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

Anjum, Ashiq; Aizad, Sanna; Arshad, Bilal; Subhani, Moeez M.; Davies-Tagg, Dominic; Abdullah, Tariq; Antonopoulos, Nikolaos

Abstract

With exabytes of data being generated from genome sequencing, a whole new science behind genomics 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 genomics 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 to be extracted from these other sources (such as clinical data sets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of data sets 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 be put in place to allow the data to be quickly retrieved. The data is then iteratively analyzed 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.

Citation

Anjum, A., Aizad, S., Arshad, B., Subhani, M. M., Davies-Tagg, D., Abdullah, T., & Antonopoulos, N. (2017). Big data analytics in healthcare: A cloud based framework for generating insights. In N. Antonopoulos, & L. Gillam (Eds.), Cloud Computing (153-170). Springer. https://doi.org/10.1007/978-3-319-54645-2_6

• Acceptance Date: Jun 3, 2017
• Online Publication Date: Jun 3, 2017
• Publication Date: 2017
• Deposit Date: Feb 12, 2019
• Publisher: Springer
• Pages: 153-170
• Series Title: Computer Communications and Networks
• Series ISSN: 1617-7975
• Book Title: Cloud Computing
• ISBN: 9783319546445
• DOI: https://doi.org/10.1007/978-3-319-54645-2_6
• Keywords: Big data, cloud computing, analytics, healthcare data, genomics data, graph models, tiered data storage,
• Public URL: http://researchrepository.napier.ac.uk/Output/1557071