Report about Literature Review

Running head: LITERATURE REVIEW
LITERATURE REVIEW
Name of the Student
Student ID

Literature ReviewLiterature Review1
LITERATURE REVIEW
Abstract
In this digital era, data science along with big data is an emerging field. Data
science is related to big data and the process of making all the decisions in a
data driven manner. There are three rounds which are mentioned in this
report. The keywords and the derived keywords are mentioned along with
the discussion of the focused article. Numerous researches have been done
on big data analytics, data science have been done which has been taken
into account and discussed in the report.

Literature ReviewLiterature Review2
LITERATURE REVIEW
Literature Review
Round 1:
- The data science is an emerging technology which is aided by big
data in vast area. The decision making process becomes easy with the
introduction of big data.
- Analytics, data science, decision-making, big data, Google-scholar,
Twenty.
TITLE AUTHOR YEAR JOURNAL
Big data
research in
information
systems: Toward
an inclusive
research agenda
Abbasi, A.,
Sarker, S. and
Chiang, R.H
2016 Abbasi, A.,
Sarker, S. and
Chiang, R.H
The opportunity
and challenge for
IS research
Agarwal, R. and
Dhar, V.
2014
Big Data
Applications of
big data to smart
cities.
Andrejevic, M. 2014
International
Journal of
Communication
Big data, open
government and
e-government:
Issues, policies
and
recommendation
s
Bertot, J.C.,
Gorham, U.,
Jaeger, P.T.,
Sarin, L.C. and
Choi, H.
2014
Information
polity
Beyond the
hype: Big data
concepts,
methods, and
analytics
Gandomi, A. and
Haider, M.
2015
International
journal of
information
management
The role of big
data in smart
city
Hashem, I.A.T.,
Chang, V., Anuar,
N.B., Adewole,
K., Yaqoob, I.,
2016
International
Journal of
Information
Management

Literature ReviewLiterature Review3
LITERATURE REVIEW
Gani, A., Ahmed,
E. and Chiroma,
H.,
Big data and its
technical
challenges.
Jagadish, H.V.,
Gehrke, J.,
Labrinidis, A.,
Papakonstantino
u, Y., Patel, J.M.,
Ramakrishnan,
R. and Shahabi,
C
2014
Communications
of the ACM
Big data: survey,
technologies,
opportunities,
and challenges
Khan, N.,
Yaqoob, I.,
Hashem, I.A.T.,
Inayat, Z., Ali, M.,
Kamaleldin, W.,
Alam, M., Shiraz,
M. and Gani, A
2014
The Scientific
World Journal
Applications of
big data to smart
cities.
Al Nuaimi, E., Al
Neyadi, H.,
Mohamed, N.
and Al-Jaroodi, J.
2015
Journal
of
Internet Services
and Applications,
6(1), p.25.
A review and
future direction
of agile, business
intelligence,
analytics and
data science.
Larson, D. and
Chang, V.,
2016
International
Journal of
Information
Management,
36(5), pp.700-
710.
Service
innovation and
smart analytics
for industry 4.0
and big data
environment.
Lee, J., Kao, H.A.
and Yang, S.,
2014
Procedia Cirp,
16, pp.3-8.
Reflections on
societal and
business model
transformation
arising from
digitization and
big data
Loebbecke, C.
and Picot, A.,
2015
2015
The Journal of
Strategic
Information
Systems,
24(3),

Literature ReviewLiterature Review4
LITERATURE REVIEW
analytics: A
research agenda.
pp.149-157.
Data science and
its relationship to
big data and
data-driven
decision making
Provost, F. and
Fawcett, T.,
2013.
2013
Big data,
1(1),
pp.51-59.
Process data
analytics in the
era of big data
Qin, S.J. 2014
AIChE Journal,
60(9), pp.3092-
3100.
Big data: A
review
Sagiroglu, S. and
Sinanc, D.
2013
International
Conference on
Collaboration
Technologies and
Systems (CTS)
(pp. 42-47). IEEE.
Data science,
predictive
analytics, and
big data in
supply chain
management:
Current state
and future
potential.
Schoenherr, T.
and Speier‐Pero,
C.
2015
Journal
of
Business
Logistics,
36(1),
pp.120-132.
Big data:
Unleashing
information
Tien, J.M. 2013
Journal
of
Systems Science
and Systems
Engineering,
22(2), pp.127-
151.

Literature ReviewLiterature Review5
LITERATURE REVIEW
Data science,
predictive
analytics, and
big data: a
revolution that
will transform
supply chain
design and
management
Waller, M.A. and
Fawcett, S.E.
2013
Journal
of
Business
Logistics,
34(2),
pp.77-84.
Big data
analytics and
firm
performance:
Effects of
dynamic
capabilities.
Wamba, S.F.,
Gunasekaran, A.,
Akter, S., Ren,
S.J.F., Dubey, R.
and Childe, S.J.
2017
Journal
of
Business
Research,
70,
pp.356-365.
Concepts,
technologies,
and applications
Watson, H.J. 2014
Communications
of the
Association for
Information
Systems,
34(1),
p.65
After going through the initial search results list, some knowledge was
gathered regarding data science and the process of decision making in a
data-driven manner where data science along with the data engineering and
processing is involved (Abbasi, Sarker and Chiang 2016). Various benefits of
the decision making process in a data driven manner is demonstrated in a
good manner led to the new set of keywords.
Data engineering, big data tool, data processing.
- Data science and its relationship to big data and data-driven decision