An Overview of Big Data Concepts, Methods, and Analytics: Challenges, Issues, and Opportunities

Abstract

In recent years, data generation is increasing on a large scale and fast pace, and the development of Internet applications, mobile applications, and network-connected sensors has also increased widely. These applications and extensive internet connections continuously produce a large volume of data, with a wide diversity and different structures, which is called big data. At the same time, technologies related to big data are also developing. The rapid growth of cloud computing and the Internet of Things (IoT) is accelerating the dramatic growth of data generation. Sensors around the world are collecting and transmitting data that will be stored and processed in the cloud, and the era of big data is coming. In this article, first, an overview of big data and the definitions of its features are explained, and then the applications of big data in different fields are examined and the challenges facing it are discussed. Finally, technologies related to big data in the field of big data analysis, data storage technologies, and visualization tools are proposed and cloud computing, IoT, and data center are examined as new technologies that are closely related to big data. The main goal of this article is to provide a comprehensive overview of big data and examine and explain various aspects of its applications and implementation.

Full text

979-8-3503-0198-4/23/$31.00 ©2023 IEEE
An Overview of Big Data Concepts, Methods, and
Analytics: Challenges, Issues, and Opportunities
Mahshad Mahmoudian
Department of Computer Engineering,
Najafabad Branch, Islamic Azad University,
Najafabad, Iran.
mah.mahmoudian@ieee.org
Yasin Kabalci
Department of Electrical Engineering
Nigde Ömer Halisdemir University
Nigde, Turkey
yasinkabalci@ohu.edu.tr
S. Mohammadali Zanjani*
Smart Microgrid Research Center,
Najafabad Branch, Islamic Azad University,
Najafabad, Iran.
sma_zanjani@pel.iaun.ac.ir
Ersan Kabalci
Department of Electrical Engineering
Nevsehir Haci Bektas Veli University
Nevsehir, Turkey
kabalci@nevsehir.edu.tr
Hossein Shahinzadeh
Department of Electrical Engineering
Amirkabir University of Technology
Tehran, Iran
h.s.shahinzadeh@ieee.org
Farshad Ebrahimi
Department of Electrical and Computer Engineering
University of Houston
Houston, TX 77004, USA
febrahimi@uh.edu
Abstract— In recent years, data generation is increasing on
a large scale and fast pace, and the development of Internet
applications, mobile applications, and network-connected
sensors has also increased widely. These applications and
extensive internet connections continuously produce a large
volume of data, with a wide diversity and different structures,
which is called big data. At the same time, technologies related
to big data are also developing. The rapid growth of cloud
computing and the Internet of Things (IoT) is accelerating the
dramatic growth of data generation. Sensors around the world
are collecting and transmitting data that will be stored and
processed in the cloud, and the era of big data is coming. In this
article, first, an overview of big data and the definitions of its
features are explained, and then the applications of big data in
different fields are examined and the challenges facing it are
discussed. Finally, technologies related to big data in the field of
big data analysis, data storage technologies, and visualization
tools are proposed and cloud computing, IoT, and data center
are examined as new technologies that are closely related to big
data. The main goal of this article is to provide a comprehensive
overview of big data and examine and explain various aspects of
its applications and implementation.
Keywords— Big Data, Data mining, Social networking, Big
Data analytics, Decision making, Information technology,
Internet of Things, Cloud computing.
I. INTRODUCTION
"Big data" is a relatively new topic in the field of
information technology. There are a lot of researchers working
on research and studies in this sector right now, and at the
same time, a lot of corporations have gotten interested in it for
a variety of reasons [1]. As a result of the considerable
applications offered by big data analysis, a variety of
businesses and fields of study, most notably those in the fields
of power distribution, healthcare, social sciences, insurance,
and finance, as well as governmental institutions, have also
begun to utilize it [2]. The analysis of large amounts of data
has become more important in modern research as well as in
modern business. These data are generated as a result of online
transactions, emails, movies, music, photos, click streams,
logs, postings, search queries, medical records, interactions on
social networks, scientific data, sensors, mobile phones, and
the programs that run on them [3]. Big data is stored in
databases that grow incrementally and contain a large volume
of information, making storage, management, sharing,
analysis, and data visualization complex tasks that require
software tools and complex databases. Throughout the course
of the previous two decades, there has been a significant
expansion of data in a variety of domains [4]. According to a
report that was published by the International Data
Corporation (IDC) in 2011, the total volume of data that was
produced and copied in the world was 1.8 zettabytes
(1.8*1024 exabytes) [5]. This figure has since increased to 40
zettabytes and is projected to reach 175 zettabytes by the year
2025. The progression of big data's expansion from 2010 to its
anticipated level of development in 2025 is seen in Figure 1.
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
175 ZB
0
20
40
60
80
100
120
140
160
180
Zetabytes
Time (Year)
Fig. 1. Annual Growth Rate of Global Data [6]
According to this report, the volume of data has grown
more than 20 times in less than a decade, and this figure will
double every two years in the near future. With the growth of
global data, the phrase "big data" has evolved to characterize
such huge collections. Big data is a very high volume of semi-
structured and unstructured data that needs more quick
analysis compared to other typical datasets and their
associated procedures [7]. Additionally, big data creates new
opportunities to discover new values and helps to gain a
deeper understanding of hidden data values, and of course,
comes with new challenges such as how to efficiently manage
and organize such data [8-9]. The generation of data has been
simpler as a result of advancements in information
technology; nowadays, big data originates from the everyday
activities of individuals, particularly in connection to the
services provided by internet companies. For instance, Google
analyzes hundreds of petabytes of data, Facebook generates
over ten petabytes of new material each month, and on
YouTube, an average of three hundred and fifty hours of video
are posted every minute. In addition, the rapid expansion of
cloud computing and the Internet of Things has contributed to
an increase in the volume of data. Computing in the cloud
offers a standardized method for storing and accessing the
digital assets of an organization [10]. As part of the Internet of
Things, sensors located all over the world are gathering and
transferring data that is then saved and processed in the cloud.
This volume of data creates many issues and challenges in
storing and retrieving heterogeneous and massive datasets,
which require hardware and software infrastructure and new
technologies to manage and leverage them. In this article, we
will review big data, its challenges, and related technologies.
* Assistant Professor, Department of Elect
rical Engineering, Najafabad
Branch, Islamic Azad University, Najafabad, Iran.
2023 5th Global Power, Energy and Communication Conference (IEEE GPECOM2023), June 14-16, 2023, Cappadocia, Turkey

First, the definition of big data, its features, and its
applications in various fields are explained. Then, the
challenges of big data in different areas such as data storage,
data visualization, data analysis, data privacy, performance
and scalability will be discussed. Finally, the technologies
related to big data in the field of data analysis, data storage,
and data virtualization, as well as the connection of big data
with cloud computing, the Internet of Things, and data centers
will be discussed [11-12].
II. OVERVIEW OF BIG DATA
The term big data refers to a rapidly growing collection of
massive and heterogeneous data in structured, unstructured,
and semi-structured formats. Due to their complex nature, big
data require powerful technologies and advanced algorithms
for management and analysis, and traditional business tools
are not effective for dealing with big data [13]. The definition
of big data is a topic on which different people disagree. Big
data, in general, is a group of data that cannot be
comprehended, gathered, managed, and processed
simultaneously using conventional hardware/software tools
and information technologies. Because of the importance of
the topic, technology companies, researchers, and data
analysts have different definitions of big data, which will be
discussed further below [14].
A. Definition of Big Data Characteristics
Big data refers to data assets that are both enormous and
complicated, and which need analysis in order to comprehend
and get information from them [15]. In 2010, Apache Hadoop
defined big data as "A dataset that has a high volume,
velocity, or variety, and traditional methods are limited in
their ability to efficiently analyze it." Based on this definition,
in May 2011, McKinsey & Company (a global consulting
organization) introduced big data as the "next frontier of
innovation, competition, and productivity." The National
Institute of Standards and Technology (NIST) defines big
data as "data sets that have such high volume, velocity, or
variety that traditional methods for efficient analysis are
limited." This definition focuses on the technological aspect
of big data. Most data scientists and big data experts define
big data with three main characteristics (known as the "3Vs").
* Volume: The dataset that conforms to the big data
standard is constantly changing and increasing over time. In
big data, there is a large amount of data with sizes ranging
from terabytes to zettabytes.
* Velocity: Big data is characterized by the rapid
generation of data, which, in turn, necessitates the rapid
processing of that data in order to derive useful insights. The
term "velocity" alludes to the real-time aspect of big data, and
in order to make the most of the potential benefits of big data
for businesses, it is necessary to gather, analyze, and use the
data in a prompt and efficient manner.
* Variety: Data comes in various types, including
structured data such as database data, semi-structured data
such as XML data, and unstructured data such as sound,
images, videos, web pages, text, etc [16].
However, others, including IDC, which is one of the most
influential leaders in big data and its research fields, have
different opinions. In 2011, IDC defined big data as follows:
"Big data technologies introduce a new generation of
technologies and architectures designed to extract value
economically from very large volumes of data with a wide
range of diversity, received, discovered, or analyzed at high
speeds." With this definition, the characteristics of big data
can be defined in the form of 4V, meaning volume (large
volume), variety (different methods), velocity (fast
production), and value (high value but low density), which is
widely recognized. The 4V characteristics of big data are
shown in Figure 2 [17].
Big Data
Velocity
•
Speed of data
Generation
•
Speed of data
Processing
•
Speed of data
Requests
Veracity
• Data trustworthiness
• Data quality
Variety
• Data source diversity
• Data structure
heterogeneity
Volume
• Data at scale
• Processing at scale
Fig. 2. 4V characteristics of big data
B. Applications of Big Data
There are numerous applications for big data, some of
which are illustrated in Figure 3 [18].
Political
Decisions
Health
Welfare
Tax Evaders
Natural Disaster Insurance
Agriculture
Smart
Grid
Unemployment
Building And
Constructions
Big Data
Fig. 3. Applications of Big Data
a) Fraud Detection and Control
In business operations, various types of fraudulent claims
or fake data exist, and identifying and controlling these data
and fraud in transactions is one of the most important
applications of big data. In most cases, fraud is discovered
after a long period of its occurrence when data is lost, and in
this case, only its effects can be reduced or policies can be
implemented to prevent its recurrence. Big data-based
platforms can examine and analyze transactions and business
operations in real-time and detect inappropriate behavior
from a user by examining large-scale patterns for all
transactions and deals, thereby changing the way fraud and
fake data are detected [19].
b) Call Center Data Analysis
Analyzing call center data is one of the useful applications
of big data. In current processes, there are no solutions for
processing customer data in the call center, and the
information and knowledge that a call center can provide is
ignored or presented with delay. Big data-based solutions in
call centers can identify recurring problems and behavioral
patterns of customers and employees by receiving and
processing call content, and help improve organizational
performance and increase customer satisfaction [20].
c) Social network analysis
One of the most important applications of big data directly
related to users is the analysis of user activity on social
networks. Users are widely active on social networks and

record a lot of information about their activities on a daily
basis, from expressing interest in a company's products on
Facebook to expressing opinions or complaints about other
products in the form of a message on Twitter. Social network
data can provide useful real-time information about market
responses to products and campaigns, enabling companies to
prepare and offer their products in line with market and
customer opinions [21].
d) Financial data analysis
Big data analysis can also be used for financial analysis
and forecasting. For example, big data is used in tools for
predicting stock market trends to support decision-making in
this area [22].
e) Agriculture
Biotechnology centers use sensor data in agriculture to
increase crop productivity. They study and simulate plant
reactions in different environmental conditions so that plants
can adjust to the environment based on this information. In
addition, big data can be used to select the type of crop to be
cultivated [23].
III. BIG DATA CHALLENGES
Data analysis of big data provides attractive and valuable
opportunities. However, researchers and experts in this field
face multiple challenges when exploring big data and
extracting knowledge and value from it. These problems exist
at various levels of storage, data display, analysis, lifecycle
management, reducing redundancy and compression, etc. In
addition, issues related to privacy and confidentiality are
especially obstacles and challenges that must be overcome in
distributed applications of big data. Some key obstacles and
challenges that must be overcome in developing big data
applications are described below [24]. Some of the existing
challenges for big data are shown in Figure 4, which we will
explain below.
10
9
8
7
6
5
4
3
2
1Big Data
Challenges
Storage Data Lifecycle
Management
Fig. 4. Some of the challenges for Big Data
A. Storage
Today's hard drives have a capacity of terabytes, while the
data generated in big data is far beyond that and is increasing
exponentially, reaching exabytes. Traditional data
management and analysis systems are based on Relational
Data Base Management Systems (RDBMS) and are only
suitable for managing structured data and are unable to store
and process such large amounts of data that are semi-
structured and unstructured [25]. The solution to this problem
is to use distributed file systems and NoSQL databases, which
are designed to manage unstructured data on a large scale.
B. Data Display
The types of datasets, their structures, the meanings of the
datasets, their organizations, the granularity of the datasets,
and their levels of accessibility can vary widely. The purpose
of displaying data is to give it meaning so that it may be
interpreted meaningfully by both users and computers. The
value of the primary data, on the other hand, is diminished by
an unsuitable display of the data, which may even impede an
effective study of the data [26]. Displaying data effectively
requires taking into account not only the structure, class, and
data type, but also the requirements and preferences of the
end user.
C. Redundancy reduction and data compression
In most cases, there is a significant amount of duplicate
information present in datasets. If the data's potential value is
not diminished in the process of decreasing this duplication
and compressing the data, the system's overall indirect costs
will be reduced to a greater extent than would have been the
case otherwise. For instance, the majority of the information
that is produced by sensor networks has a significant amount
of redundancy. This redundancy may be eliminated, and the
quantity of the resulting data can be reduced [27].
D. Data Lifecycle Management
Sensors and ubiquitous computing systems are creating
data at an unprecedented rate and scale, and present storage
systems are not capable of sustaining such enormous volumes
of data. This is in contrast to the comparatively modest
advances that storage systems have been making in
comparison. The worth of the data is taken into consideration
during the process of managing the data lifecycle to
determine which data should be kept and which should be
discarded.
E. Analysis
The big data analysis process, which has a large volume
of unstructured or semi-structured heterogeneous data,
requires a lot of resources and time. To address this issue,
distributed processing architectures are used, where data is
divided into smaller sections and made available for
processing by the number of computers in the network, and
finally, the processed data are combined [28].
F. Confidentiality of Information
One of the important challenges of big data is the
confidentiality and preservation of information. Most big
data providers and owners cannot efficiently maintain and
analyze their large datasets due to their limited capacity. They
rely on data analysis experts and tools that increase potential
security risks. Therefore, maintaining the confidentiality of
information is a major issue and challenge in big data [29].
G. Energy Management
With the increasing volume of data and demand for
analysis, processing, storage, and transfer of big data,
inevitably more electrical energy will be consumed for these
purposes. Therefore, mechanisms for controlling and
managing energy consumption levels for big data must be
established.
H. Scalability
The big data analytics system must support current and
future datasets. Therefore, the analytics algorithm should be
capable of processing increasingly complex datasets that are
expanding over time [30].
I. Collaboration
Big data analytics is interdisciplinary research that calls
for the cooperation of specialists from diverse domains in
order to fully utilize the potential of big data. To enable
scientists and engineers from diverse professions to access
various types of data and fully utilize their knowledge to

interact with one another in order to achieve the analytics
objectives, a comprehensive big data network architecture
must be developed [31].
IV. BIG DATA MANAGEMENT TOOLS AND TECHNOLOGIES
Big Data management involves organizing and utilizing a
large amount of data. Assuring data quality and accessibility
for use in Business Intelligence (BI) projects and Big Data
analytics is the aim of big data management. For analytics,
storage, and visualization, a variety of Big Data management
solutions are employed, some of which are briefly covered in
this section [32]. In addition, the relevant technologies related
to Big Data will also be discussed in this section.
A. Data Analysis
* Hadoop: An open-source software framework that
provides scalable solutions for solving problems with big data
on a set of computers. Hadoop is made up of two key
components: the MapReduce (MR) framework and the
Hadoop Data File System (HDFS). The data storage source
for MR is HDFS, a distributed file system created by Google
based on the Data File System and running on commercial
hardware (DFS).
* Hive: An open-source data warehouse for querying and
analyzing large sets of data stored in Hadoop files. It features
a SQL-like user interface for querying data held in multiple
Hadoop-integrated databases and storage systems. It was
initially introduced and developed by Facebook and is now
offered as an open-source tool.
* Pig: An advanced environment for developing
MapReduce applications using Hadoop. Pig Latin, a high-
level descriptive language that can express huge data
gathering and analysis tasks in MR programming, is the
language utilized in this platform.
* Platform: It is a tool for analyzing and discovering big
data. It is a platform that automatically takes user queries to
the target and allows users to interact visually with vast
amounts of data at a petabyte scale in the shortest possible
time. In fact, it creates an abstraction layer that anyone can
use to simplify and organize their datasets.
* Rapidminer: It is software that offers an integrated
platform for business analysis, predictive analytics, text
mining, machine learning, and data mining. Rapidminer
covers all data mining operations, including data preparation,
validation, visualization, and result optimization. It is used
for both the development of commercial applications as well
as research and education [33].
B. Storage Technologies
For the administration of huge volumes of data, methods
of data storage that are both efficient and effective are
necessary. This is due to the fact that the size and volume of
the data continue to rise at an alarming rate. Both the
virtualization of storage and the compression of data have
been major contributors to the total development that has
been made in this sector.
* HBase: The columnar, non-relational database known
as HBase is supported by the Apache Hadoop File System
(HDFS), which acts as the basis for the database. Users are
able to get read and write access in real-time to vast volumes
of data that come from a broad range of sources and
organizational forms with the help of HBase, which is a free
and open-source database system that anyone may download
and install on their own computers.
* SkyTree: A high-performance platform for machine
learning and data analysis that specifically focuses on
managing and analyzing big data.
* Non-Relational Databases: A strategy for managing
and constructing databases that are appropriate for use with
vast amounts of data in contexts that are dispersed is referred
to as a non-relational database, which is also known as
NoSQL. The most widely used of these databases is Apache
Cassandra, which was initially developed for Facebook in
2008 before being made available under an open-source
license. Additional examples of these databases are
SimpleDB, Google BigTable, MongoDB, and Voldemort.
Large organizations like Netflix, LinkedIn, and Twitter
employ one or more of these databases [34].
C. Data visualization tools
There are numerous open-source data visualization tools,
some of which are mentioned below [35].
* R: A free and open-source programming language and
development environment designed for visualizing and
graphically representing data based on graphic and statistical
computations. R is a programming language that is often
utilized in the statistical software development and data
analysis fields.
* Tableau: A tool used for visualizing results in the form
of charts, maps, graphs, and other graphics. There is also the
possibility of connecting Hadoop and Tableau, and
interaction between these two products.
* Infogram: This tool allows for the easy selection of a
wide range of ready-made visual templates. Additionally,
there are additional templates such as map charts and videos
in this software, and the ability to share created models are
also provided.
* ChartBlocks: A free online tool that provides the ability
to visualize databases and extensive pages without the need
for any complex code.
* Tangle: This visual tool provides capabilities beyond
data visualization and allows designers and developers to
design programs interactively for a better understanding of
data relationships.
D. Big data-related technologies
Some significant technologies that are closely connected
to big data are covered in this section.
a) Cloud computing
Cloud computing has a close relationship with big data.
Figure 5 depicts the main components of cloud computing.
The term "cloud computing" refers to a type of technology
that is capable of storing significant amounts of data. The
main goal of cloud computing is to use centralized
management of computational resources and capacities to
provide various applications by sharing resources in a unified
manner and making these applications accessible to users in
a transparent and efficient manner [36].
Bigdata Applications And
Services
Inquiry, Analysis And
Excavate Parallel Algorithm
Parallel Computing
Distributed Storage
Traditional Applications
And Services
Virtual Resources Pool
Flexible Resource
Scheduling Management
Virtualization
Cloud Computing Resources And Platform
Cloud Computing Applications And Services
Fig. 5. The main components of cloud computing [37]
The proliferation of cloud-based computing services has
opened up new avenues for managing massive datasets. What
this means is that the advent of big data serves to hasten the
maturation of cloud computing. Cloud computing and its
offshoot, cloud storage, have made it possible to effectively
handle massive data sets. Big data acquisition and analysis in
the cloud may be sped up with the use of parallel computing
power [38].

Smart Energy
Social
Sustainability
“
Smartization
”
Smart Grid IoT Integration
Smart Grid
and Smart
Cities
1. Power & IT Management
2. Integrated City Mobility
3. Security Management
4. Environment Information System
5. Weather Intelligence
6. E-Mobility
Smart Mobility Smart Water Smart Public Service Smart Homes and
Buildings
Smart Grid
Automation &
Flexible Distribution
Smart Metering
Management and
Demand Response
Renewable
Integration and
Micro Grid
Real-Time Smart
Grid Software Suite
Gas Distribution
Management
EV Charging
Infrastructure &
Supervision Services
Traffic Management
Tolling and
Congestion Charging
Integrated Mobility
•
Public Transit
•
Traveler
Information
Distribution
Management & Leak
Detection
Power Control &
Security Systems
Integration
Stormwater
Management and
Urban Flooding
Public Safety
•
Video Surveillance
•
Emergency
Management
Digital City Services
•
e-Government
•
Education
•
Healthcare
•
Tourism
Street Lighting
Management
High-Performance
Buildings
•
Energy Efficiency &
•
Security Solutions
Energy Services
Efficient Homes
•
Home Energy
Management
Connection to the Smart
Grid
Economical Technical Policy
Big Data Analytics
Raw Data Clean Data Mo dels Production Components Monitoring Data
Temporary Data Reports
Source Data Access Data Processing Modeling Deployment Monitoring
Experiments, Exploratory
Analysis, Reporting
Fig. 6. A visual representation of equipment and methods for collecting data in the smart grid and smart cities Based on IoT
b) Internet of Things
A large number of network sensors are installed in various
devices around the world, collecting various types of data
such as network communication data, environmental data,
geographic data, astronomical data, and more. Since the
information resources collected in the Internet of Things are
from various environments, the big data generated by the
Internet of Things has different characteristics compared to
general big data [39]. Heterogeneity, diversity, non-
structuredness, redundancy, and rapid growth are some of the
characteristics of big data generated by the Internet of Things.
Figure 6 shows the equipment and methods for collecting
information in the smart grid and smart cities Based on the
Internet of Things platform.
An Intel report has mentioned three characteristics of big
data in the Internet of Things:
* Abundant terminals that produce large amounts of data.
* The Internet of Things often produces semi-structured
or unstructured data.
* Only after analysis is data from the Internet of Things
valuable.
c) Data Center
Data centers are not just centralized storage facilities for
data by one organization; they also have additional
responsibilities. Data collection, data processing, data
organization, data value optimization, and operations are all
performed in a data center. A data center organizes and
maintains a large volume of data in accordance with its
primary goal and development route. Big data's rise has
presented data centers with both possibilities and obstacles
for expansion [40-42].
V. CONCLUSION
In this review article, big data and related concepts
including definitions, features, challenges, and leading issues
and technologies in data analysis, storage, and data
visualization have been discussed. Additionally, the Internet
of Things, cloud computing, and data centers as technologies
closely related to big data that contribute to its progress and
development have been explained. Despite significant
advancements in the field of big data, compared to other
technologies, there are still significant shortcomings in this
area and many issues remain to be resolved. Standardization,
technologies related to big data storage, real-time
performance, big data management, search, exploration and
analysis of big data, the development of big data applications
in various fields, data security, and mechanisms related to big
data security and privacy are issues that researchers must
address and provide appropriate solutions.

REFERENCES
[1] Escobar, C. A., McGovern, M. E., & Morales-Menendez, R. (2021).
Quality 4.0: a review of big data challenges in manufacturing. Journal
of Intelligent Manufacturing, 32, 2319-2334.
[2] Kong, L., Liu, Z., & Wu, J. (2020). A systematic review of big data-
based urban sustainability research: State-of-the-science and future
directions. Journal of Cleaner Production, 273, 123142.
[3] Nti, I. K., Quarcoo, J. A., Aning, J., & Fosu, G. K. (2022). A mini-
review of machine learning in big data analytics: Applications,
challenges, and prospects. Big Data Mining and Analytics, 5(2), 81-97.
[4] Hajjaji, Y., Boulila, W., Farah, I. R., Romdhani, I., & Hussain, A.
(2021). Big data and IoT-based applications in smart environments: A
systematic review. Computer Science Review, 39, 100318.
[5] Mallappallil, M., Sabu, J., Gruessner, A., & Salifu, M. (2020). A review
of big data and medical research. SAGE open medicine, 8,
2050312120934839.
[6] Janev, V. (2021). Semantic intelligence in big data applications. Smart
Connected World: Technologies and Applications Shaping the Future,
71-89.
[7] Lampropoulos, G. (2023). Artificial Intelligence, Big Data, and
Machine Learning in Industry 4.0. In Encyclopedia of Data Science
and Machine Learning (pp. 2101-2109). IGI Global.
[8] Moradi, J., Shahinzadeh, H., Nafisi, H., Marzband, M., &
Gharehpetian, G. B. (2019, December). Attributes of big data analytics
for data-driven decision making in cyber-physical power systems.
In 2020 14th international conference on protection and automation of
power systems (IPAPS) (pp. 83-92). IEEE.
[9] Kabalcı, Y., & Ali, M. (2019, June). Energy Internet: A Novel Vision
for Next-Generation Smart Grid Communications. In 2019 1st Global
Power, Energy and Communication Conference (GPECOM) (pp. 96-
100). IEEE.
[10] Pramanik, S., & Bandyopadhyay, S. K. (2023). Analysis of Big Data.
In Encyclopedia of Data Science and Machine Learning (pp. 97-115).
IGI Global.
[11] Pal, K. (2023). A Review of Big Data Analytics for the Internet of
Things Applications in Supply Chain Management. Applied AI and
Multimedia Technologies for Smart Manufacturing and CPS
Applications, 221-245.
[12] Escobar, C. A., McGovern, M. E., & Morales-Menendez, R. (2021).
Quality 4.0: a review of big data challenges in manufacturing. Journal
of Intelligent Manufacturing, 32, 2319-2334.
[13] Shahinzadeh, H., Zanjani, S. M., Moradi, J., Fayaz-dastgerdi, M. H.,
Yaïci, W., & Benbouzid, M. (2022, October). The Transition Toward
Merging Big Data Analytics, IoT, and Artificial Intelligence with
Blockchain in Transactive Energy Markets. In 2022 Global Energy
Conference (GEC) (pp. 241-246). IEEE.
[14] Sharma, A., Singh, G., & Rehman, S. (2020). A review of big data
challenges and preserving privacy in big data. Advances in Data and
Information Sciences: Proceedings of ICDIS 2019, 57-65.
[15] Ahmed, H., & Ismail, M. A. (2020). Towards a novel framework for
automatic big data detection. IEEE Access, 8, 186304-186322.
[16] Zanjani, S. M., Zanjani, S. H., Shahinzadeh, H., Rezaei, Z., Kaviani-
Baghbaderani, B., & Moradi, J. (2022, November). Big Data Analytics
in IoT with the Approach of Storage and Processing in Blockchain.
In 2022 6th Iranian Conference on Advances in Enterprise
Architecture (ICAEA) (pp. 1-6). IEEE.
[17] Reda, O., Sassi, I., Zellou, A., & Anter, S. (2020, September). Towards
a data quality assessment in big data. In Proceedings of the 13th
International Conference on Intelligent Systems: Theories and
Applications (pp. 1-6).
[18] Shi, Y. (2022). Advances in big data analytics. Adv Big Data Anal.
[19] Keskar, V., Yadav, J., & Kumar, A. (2022). Perspective of anomaly
detection in big data for data quality improvement. Materials Today:
Proceedings, 51, 532-537.
[20] Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big
data service architecture: a survey. Journal of Internet
Technology, 21(2), 393-405.
[21] Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social
media big data analytics: A survey. Computers in Human
Behavior, 101, 417-428.
[22] Ren, S. (2022). Optimization of enterprise financial management and
decision-making systems based on big data. Journal of
Mathematics, 2022, 1-11.
[23] Cravero, A., Pardo, S., Sepúlveda, S., & Muñoz, L. (2022). Challenges
to Use Machine Learning in Agricultural Big Data: A Systematic
Literature Review. Agronomy, 12(3), 748.
[24] Demirol, D., Das, R., & Hanbay, D. (2022). A key review on security
and privacy of big data: issues, challenges, and future research
directions. Signal, Image and Video Processing, 1-9.
[25] Mazumdar, S., Seybold, D., Kritikos, K., & Verginadis, Y. (2019). A
survey on data storage and placement methodologies for cloud-big data
ecosystem. Journal of Big Data, 6(1), 1-37.
[26] Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data
analytics and firm performance: Findings from a mixed-method
approach. Journal of Business Research, 98, 261-276.
[27] Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data
analytics for manufacturing internet of things: opportunities,
challenges and enabling technologies. Enterprise Information
Systems, 14(9-10), 1279-1303.
[28] ur Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P.,
& Perera, C. (2019). The role of big data analytics in industrial Internet
of Things. Future Generation Computer Systems, 99, 247-259.
[29] Talha, M., Abou El Kalam, A., & Elmarzouqi, N. (2019). Big data:
Trade-off between data quality and data security. Procedia Computer
Science, 151, 916-922.
[30] Gupta, R., Jadav, N. K., Nair, A., Tanwar, S., & Shahinzadeh, H. (2022,
September). Blockchain and AI-based Secure Onion Routing
Framework for Data Dissemination in IoT Environment Underlying 6G
Networks. In 2022 Sixth International Conference on Smart Cities,
Internet of Things and Applications (SCIoT) (pp. 1-6). IEEE.
[31] Han, H., & Trimi, S. (2022). Towards a data science platform for
improving SME collaboration through Industry 4.0
technologies. Technological Forecasting and Social Change, 174,
121242.
[32] Rao, T. R., Mitra, P., Bhatt, R., & Goswami, A. (2019). The big data
system, components, tools, and technologies: a survey. Knowledge and
Information Systems, 60, 1165-1245.
[33] Pavithra, N., & Manasa, C. M. (2021, December). Big Data Analytics
Tools: A Comparative Study. In 2021 IEEE International Conference
on Computation System and Information Technology for Sustainable
Solutions (CSITSS) (pp. 1-6). IEEE.
[34] Ikegwu, A. C., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo,
O. R. (2022). Big data analytics for data-driven industry: a review of
data sources, tools, challenges, solutions, and research
directions. Cluster Computing, 25(5), 3343-3387.
[35] Archana Acharya, T., & Veda Upasan, P. (2020). A stitch in time saves
nine: a Big Data analytics perspective. In Smart Technologies in Data
Science and Communication: Proceedings of SMART-DSC 2019 (pp.
227-243). Springer Singapore.
[36] Abualigah, L., Diabat, A., & Elaziz, M. A. (2021). Intelligent workflow
scheduling for Big Data applications in IoT cloud computing
environments. Cluster Computing, 24(4), 2957-2976.
[37] Srinivas, J., Das, A. K., & Rodrigues, J. J. (2020). 2PBDC: privacy-
preserving bigdata collection in cloud environment. The Journal of
Supercomputing, 76, 4772-4801.
[38] Bagherzadeh, L., Shahinzadeh, H., Shayeghi, H., Dejamkhooy, A.,
Bayindir, R., & Iranpour, M. (2020, July). Integration of cloud
computing and IoT (CloudIoT) in smart grids: Benefits, challenges, and
solutions. In 2020 International Conference on Computational
Intelligence for Smart Power System and Sustainable Energy
(CISPSSE) (pp. 1-8). IEEE.
[39] Kabalci, Y., Kabalci, E., Padmanaban, S., Holm-Nielsen, J. B., &
Blaabjerg, F. (2019). Internet of things applications as energy internet
in smart grids and smart environments. Electronics, 8(9), 972.
[40] Shahinzadeh, H., Mirhedayati, A. S., Shaneh, M., Nafisi, H.,
Gharehpetian, G. B., & Moradi, J. (2020, December). Role of joint 5G-
IoT framework for smart grid interoperability enhancement. In 2020
15th International Conference on Protection and Automation of Power
Systems (IPAPS) (pp. 12-18). IEEE.
[41] Shahinzadeh, H., Moradi, J., Gharehpetian, G. B., Nafisi, H., & Abedi,
M. (2019, January). IoT architecture for smart grids. In 2019
International Conference on Protection and Automation of Power
System (IPAPS) (pp. 22-30). IEEE.
[42] Moazzami, M., Sheini-Shahvand, N., Kabalci, E., Shahinzadeh, H.,
Kabalci, Y., & Gharehpetian, G. B. (2021, October). Internet of things
architecture for intelligent transportation systems in a smart city.
In 2021 3rd Global Power, Energy and Communication Conference
(GPECOM) (pp. 285-290). IEEE.