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status, examine office test comes to fruition, radiology pictures, vital signs, singular estimations like age and
weight, and charging data. EHR systems are proposed to store data decisively and to get the state of a patient
transversely after some time.

REFERENCES
[1] L. Wang, Heterogeneous data and big data analytics, Autom. Control and Inform. Sci. 3 (1) (2017) 8–15,
https://doi.org/10.12691/acis-3-1-3.
[2] O. Day, T. Khoshgoftaar, A survey on independent transfer learning, J. Big Data 4 (1) (2017), https://doi.org/
10.1186/s40537-017-0089-0.
[3] E. Tromp, M. Pechenizkiy, M. Gaber, Expressive modeling for trusted big data analytics: techniques and
applications in sentiment analysis, Big Data Anal. 2 (1) (2017), https://doi.org/10.1186/s41044-016-0018-9.
[4] A.E. Hassanien, N. Dey, S. Borra (Eds.), Medical Big Data and Internet of Medical Things: Advances, Chal-
lenges, and Applications, Taylor & Francis, 2019.
[5] P. Jones, A data linkage strategy for producing census and population statistics from administrative data, Int.
J. Popul. Data Sci. 1 (1) (2017), https://doi.org/10.23889/ijpds.v1i1.378.
[6] C. Luo, Y. Chen, Research on association rule mining algorithm based on distributed data, Adv. Mater. Res.
998–999 (2014) 899–902, https://doi.org/10.4028/www.scientific.net/amr.998-999.899.
[7] N. Dey, A.E. Hassanien, C. Bhatt, A.S. Ashour, S.C. Satapathy (Eds.), Internet of Things and Big Data An-
alytics Toward Next-Generation Intelligence, Springer International Publishing, 2018.
[8] J. Bughin, Big data, Big bang? J. Big Data 3 (1) (2016), https://doi.org/10.1186/s40537-015-0014-3.
[9] C. Domenig, M. Aspalter, M. Umathum, T. Holzenbein, Redo pedal bypass surgery after pedal graft failure:
gain or gadget? Ann. Vasc. Surg. 21 (6) (2007) 713–718, https://doi.org/10.1016/j.avsg.2007.07.010.
[10] M. Hoskins, Common big data challenges and how to overcome them, Big Data 2 (3) (2014) 142–143, https://
doi.org/10.1089/big.2014.0030.
[11] S. Boubiche, D. Boubiche, A. Bilami, H. Toral-Cruz, Big data challenges and data aggregation strategies
in wireless sensor networks, IEEE Access 6 (2018) 20558–20571, https://doi.org/10.1109/access.2018.
2821445.
[12] A. Wilson, Big-data analytics for predictive maintenance modeling: challenges and opportunities, J. Pet.
Technol. 68 (10) (2016) 71–72, https://doi.org/10.2118/1016-0071-jpt.
[13] M. Desai, The integration of the data scientist into the team: implications and challenges, Data Sci. (2017)
1–6, https://doi.org/10.3233/ds-170008.
[14] E. Chalmers, D. Hill, V. Zhao, E. Lou, Prescriptive analytics applied to brace treatment for AIS: a pilot dem-
onstration, Scoliosis 10 (S1) (2015), https://doi.org/10.1186/1748-7161-10-s1-o64.
[15] V. Abrar, M.R. Wahyudi, Implementasi Heterogenous Distributed Database System Oracle Xe 10g dan
MySQL Rekam Medis Poliklinik UIN Sunan Kalijaga, Creat. Inform. Technol. J. 4 (1) (2016) 9, https://
doi.org/10.24076/citec.2016v4i1.91.
[16] V. Boehme-Neßler, Privacy: a matter of democracy. Why democracy needs privacy and data protection, Int.
Data Priv. Law 6 (3) (2016) 222–229, https://doi.org/10.1093/idpl/ipw007.
[17] W. Cooper, Origin and development of data envelopment analysis: challenges and opportunities, Data En-
velop. Anal. J. 1 (1) (2014) 3–10, https://doi.org/10.1561/103.00000002.
[18] R. Luethi, M. Phillips, Challenges and solutions for long-term permafrost borehole temperature monitor-
ing and data interpretation, Geogr. Helv. 71 (2) (2016) 121–131, https://doi.org/10.5194/gh-71-121-2016.
[19] R. Kashyap, A. Piersson, Big data challenges and solutions in the medical industries, in: Handbook of Re-
search on Pattern Engineering System Development for Big Data Analytics, IGI Global, 2018, pp. 1–24.
[20] R. Kashyap, A. Piersson, Impact of big data on security, in: Handbook of Research on Network Forensics and
Analysis Techniques, IGI Global, 2018, pp. 283–299.