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10.3 DATABASE MANAGEMENT SYSTEM AND NGS 249

with these data to make it useful for retrieval. At present, they are assumed to be a suitable way to
execute low priced and larger scale healthcare analysis. However, expansion of the gap between out-
comes and healthcare costs is one of the most challenging issues. Efforts from many developed coun-
tries are under way to fill this gap. The gap arises between the cost of healthcare and outcomes because
of poor management of insights from research, poor usage of existing evidence, and poor attainment of
care experience. Big data has been used in the healthcare sector in three different areas such as disease
prevention, identification of risk factors for causing diseases, and designing behavioral change in
health [20, 21]. Hence, a demand arises to enhance the quality of healthcare and patient outcomes
through enhancing the availability of data [22, 23]. Big data streams in healthcare are classified into
different categories [24]. Different sources of big data belonging to the medical sector are clinical
registries, biometric data, patient-reported data, medical imaging, prospective cohort studies, the in-
ternet, biomarker data, and large clinical trials. Dey et al. [25] addressed the real time big data an-
alytics to derive intelligence systems in geological science and also provided models to guide the
researchers in developing different remote sensing visualizations and data schemes. Both big data
and the internet of things (IoT) are inter related. Satapathy and coworker [26] provided the knowl-
edge regarding the IoT and big data analytics towards next-generation intelligence. Additionally,
Bhattetal. [27] provided the knowledge regarding the IoT and big data technologies for next gen-
eration healthcare area. They also reviewed the IoT concept and healthcare application to realize
better healthcare with affordable costs.

10.3 DATABASE MANAGEMENT SYSTEM AND NEXT GENERATION
SEQUENCING (NGS)
Data is defined as a collection of entities. If entities come from a biological experiment, it is called
biological data. The processed data is known as information. A database is a structure that can be used
to store information about multiple types of entities, relationships among those entities, and attributes.
An entity is a portion, place, object, or event for which we want to store processed data. Constraints are
certain terms in the database that protect the integrity of the database. The schema is various view of the
database for the use of the various system, components of the database management system (DBMS),
and for application security. DBMS is a program or collection of programs through which the user can
interact with the database [28]. Nowadays, it is also important to analyze and describe the big data in
communication science. The total amount of data has rapidly increased by several folds in terms of
volume, variety, and velocity of generation as well as consumption origination, which helps for dif-
ferent sectors, including biomedical sciences.
In biological research, sectors have implemented the advantages of sequencing technologies, based
on next generation sequencing (NGS). A sequence is a single, continuous molecule of nucleic acid and
proteins. The process of sequencing a nucleotide was first discovered by Frederick Sanger. This process
is called Sanger sequencing. More economical sequencing technologies known as NGS were devel-
oped [29]. The objectives of the NGS are to obtain fidelity, read length, infrastructure cost, and handle
large volumes of data. NGS technology plays an important role in biology, especially in human genome
study [30]. NGS technologies are implicated for several applications including gene expression pro-
filing, de novo assembly, re-sequencing, and transcriptomics sequencing at both deoxyribonucleic acid
(DNA) and ribonucleic acid (RNA) level. Metagenomics, microbial diversity, and epigenetic changes