internetlivestats.com/. Retrieved 16 July 2016.
- ↑ Kemp, S. (27 January 2016). "Digital in 2016". We Are Social. We Are Social Ltd. https://wearesocial.com/uk/special-reports/digital-in-2016. Retrieved 27 June 2016.
- ↑ "Internet Growth Statistics". Internet World Stats. Miniwatts Marketing Group. https://www.internetworldstats.com/emarketing.htm. Retrieved 21 May 2018.
- ↑ Vagata, P.; Wilfong, K. (10 April 2014). "Scaling the Facebook data warehouse to 300 PB". Facebook Code. Facebook. https://code.facebook.com/posts/229861827208629/scaling-the-facebook-data-warehouse-to-300-pb/. Retrieved 27 June 2016.
- ↑ Dhavalchandra, P.; Jignasu, M.; Amit, R. (2016). "Big data—A survey of big data technologies". International Journal Of Science Research and Technology 2 (1): 45–50. http://www.ijsrt.us/vol2issue1.aspx.
- ↑ Dean, B.B.; Lam, J.; Natoli, J.L. et al. (2009). "Review: Use of electronic medical records for health outcomes research: A literature review". Medical Care Research and Review 66 (6): 611–38. doi:10.1177/1077558709332440. PMID 19279318.
- ↑ Ergüzen, A.; Erdal, E. (2017). "Medical Image Archiving System Implementation with Lossless Region of Interest and Optical Character Recognition". Journal of Medical Imaging and Health Informatics 7 (6): 1246-1252. doi:10.1166/jmihi.2017.2156.
- ↑ Dinov, I.D. (2016). "Volume and Value of Big Healthcare Data". Journal of Medical Statistics and Informatics 4: 3. doi:10.7243/2053-7662-4-3. PMC PMC4795481. PMID 26998309. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795481.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
