Summary
Big data are receiving an increasing attention in biomedicine and healthcare. It is
therefore important to understand the reason why big data are assuming a crucial role
for the biomedical informatics community. The capability of handling big data is becoming
an enabler to carry out unprecedented research studies and to implement new models
of healthcare delivery. Therefore, it is first necessary to deeply understand the
four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity,
and their meaning in practice. Then, it is mandatory to understand where big data
are present, and where they can be beneficially collected. There are research fields,
such as translational bioinformatics, which need to rely on big data technologies
to withstand the shock wave of data that is generated every day. Other areas, ranging
from epidemiology to clinical care, can benefit from the exploitation of the large
amounts of data that are nowadays available, from personal monitoring to primary care.
However, building big data-enabled systems carries on relevant implications in terms
of reproducibility of research studies and management of privacy and data access;
proper actions should be taken to deal with these issues. An interesting consequence
of the big data scenario is the availability of new software, methods, and tools,
such as map-reduce, cloud computing, and concept drift machine learning algorithms,
which will not only contribute to big data research, but may be beneficial in many
biomedical informatics applications. The way forward with the big data opportunity
will require properly applied engineering principles to design studies and applications,
to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies,
and to improve data processing and data management regulations.
Keywords
Big data - data analytics - research reproducibility - cloud - NoSQL - map-reduce
