Big data analytics in healthcare: A cloud based framework for generating insights

Book chapter


Anjum, Ashiq, Aizad, Sanna, Arshad, Bilal, Subhani, Moeez, Davies-Tagg, Dominic, Abdullah, Tariq and Antonopoulos, Nikolaos 2017. Big data analytics in healthcare: A cloud based framework for generating insights. in: Springer.
AuthorsAnjum, Ashiq, Aizad, Sanna, Arshad, Bilal, Subhani, Moeez, Davies-Tagg, Dominic, Abdullah, Tariq and Antonopoulos, Nikolaos
Abstract

With exabytes of data being generated from genome sequencing, a whole new science behind genomic big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being sequenced. Huge amounts of genomic data along with a vast variety of clinical data cannot be handled using existing frameworks and techniques. It is to be efficiently stored in a warehouse where a number of things have to be taken into account. Firstly, the genome data is to be integrated effectively and correctly with clinical data. The other data sources along with their formats have to be identified. Required data is then extracted from these other sources (such as clinical datasets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of datasets are being integrated with huge amounts of genome. Secondly, since the data is captured at disparate locations individually by clinicians and scientists, it brings the challenge of data consistency. It has to be made sure that the data consistency is not compromised as it is passed along the warehouse. Checks have to be put in place to make sure the data remains consistent from start to finish. Thirdly, to carry this out effectively, the data infrastructure has to be in the correct order. How frequently the data is accessed plays a crucial role here. Data in frequent use will be handled differently than data which is not in frequent use. Lastly, efficient browsing mechanisms have to put in place to allow the data to be quickly retrieved. The data is then iteratively analysed to get meaningful insights. The challenge here is to perform analysis very quickly. Cloud Computing plays an important role as it is used to provide scalability.

With exabytes of data being generated from genome sequencing, a
whole new science behind genomic big data has emerged. As technology
improves, the cost of sequencing a human genome has gone down considerably
increasing the number of genomes being sequenced. Huge amounts of genomic
data along with a vast variety of clinical data cannot be handled using existing
frameworks and techniques. It is to be efficiently stored in a warehouse where a
number of things have to be taken into account. Firstly, the genome data is to be
integrated effectively and correctly with clinical data. The other data sources along
with their formats have to be identified. Required data is then extracted from these
other sources (such as clinical datasets) and integrated with the genome. The main
challenge here is to be able to handle the integration complexity as a large number
of datasets are being integrated with huge amounts of genome. Secondly, since the
data is captured at disparate locations individually by clinicians and scientists, it
brings the challenge of data consistency. It has to be made sure that the data
consistency is not compromised as it is passed along the warehouse. Checks have
to be put in place to make sure the data remains consistent from start to finish.
Thirdly, to carry this out effectively, the data infrastructure has to be in the correct
order. How frequently the data is accessed plays a crucial role here. Data in
frequent use will be handled differently than data which is not in frequent use.
Lastly, efficient browsing mechanisms have to put in place to allow the data to be
quickly retrieved. The data is then iteratively analysed to get meaningful insights.
The challenge here is to perform analysis very quickly. Cloud Computing plays an
important role as it is used to provide scalability.

KeywordsBig data; Cloud computing; Analytics; Healthcare data; Graph models; Tiered data storage
Year2017
PublisherSpringer
Series1617-7975
ISBN9783319546445
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-54645-2
Web address (URL)http://hdl.handle.net/10545/621422
hdl:10545/621422
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Publication dates2017
Publication process dates
Deposited17 Feb 2017, 16:57
ContributorsUniversity of Derby
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