University of Campbellsville Big Data Analytics in Manufacturing of IoT Paper The recent advances in information and communication technology (ICT) has pro

University of Campbellsville Big Data Analytics in Manufacturing of IoT Paper The recent advances in information and communication technology (ICT) has promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while it can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data.

For this assignment, you are required to research the benefits as well as the challenges associated with Big Data Analytics for Manufacturing Internet of Things.

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please refer attached documents Review
Applied Medical Informatics
Vol. 41, No. 2 /2019, pp: 53-64
A Systematic Review of Big Data Potential to Make Synergies
between Sciences for Achieving Sustainable Health: Challenges
and Solutions
Shirin AYANI1, Khadijeh MOULAEI2,*, Sarah DARWISH KHANEHSARI1, Maryam
JAHANBAKHSH3, Faezeh SADEGHI4
Rayavaran Medical Informatics Company, Smart Hospital and Telemedicine Research Center,
Tehran, Iran.
2 Iran University of Medical Sciences, School of Management and Medical Information, Khadijeh
Moulaei, No.2, Corner of Hamsayegan Street, Valiasr Ave, Tehran, Iran.
3 Isfahan University of Medical Sciences, Isfahan, Iran.
4 Faran (Mehr Danesh) Non-governmental Institute of Virtual Higher Education, Department of
English Language, Tehran, Iran.
E-mail: Moulaei.kh@tak.iums.ac.ir
1
* Author to whom correspondence should be addressed; Tel: +982188783115; Fax: +982188661654
Received: January 9, 2019 /Accepted: May 16, 2019/ Published online: June 30, 2019
Abstract
The importance of the healthcare industry, benefiting from the synergies between sciences, adds to
the necessity of discovering knowledge, which is achievable with big data analytics tools. The purpose
of this article is to examine the challenges and provide solutions for using big data in the healthcare
industry. The methods of this article are derived from PRISMA guidelines and its models. A variety
of databases and search engines including PubMed, Scopus, Elsevier, IEEE, Springer, Web of
Science, Proquest, and Google Scholar were searched according to credible keywords. The results of
the present study showed that the problems associated with the use of big data in the healthcare
industry could be classified in four groups including “data gathering, storage and integration”, “data
analysis”, “knowledge discovery and information interpretation”, and “infrastructure”. Although the
results point to a high frequency of challenges in the “data gathering, storage and integration” group,
the greatest weight of problems, due to their importance, appears to be visible in the “infrastructure”
group. Considering the numerous benefits of using big data, it is imperative to identify the challenges
and resolve them accurately. It is expected that all the barriers can be removed soon. Big data analytics
tools will be able to offer the best possible strategies based on human individual and social conditions
in the context of artificial intelligence methods.
Keywords: Big data; Data analysis; Data integration; Internet Of Things (IOT); Medical informatics;
Biological informatics
Introduction
At the end of the 1990s, in order to make the right decisions and gain a better understanding of
market behaviors, the role of gathering data, integrating and interpreting business information was
emphasized by the researchers. For this purpose, the term “Big Data” was introduced by Michael
Cox and David Ellsworth in 1997 [1]. Big data is referred to as a set of data whose volume is beyond
53
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Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh
SADEGHI
the capabilities of current databases and technologies. Therefore, in order to analyze these data,
databases with volume capabilities higher than terabyte and Exabyte are needed [2].
Big data separating factors from other data include volume (scale and size of data in storage),
velocity (the speed in which this data is generated, produced, created, refreshed, and streamed),
variety (multiple different forms of the data), veracity (uncertainty of the data that leads to confidence
or trust in the data), and value (deriving business value and insights from the data) [3-6].
Additionally, the outcomes of big data analyses contribute to the identification of unknown
patterns that show the causal relationships between different events in a wide range of information
in the real world and ends in knowledge production [7].
Before the introduction of the concept of big data, with the emergence of information
revolutionary age, it was possible to collect the data associated with healthcare activities in related
centers [8], and healthcare providers who exploited health information systems like hospital
information systems (HISs) started generating massive data [9]. While the information systems were
used, specialists’ level of expectation went up, and another need was formed: how to understand the
multidimensional causal relationships associated with individual and social health. Simultaneously,
big data was introduced to the world and health researchers showed interest in this field [10-12]. Big
data in the healthcare industry refers to a set of data related to diagnosis and treatment of diseases,
contagious diseases, nutritional status, climate, political status, security of a country (especially war
conditions), cultural status, social system, regional and vernacular status, metabolism and
micronutrients (ions), genetics and cells, the economic status, the insurance companies’ bills and other
things [1, 10, 13-15]. To collect these data, equipment, and tools such as the Internet, smartphones,
social media, sensors and databases – which are related to the scientific societies and hospitals- are
used as well as clinical and hospital information systems [14, 16]. After gathering data, it was possible
to discover unknown patterns associated with some features carried out by the use of advanced
analytics tools. These features that are used by advanced analytics tools include individual and social
disease management, changes in habits and pathogenic conditions, prevention, diagnosis and
treatment of diseases especially rare diseases, forecasting, individualization of health services, support
and supervision of health social services [14]. One of the valuable advantages of analyzing data in the
healthcare industry is the knowledge discovery beyond researchers’ imagination, which ends in the
successful medical decision making of healthcare providers and producing clinical decision support
systems [3, 17, 18]. Analysis of this data is done by the use of particular computing technologies,
which requires specific hardware structures and operating platforms. At this time, operating
platforms, hardware structures, and advanced technologies for using big data are acceptably
reachable. Extensible Markup Language (XML), Web Services, Database Management Systems,
Hadoop, SAP HANA and analytical software, are their examples [1, 14, 19, 20]. On the other hand,
because of the considerable data volume, it is impossible to store and transfer data using traditional
methods and technologies. Today, SQL, MySQL, and Oracle databases are widely used for
implementing information systems, while for storing large data, Apache and Nosql databases are
required [1].
The big data formation is performed in three stages of collecting, processing and visualizing data
[15, 21]. Each step is accompanied by significant challenges that prevent successful implementation
of big data operationally. Therefore, the purpose of this study was to survey the applications of big
data in the healthcare industry in order to increase the synergy, as well as achieve sustainable health
to survey challenges and provide suitable solutions.
Material and Method
The methods of this article are derived from PRISMA guidelines and its models (for further
information see www.prisma-statement.org).
54
Appl Med Inform 41(2) June/2019
A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable
Health: Challenges and Solutions
Information Resources
A variety of databases and search engines including Pubmed, Scopus, Elsevier, IEEE, Springer,
Web of Science, Proquest and Google Scholar were searched according to credible keywords and the
pre-specified search strategy mentioned below. The databases were searched from May 24, 2018, to
July 30, 2018.
Keywords and Search Strategy
The keywords of this research used in the search strategy are as follows: Big Data, Data Sets, Big
Data Analytics, Big Data Analytics Tools, Administrative Data, Structured Data, Unstructured Data,
Business Process Analytics, Real-time Analytics, Information Technology Management, Health Care,
Health Care Industries, E-Health Solutions, Social Health, Clinical Registries, Bioinformatics, Health
Informatics, Medical Informatics, Sensor Informatics, Challenge, Solution, Problems.
The applied search strategy was [Big data* AND (Business Process OR Data sets OR Real-time
OR Administrative Data OR Unstructured Data OR Structured Data OR Information Technology
Management OR Resource-based Theory) AND (Solutions* OR Challenges* OR Problems*) AND
(Healthcare OR E-Health OR Social Health OR Public Health OR Clinical Registries OR Medical
OR *Informatics OR Bioinformatics)].
Inclusion, Exclusion and Data Extraction
The inclusion criteria were as follows:
Full-text resources were available.
The articles were published in the last 10 years.
? The articles were published in scientific and high-ranking journals.
? Big data challenges and their potential solutions in the healthcare industry were suggested.
The exclusion criteria were as follows:
? The challenges were unrelated to the big data in the healthcare industry.
? The definitions were not clear and related to the challenges and their solutions.
? Concerning challenges and their solutions, the articles were not comprehensive.
First, all the challenges introduced in the selected articles were extracted. The challenges were
categorized into four groups: “data gathering, storage and integration”, “data analysis”, “knowledge
discovery and information interpretation” and “infrastructure”.
Then, to find or propose solutions for each challenge, the necessary examinations were carried
out and solutions were put in different groups along with their related challenges.
?
?
Results
The search results are shown in Figure 1, and the results of these studies are illustrated in Table
1. In this table, the problems were grouped, and solutions to each problem were specified.
Data Gathering, Storage and Integration
Over the years, the volume of generated data has been increased significantly by healthcare
organizations. These data are collected from various sources as well as by various tools and
technologies such as information systems, cell phones, wireless sensors, RFID (radio frequency
identification) and so on [22, 25]. Therefore, in order to create big data in the healthcare industry,
heterogeneous sources and different formats are used [28]. For this reason, it is normal to face some
problems such as noise, confounding factors, and inconsistencies in the gathered data collection [25].
Also, during data gathering, for some reasons such as inadequate storage space and gathering data
from various sources, some valuable data can be possibly ignored or removed [22].
Ladha and his colleagues argued that lost data might cause the creation of invalid patterns.
Therefore, three potential constraints in gathering data should be taken into account. First, some data
[
Appl Med Inform 41(2) June/2019
55
Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh
SADEGHI
may be missing or artificial. Second, in some cases, some found data could lead to the definition of
ambiguous or contradictory variables. Third, some variables can act as confounding factors during
the analysis. The measure of the error rate in each one of these three stages indicates the
ineffectiveness of big data and presents the risk of its use by conducting scientific researches [11].
It is noteworthy that in order to avoid gathering data redundancy, it is necessary to identify and
provide methods to prevent data abundance and redundant data storage [35].
Figure 1. Literature search criteria with inclusion and exclusion criteria
3.2. Data Analysis
Analyzing big data, some errors occurring during the data gathering and those errors that were
hidden in the databases were identified and corrected as much as possible. Current information
systems in the healthcare industry are not integrated, so by gathering data in a big data repository,
these errors can be identified [39]. Because of this and due to the lack of practical methods for
accurate and rapid processing of massive volume of data, big data analysis brings up a critical issue
[32]. However, since analyzing these health data leads to significant worthwhile outcomes, many
researchers are trying to overcome these challenges [40, 41]. For example, Mathew and Pillai
described the SAP HANA platform in data analysis as highly useful. SAP HANA utilizes some data
mining methods for analyzing complex and large volumes of data[3]. It is also imperative to use some
techniques such as networks, graphs, and charts for data analysis [42-44]. Note that if no pattern is
extracted, it is necessary to re-formulate and repeat the analysis step[14]. The system analyst’s skill in
recognizing the patterns, the definition of the rules, process modeling, error detection, and setting
error threshold are important [45]. Evaluating the results of processing big data in order to validate
the acquired patterns is also a significant challenge that must be done by professional multidisciplinary
teams [1].
Another critical point is the need for a remarkable space of temporary memory that is accessible
on unique hardware platforms while analyzing the data [13].
56
Appl Med Inform 41(2) June/2019
A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable
Health: Challenges and Solutions
Table 1. Big data challenges and their solutions in the healthcare industry
ID Challenge
Challenge
Group
1 Data gathering, Difficulty in gathering and
integrating data
storage and
integration
Lack of time priority in
gathering data
Data ambiguity
2
3
4
Solution
Reference
Creating distributed databases which are [2,22,23]
interrelated and choosing a scientific manner
to gather data from health centers nationally
Having data gathering patterns so that data [11,18]
interconnections are considered
Using various scientific techniques to clarify
[13]
data and code them and defining flexible
formats for high-clarity data gathering
Heterogeneity of different Using semantic networks and ontological [10,24,25]
sources of gathered data interpretations
Artificial nature of some Using Loss data analysis and providing clear [11,26,27]
data elements due to the definitions of variables
loss of other relevant data
Existence of noisy data
Using preprocessing techniques (like PCA)
[26]
Massive data
Centralized data monitoring, preventing [17,28,29]
redundancy and filtering unnecessary data
Data analysis
Difficulty in analyzing big Using advanced computing technologies and [30,31]
data
processing parallelism
Difficulty in extracting Using advanced data mining techniques to [13,23]
patterns and models
obtain valuable models and patterns
Uncertainty about the Evaluating is done to confirm the accuracy and [13,23]
accuracy of extracted value of the model
information, patterns, and
models
Knowledge
Interpretation of patterns Information interpretation after getting help
[1]
from experts in multidisciplinary or
discovery and
information
interdisciplinary fields
interpretation Difficulty in representing Defining flexible formats to represent the
[23]
knowledge
interpreted information. The multidisciplinary
or
interdisciplinary
specialists
should
document extracted knowledge
Studying the
The validity of explicit knowledge should be
[26]
generalizability of
confirmed statistically and epidemiologically
knowledge and the
accuracy of explicit
knowledge
Infrastructure Absence of specific rules Defining a set of rules in the form of specific [13,32,33]
and standards
frameworks to achieve standards and to apply
the rules in a correct way
Immaturity of required Identifying and implementing modern [25,27,36]
infrastructure
technologies and then complying the existing
infrastructure with them
Lack of a stakeholder
Making collective business policies for [32, 34]
applicability of big data
Data security (availability, Using advanced security standards and related [23,35,36]
confidentiality,
and technologies and then continuing the
integrity]
monitoring over data accuracy and quality
Lack of proper bandwidth Using high bandwidth or special data transfer [26, 37]
for data transfer
protocols
Lack of some important Applying information systems like transaction
[38]
information systems for processing systems, registration systems, and
storing data digitally as in decision support systems
Electronic
Medical
Records
[
Appl Med Inform 41(2) June/2019
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Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH KHANEHSARI, Maryam JAHANBAKHSH, Faezeh
SADEGHI
Knowledge Discovery and Information Interpretation
One of the major challenges of big data is the interpretation of information and patterns after the
analysis. At this point, the leading question is how new knowledge can be obtained from the
aggregation of information; then how it can be documented, displayed, and verified [46].
Knowledge discovery during data analysis based on the Internet of Things is one of the biggest
challenges that professionals encounter. Devices and software based on the Internet of Things
generate substantial data streams, which result in the mass production of data. Researchers can use
Artificial Intelligence and Machine Learning to interpret this information. Machine Learning
algorithms and Intelligent Computing are considered achievable solutions for analyzing big data
based on the Internet of Things [22, 47]. On the other hand, the simple representation of the
knowledge extracted from big data is a serious issue. If it is not possible to demonstrate the novel
knowledge, it is impossible to develop and apply it. Therefore, to eliminate the complexity of the
discovered patterns and create relations between them, experts’ opinions in different subjects areas
and from other perspectives are needed [35]. Identifying invalid patterns and accrediting extracted
knowledge also needs multidisciplinary expert teams. Since a pattern may be taken from a specialized
field to the others, it is not easy to perceive the relationships between them and to evaluate the results
[35, 48].
Infrastructure
Infrastructure refers to the use of a combination of hardware, software, and services that should
be robust, supportive, and scalable [49]. The infrastructure of big data refers to all cases that can
support its lifecycle on a large scale over time. In the infrastructure of big data, it is essential to be
ensured of data security (including confidentiality, integrity, and availability) [50, 51). There are many
challenges in healthcare that make it impossible to create a secure infrastructure; for example, high
bandwidth is effective to the speed of data stream [32]. Communication channels based on highbandwidth help gathering and managing data and protecting its security [52].
On the other hand, storing and retrieving data from clinical and hospital information systems is a
very complex, time-consuming, expensive endeavour, which requires a robust infrastructure.
Databases and computing systems such as Mongo, Hadoop, and MapReduce can provide proper
infrastructure for applying big data[53]. Concerning the characteristics of health data, the Mongo
database can provide high performance; accessibility and scalability for big data. This can successfully
create data repositories [54, 55].
The conceptual framework of big data analysis in the healthcare industry varies from the
traditional data analysis frameworks; so, processing should be distributed and performed all across
the nodes of the network. Therefore, instead of using a machine, the processing is broken down and
carried out by variant machines, and their analytics can be performed in parallel with the help of
MapReduce [56, 57]. Besides, open source platforms such as Hadoop, which operates in clo…
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