Cloud computing and big data

High-performance computing is where there is either a cluster and grid of servers or virtual machines that are connected by a network for a distributed storage and workflow (Bhokare et al., 2016; Connolly & Begg, 2014; Minelli, Chamber, & Dhiraj, 2013). Parallel computing environments draw on the distributed storage and workflow on the cluster and grid of servers or virtual machines for processing big data (Bhokare et al., 2016; Minelli et al., 2013). NoSQL databases have benefits as they provide a data model for applications that require a little code, less debugging, run on clusters, handle large scale data, stored across distributed systems, use parallel processing, and evolve with time (Sadalage & Fowler, 2012).  Cloud technology is the integration of data storage across a distributed set of servers or virtual machines through either traditional relational database systems or NoSQL database systems while allowing for data preprocessing and processing through parallel processing (Bhokare et al., 2016; Connolly & Begg, 2014; Minelli et al., 2013; Sadalage & Folwer, 2012).

Clouds can come in different flavors depending on how much the organization and supplier want to manage: Infrastructure as a Service, Platform as a Service, and Software as a Service (Connolly & Begg, 2014).  Thus, this makes the enterprise IT act as a broker across the various cloud options.  Also, analyzing exactly how and where data are stored to ensure it complies with various national and international data rules and regulations while preserving data privacy exist with the type of cloud use: public, community, private and hybrid clouds (Minelli et al. 2013; Conolloy & Begg, 2014).

Public cloud environments are where a supplier to a company provides a cluster or grid of servers through the internet like Spark AWS, EC2 (Connolly & Begg, 2014; Minelli et al. 2013).  Cloud computing can be thought of as a set of building blocks.  The company can grow or shrink a number of servers and services when needed dynamically, which allows the company to request the right amount of services for their data collection, storage, preprocessing, and processing needs (Bhokare et al., 2016; Minelli et al., 2013; Sadalage & Fowler, 2012).  This allows for the company to purchase the services it needs, without having to purchase the infrastructure to support the services it might think it will need. This allows for hyper-scaling computing in a distributed environment, also known as hyper-scale cloud computing, where the volume and demand of data explode exponentially yet still be accommodated in public, community, private, or hybrid cloud in a cost efficiently (Mainstay, 2016; Minelli et al., 2013).

Data storage and sharing are a key component of using enterprise public clouds (Sumana & Biswal, 2016).  However, it should be noted that the data is stored in the public cloud is stored on the same servers as probably the company’s competitors, so data security is an issue. Sumana and Biswal (2016) proposed that a key aggregate cryptosystem to be used, where the enterprise holds the master key for all its enterprise files, whereas going a deep layer users can have other data encrypted to send within the enterprise, without needing to know the enterprise file key. This proposed solution for data security in a public cloud allows for end-user registration, end-user revocation, file generation and deletion, and file access and traceability.

A community cloud environment is a cloud that is shared exclusively by a set of companies that share the similar characteristics, compliance, security, jurisdiction, etc. (Connolly & Begg, 2014). Thus, the infrastructure of all of these servers and grids meet industry standards and best practices, with the shared cost of the infrastructure is maintained by the community.

Private cloud environments have a similar infrastructure to a public cloud, but the infrastructure only holds the data one company exclusively, and its services are shared across the different business units of that one company (Connolly & Begg, 2014; Minelli et al., 2013). An organization may have all the components already to build a cloud through various on-premise computing resources and thus tend to build a cloud system using open source code on their internal infrastructure; this is called an on-premise private cloud (Bhokare et al., 2016). The benefit of the private cloud is full control of your data, and the cost of the servers are spread across all the business units, but the infrastructure costs (initial, upgrades, and maintenance costs) are in the company.

Hybrid clouds are two or more cloud structures that have either a private, community or public aspect to them (Connolly & Begg, 2014).  This allows for some data to be retained in the house if need be, and reducing the size of capital expenditure for the internal cloud infrastructure, while other data is stored externally where the cost of the infrastructure is not directly felt by the organization.

References

  • Bhokare, P., Bhagwat, P., Bhise, P., Lalwani, V., & Mahajan, M. R. (2016). Private Cloud using GlusterFS and Docker.International Journal of Engineering Science5016.
  • Connolly, T., Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, (6th). Pearson Learning Solutions. [Bookshelf Online].
  • (2016). An economic study of the hyper-scale data center. Mainstay, LLC, Castle Rock, CO, the USA, Retrieved from http://cloudpages.ericsson.com/ transforming-the-economics-of-data-center
  • Minelli, M., Chambers, M., &, Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. [Bookshelf Online].
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, [Bookshelf Online].
  • Sumana, P., & Biswal, B. K. (2016). Secure Privacy Protected Data Sharing Between Groups in Public Cloud.International Journal of Engineering Science3285.
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Big Data Analytics: Privacy & HIPAA

Since its inception 25 years ago, the human genome project has been sequenced many 3B base pair of the human genomes (Green, Watson, & Collins, 2015).  This project has given rise of a new program, the Ethical, Legal and Social Implication (ELSI) project.  ELSI got 5% of the National Institute of Health Budget, to study ethical implications of this data, opening up a new field of study (Green et al., 2015 & O’Driscoll, Daugelaite, & Sleator, 2013).  Data sharing must occur, to leverage the benefits of the genome projects and others like it.  Poldrak and Gorgolewski (2014) stated that the goals of sharing data help out with the advancement of the field in a few ways: maximizing the contribution of research subjects, enabling responses to new questions, enabling the generation of new questions, enhance research results reproducibility (especially when the data and software used are combined), test bed for new big data analysis methods, improving research practices (development of a standard of ethics), reducing the cost of doing the science (what is feasible for one scientist to do), and protecting valuable scientific resources (via indirectly creating a redundant backup for disaster recovery).  Allowing for data sharing of genomic data can present ethical challenges, yet allow for multiple countries and disciplines to come together and analyze data sets to come up with new insights (Green et al., 2015).

Richards and King (2014), state that concerning privacy, we must think of it regarding the flow of personal information.  Privacy cannot be thought of as a binary, as data is private and public, but within a spectrum.  Richards and Kings (2014) argue that the data as exchanged between two people has a certain level of expectation of privacy and that data can remain confidential, but there is never a case were data is in absolute private or public.  Not everyone in the world would know or care about every single data point, nor will any data point be kept permanently secret if it is uttered out loud from the source.  Thus, Richards and Kings (2014) stated that transparency can help prevent abuse of the data flow.  That is why McEwen, Boyer, and Sun (2013) discussed that there could exist options for open-consent (your data can be used for any other future research project), broad-consent (describe various ways the data could be used, but it is not universal), or an opt-out-consent (where participants can say what their data shouldn’t be used for).

Attempts are being made through the enactment of Genetic Information Nondiscrimination Act (GINA) to protect identifying data for fears that it can be used to discriminate against a person with a certain type of genomic indicator (McEwen et al., 2013).  Internal Review Boards and Common Rules, with the Office of Human Research Protection (OHRP), have guidance on information flow that is de-identified.  De-identified information can be shared and is valid under current Health Insurance Portability and Accounting Act of 1996 (HIPAA) rules (McEwen et al, 2013).  However, fear of loss of data flow control comes from increase advances in technological decryption and de-anonymisation techniques (O’Driscoll et al., 2013 and McEwen et al., 2013).

Data must be seen and recognized as a person’s identity, which can be defined as the “ability of individuals to define who they are” (Richards & Kings, 2014). Thus, the assertion made in O’Driscoll et al. (2013) about how the ability to protect medical data, with respects to bid data and changing concept, definitional and legal landscape of privacy is valid.  Thanks to HIPAA, cloud computing, is currently on a watch list. Cloud computing can provide a lot of opportunity for cost savings. However, Amazon cloud computing is not HIPAA compliant, hybrid clouds could become HIPAA, and commercial cloud options like GenomeQuest and DNANexus are HIPAA compliant (O’Driscoll et al., 2013).

However, ethical issues extend beyond privacy and compliance.  McEwen et al. (2013) warn that data has been collected for 25 years, and what if data from 20 years ago provides data that a participant can suffer an adverse health condition that could be preventable.  What is the duty of the researchers today to that participant?  How far back in years should that go through?

Other ethical issues to consider: When it comes to data sharing, how should the researchers who collected the data, but didn’t analyze it should be positively incentivized?  One way is to make them co-author of any publication revolving their data, but then that makes it incompatible with standards of authorships (Poldrack & Gorgolewski, 2013).

 

Resources:

  • Green, E. D., Watson, J. D., & Collins, F. S. (2015). Twenty-five years of big biology. Nature, 526.
  • McEwen, J. E., Boyer, J. T., & Sun, K. Y. (2013). Evolving approaches to the ethical management of genomic data. Trends in Genetics, 29(6), 375-382.
  • Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510-1517
  • O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013). ‘Big data,’ Hadoop and cloud computing in genomics. Journal of biomedical informatics, 46(5), 774-781.
  • Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest L. Rev., 49, 393.