1Big Data Analytics for Manufacturing Internet ofThings.docx

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Big Data Analytics for Manufacturing Internet of
Things: Opportunities, Challenges and Enabling
Technologies
Hong-Ning Dai, Hao Wang, Guangquan Xu, Jiafu Wan,
Muhammad Imran
Abstract— The recent advances in information and commu-
nication technology (ICT) have promoted the evolution of con-
ventional computer-aided manufacturing industry to smart data-
driven manufacturing. Data analytics in massive manufacturing
data can extract huge business values while can also result
in research challenges due to the heterogeneous data types,
enormous volume and real-time velocity of manufacturing data.
This paper provides an overview on big data analytics in
manufacturing Internet of Things (MIoT). This paper first starts
with a discussion on necessities and challenges of big data
analytics in manufacturing data of MIoT. Then, the enabling
technologies of big data analytics of manufacturing data are
surveyed and discussed. Moreover, this paper also outlines the
future directions in this promising area.
Index Terms— Smart Manufacturing; Data Analytics; Data
Mining; Internet of Things
I. INTRODUCTION
The manufacturing industry is experiencing a paradigm shift
from automated manufacturing industry to “smart manufactur-
ing” [1]. During this evolution, Internet of Things (IoT) plays

an important role of connecting the physical environment of
manufacturing to the cyberspace of computing platforms and
decision-making algorithms, consequently forming a Cyber-
Physical System (CPS) [2]. We name such industrial IoT
dedicated to manufacturing industry as manufacturing IoT
(MIoT) in this paper.
MIoT consists of a wide diversity of manufacturing equip-
ments, sensors, actuators, controllers, RFID tags and smart me-
ters, which are connected with computing platforms through
wired or wireless communication links. There is a surge of
big volume of data traffic generated from MIoT. The MIoT
data is featured with large volume, heterogeneous types (i.e.,
structured, semi-structured, unstructured) and is generated in a
real-time fashion. The analytics of MIoT data can bring many
benefits, such as improving factory operation and produc-
tion, reducing machine downtime, improving product quality,
H.-N. Dai is with Faculty of Information Technology, Macau
University of
Science and Technology, Macau. E-mail: [email protected]
H. Wang is with Faculty of Engineering and Natural Sciences,
Norwegian
University of Science & Technology, Gjøvik, Norway. Email:
[email protected]
G. Xu is with Tianjin Key Laboratory of Advanced Networking,
), College
of Intelligence and Computing, Tianjin University, Tianjin,
China Email:
[email protected]
J. Wan is with School of Mechanical & Automotive Engineer-
ing, South China University of Technology, Guangzhou, China
Email:
[email protected]
M. Imran is College of Computer and Information Sciences,
King Saud

University, Saudi Arabia, Email: [email protected]
enhancing supply chain efficiency and improving customer
experience [3]–[5]. However, there are also many challenges
in data analytics in MIoT in the different phases of the whole
life cycle of data analytics.
There are several surveys on data analytics in manufacturing
industry. The work of [5] proposes a data-driven smart manu-
facturing framework and provides several application scenarios
based on this conceptual framework. The necessities of big
data analytics in smart manufacturing are summaried in [6].
The work of [4] provides an overview on data analytics in
manufacturing with a case study. Tao and Qi presents an
overview of service-oriented manufacturing in [7]. However,
most of the aforementioned studies lack of the introduction of
enabling technologies corresponding to the challenges, which
are of interest to both academic researchers and industrial
practitioners.
Therefore, the aim of this paper is to provide an overview
on data analytics in MIoT from opportunities, challenges and
enabling technologies. The main contributions of this paper
can be summarized as follows.
• We provide a summary on key characteristics of MIoT
and a life cycle of big data analytics for MIoT data.
We also discuss necessities and challenges of big data
analytics in MIoT.
• We present an overview on enabling technologies of
big data analytics for MIoT from the aspects of data
acquisition, data preprocessing and data analytics.
• We given an outline of future research directions in
aspects of security, privacy, fog computing and new data
analytics methods.

The rest of this paper is organized as follows. Section II
gives the discussion on necessities and challenges of big data
analytics in MIoT. Section III introduces enabling technologies
of big data analytics in MIoT. Section IV discusses the future
research directions. Finally, this paper is concluded in Section
V.
II. NECESSITIES AND CHALLENGES OF BIG DATA
ANALYTICS FOR MANUFACTURING INTERNET OF
THINGS
In this section, we first introduce the key characteristics of
Manufacturing Internet of Things in Section II-A. We then
introduce the life cycle of big data analytics for MIoT in
Section II-B. We next discuss the necessities of big data
analytics for MIoT in Section II-C and the challenges in
Section II-D.
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A. Key characteristics of Manufacturing Internet of Things
In this paper, we roughly categorize IoT into consumer
Internet of Things (CIoT) and Manufacturing Internet of
Things (MIoT). Table I compares MIoT with CIoT. In contrast
to MIoT, CIoT mainly serve for consumers. Hence, CIoT
mainly consists of consumer devices (e.g., smart phones,
wearable electronics) and smart appliances (e.g., refrigerators,
TVs, washing machines). CIoT mainly aims to improve user
experience while MIoT mainly focuses on improving factory
operations and production, reducing the machine downtime
and improving product quality. Moreover, MIoT usually works
in harsh industrial environment (like vibrated, noisy and ex-
tremely high/low temperature) while CIoT works in moderate
environment. In addition, MIoT applications usually require
high data-rate network connection with low delay while CIoT
applications have relaxed requirement on network connection.

Furthermore, MIoT systems are usually mission-critical and
sensitive to system failure or machinery downtime while CIoT
systems are non-mission-critical.
In this paper, we mainly focus on MIoT. The MIoT ensures
the connection of various things (smart objects) mounted with
various electronic or mechanic sensors, actuators, instruments
and software systems which can sense and collect information
from the physical environment and then make actions on
the physical environment. During this procedure, the data
analytics plays an important role in extracting informative
values, forecasting the coming events and predicting the in-
crement/decrements of products.
B. Life cycle of big data analytics for MIoT
We first introduce the life cycle of big data analytics for
MIoT. Figure 1 shows that the life cycle of big data analytics
for MIoT consists of three consecutive stages: 1) Data Acqui-
sition, 2) Data Preprocessing and Storage, 3) Data Analytics.
There are other taxonomies [5], [8], [9]. We categorize the life
cycle of big data analytics into the above three stages since
this taxonomy can accurately capture the key features of big
data analytics in MIoT.
1. Data acquisition consists of data collection and data
transmission. Firstly, data collection involves acquiring
raw data from various data sources in the whole man-
ufacturing process via dedicated data collection tech-
nologies. For example, RFID tags are scanned by RFID
readers in product warehouse. Then, the collected data
will be transmitted to the data storage system through
either wired or wireless communication systems. Details
about enabling technologies of data acquisition are given
in Section III-A.

2. Data preprocessing and storage. After data collection,
the raw data needs to be preprocessed before keeping
them in data storage systems because of the big volume,
redundancy, uncertainty features of the raw data [4]. The
typical data preprocessing techniques include data clean-
ing, data integration and data compression. Data storage
refers to the process of storing and managing massive
data sets. We divide the data storage system into two
components: storage infrastructure and data management
software. The infrastructure not only includes the storage
devices but also the network devices connecting the
storage devices together. In addition to the networked
storage devices, data management software is also nec-
essary to the data storage system. Details about enabling
technologies of data preprocessing and data storage are
given in Section III-B.
3. Data analytics. In data analysis phase, various data ana-
lytical schemes are used to extract valuable information
from the massive manufacturing data sets. We roughly
categorize the data analytical schemes into four types:
(i) statistic modelling, (ii) data visualization, (iii) data
mining and (iv) machine learning. Details about enabling
technologies of data analysis are presented in Section
III-C.
C. Necessities of big data analytics for MIoT
There is an enormous amount of data generated from the
whole manufacturing chain consisting of raw material supply,
manufacturing, product distribution, logistics and customer
support, as shown in Figure 1. Such “big data” needs to be
extensively analysed so that some valuable and informative
information can be extracted.

We summarize the reasons of big data analytics for MIoT
as follows:
• Improving factory operations and production. The pre-
dictive analytics of manufacturing data and customer
demand data can help to improve machinery utilization
consequently enhancing factory operations. For example,
the demands for certain products are often related to
weather or seasonal conditions (e.g., down coats related
to the cold weather). Forecasting a cold wave can be
used to make pro-active allocation of machinery resources
and pre-purchasing raw materials to fulfill the upsurge
demands.
• Reducing machine downtime. The prevalent sensors de-
ployed throughout the whole product line can collect
various data reflecting machinery status. For example, the
analysis of machinery health data can help to identify
the root cause of failure consequently reducing machine
downtime [4]. Moreover, the sensory data from automatic
assembly line can also be used to determine excessive
load of machines so as to balance the loads among
multiple machines [10].
• Improving product quality. On one hand, the analysis of
market demand and customer requirement can be used to
improve the product design in reflecting product improve-
ments. During the product manufacturing procedure, the
analysis of manufacturing data can help to reduce the
ratio of defective goods by identifying the root cause. As
a result, the product quality can be improved.
• Enhancing supply chain efficiency. The proliferation of
various sensors, RFID and tags during supplier, manufac-
turing and transportation generates massive supply chain
data, which can be used to analyse supply risk, predict

delivery time, plan optimal logistic route, etc. Moreover,
the analysis of inventory data can reduce the holding
3
TABLE I
COMPARISON BETWEEN MIOT AND CIOT
Manufacturing IoT Consumer IoT
Goal Manufacturing-industry Centric Consumer Centric
Devices
Machines, Sensors, Controllers, Actuators,
Smart meters
Consumer devices and Smart appliances
Working
Environment
Harsh (vibration, noisy, extremely high/low
temperature)
Moderate
Data rate High (usually) Low or average
Delay Delay sensitive Delay tolerant
Mission Mission-critical Non-mission-critical
costs and fulfill the dynamic demands by establishing
safety stock levels. In addition, big data analytics on IoT-

enabled intelligent manufacturing shops [3] can also help
to make accurate logistic plan and schedules. As a result,
the system efficiency can be greatly improved.
• Improving customer experience. Companies can obtain
customer data from various sources, such as sales chan-
nels, partner distributors, retailers, social media platforms.
Then, big data analytics on customer data offers de-
scriptive, predictive and prescriptive solutions to enable
companies to improve product design, quality, delivery,
warrant and after-sales support. As a result, the customer
experience can be improved. For example, the IoT data in
the whole food supply-chain is also beneficial to prevent
mischievous actions and guarantee food safety [11].
D. Challenges of big data analytics for MIoT
MIoT data has the following characteristics: (1) massive
volume, (2) heterogeneous data types, (3) being generated in
real-time fashion and (4) bringing huge both business value
and social value. The unique features cause the research
challenges in big data analytics for MIoT. We summarize the
challenges in the following aspects.
1. Challenges in data acquisition
Data acquisition addresses the issues including data col-
lection and data transmission, during which there are the
following challenges.
• Difficulty in data representation. MIoT data has different
types, heterogeneous structures and various dimensions.
For example, manufacturing data can be categorized into
structured data, semi-structured and un-structured data
[5]. How to represent these structured, semi-structured
and un-structured data becomes one of major challenges

in big data analytics for MIoT.
• Efficient data transmission. How to transmit the tremen-
dous volumes of data to data storage infrastructure in
an efficient way becomes a challenge due to the follow-
ing reasons: (i) high bandwidth consumption since the
transmission of big data becomes a major bottleneck of
wireless communication systems [8]; (ii) energy efficiency
is one of major constraints in many wireless industrial
systems, such as industrial wireless sensor networks [12].
2. Challenges in data preprocessing and storage
Data generated from MIoT leads to the following research
challenges in data preprocessing.
• Data integration. Data generated in MIoT has the vari-
ous types and heterogeneous features. It is necessary to
integrate the various types of data so that efficient data
analytics schemes can be implemented. However, it is
quite challenging to integrate different types of MIoT
data.
• Redundancy reduction. The raw data generated from
MIoT is characterized by the temporal and spatial re-
dundancy, which often results in the data inconsistency
consequently affecting the subsequent data analysis. How
to mitigate the data redundancy in MIoT data becomes a
challenge.
• Data cleaning and data compression. In addition to data
redundancy, MIoT data is often erroneous and noisy due
to the defected machinery or errors of sensors. However,
the large volume of the data makes the process of data
cleaning more challenging. Therefore, it is necessary to

design effective schemes to compress MIoT data and
clean the errors of MIoT data.
Data storage plays an important role in data analysis and
value extraction. However, designing an efficient and scalable
data storage system is challenging in MIoT. We summarize
the challenges in data storage as follows.
• Reliability and persistency of data storage. Data storage
systems must ensure the reliability and the persistency of
MIoT data. However, it is challenging to fulfill the above
requirements of big data analytics while balancing the
cost due to the tremendous amount of data [13].
4
Data Analytics
Supplier Manufacturer
Distributor
Retailer CustomerRaw materials Logistics
sensor meter QR code
bar code
Reader robot armsurveillance camera portable PCs Panel PC
WiFi AP
Macro BS
Communication network

Devices
Storage/Computing
Management
Management Software
File Systems
DBMS
Distributed Computing Models
Data Preprocessing
Data Integration
Data cleaning
Compression
Small BS
Manufacturing chain
Storage Infrastructure
Fast storage
Short term storage
Long term archival
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tio
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Descriptive
analytics
Diagnostic
analytics
Predictive
analytics
Prescriptive
analytics
Small BS
IoT gateway IoT gateway
Fig. 1. Life cycle of Big Data Analytics for MIoT
• Scalability. Besides the storage reliability, another chal-
lenging issue lies in the scalability of storage systems for
big data analytics. The various data types, the heteroge-
neous structures and the large volume of massive data
sets of MIoT lead to the in-feasibility of conventional
databases in big data analytics. As a result, new storage
paradigms need to be proposed to support large scale data
storage systems for big data analytics.

• Efficiency. Another concern with data storage systems is
the efficiency. In order to support the vast number of
concurrent accesses or queries initiated during the data
analytics phase, data storage needs to fulfill the efficiency,
the reliability and the scalability requirements together,
which is extremely challenging.
3. Challenges in data analytics
It is quite challenging in big data analytics for MIoT due
to the tremendous volume, the heterogeneous structures and
the high dimension. The major challenges in this phase are
summarized as follows.
• Data temporal and spatial correlation. Different from
conventional data warehouses, MIoT data is usually spa-
tially and temporally correlated. How to manage the data
and extract valuable information from the temporally/
spatially-correlated MIoT data becomes a new challenge.
• Efficient data mining schemes. The tremendous volume of
MIoT data leads to the challenge in designing efficient
data mining schemes due to the following reasons: (i)
it is not feasible to apply conventional multi-pass data
mining schemes due to the huge volume of data, (ii) it is
critical to mitigate the data errors and uncertainty due to
the erroneous features of MIoT data.
• Privacy and security. It is quite challenging to pertain the
privacy and ensure the security of data during the analyt-
ics process. Though there are a number of conventional
privacy-preserving data analytical schemes, they may not
be applicable to the MIoT data with the huge volume, het-
erogeneous structures, and spatio-temporal correlations.

Therefore, new privacy-preserving data mining schemes
need to be proposed to address the above issues.
III. ENABLING TECHNOLOGIES
In this section, we discuss the enabling technologies of big
data analytics in MIoT. According to the three phases in the
life cycle of big data analytics in MIoT, we roughly categorize
these technologies into data acquisition, data preprocessing
and storage, data analytics. In particular, we first discuss the
data acquisition related technologies in Section III-A. We then
describe the data preprocessing and storage in Section III-B.
In Section III-C, we discuss the data analytics in MIoT.
A. Data acquisition
As shown in Figure 1, the whole manufacturing chain
involves with multiple parties such as suppliers, manufacturers,
distributors, logistics, retailers and customers. As a result,
different types of data sources generate from each of these
sectors. Take a manufacturing factory an example. Sensors
deployed at the production line can collect device data, product
data, ambient data (like temperature, humidity, air pressure),
electricity consumption, etc. In the product warehouse, RFID
or other tags can help to identify and track products. RFID
5
Coverage
B
an
dw

id
th
Bluetooth IEEE 802.15.6
(WBAN)
IEEE 802.15.4
(Wireless HART, ZigBee, 6LoPAN)
LPWAN
3G
4G
5G
IEEE 802.11
(a, b, g, n, ac, ad)
2G
1Mbps
10Mbps
100Mbps
10m 100m 10km1km
Fig. 2. Wireless Communication Technologies for MIoT (figure
is not
scalable)
tags attached at products can be read in a short distance by a
RFID reader in a wireless manner.

The collected data can then be transmitted to the next stage
via either wired or wireless manner. Industrial Ethernet is
one of the most typical wired connections in manufacturing.
When Ethernet is applied to an industrial setting, more rugged
connectors and more durable cables are often required to sat-
isfy harsh environment requirements (like vibration, noise and
temperature). Compared with wired communications, wireless
communications do not require communication wiring and
related infrastructure consequently saving the cost and improv-
ing scalability. The major obstacle of the wide deployment of
wireless communications in industrial systems is the lower
throughput and the higher delay than wired communications.
However, the recent advances in wireless communications
make wireless connections feasible in industrial components.
Various sensors, RFIDs and other tags can connect with
IoT gateways, WiFi Access Points (APs), small base station
(BS) and macro BS to form an industrial wireless sensor
networks (IWSN) [14]. It is worth mentioning that different
wireless technologies have different coverage and bandwidth
capabilities. Figure 2 gives the comparison of various wireless
technologies regarding to coverage and bandwidth. In particu-
lar, it is shown in Figure 2 that conventional wireless technolo-
gies like Near Field Communications (NFC), RFID, Bluetooth
Low Energy (LE), wireless body sensor networks (WBAN),
Internet Protocol (IPv6), Low-power Wireless Personal Area
Networks (6LoWPAN) and Wireless Highway Addressable
Remote Transducer (WirelessHART) [15] are suffering from
short communication range (i.e., most of them can typically
cover less than hundreds of meters). As a result, they cannot
support the wide-coverage industrial applications, like smart
metering, smart cities and smart grids [16]. It is true that
other wireless technologies such as WiFi (IEEE 802.11) and
mobile communication technologies (such as 2G, 3G, 4G
networks) can provide longer coverage range while they often

require high energy consumption at handsets, whereas most
of sensor nodes have the limited energy (i.e., supplied by
batteries). Therefore, WiFi and other mobile communication
technologies may not be feasible in IWSN due to the high
energy consumption.
Recently, Low Power Wide Area Networks (LPWAN) es-
sentially provide a solution to the wide coverage demand
while saving energy. Typically LPWAN technologies include
Sigfox, LoRa, Narrowband IoT (NB-IoT) [17]. LPWAN has
lower power consumption than WiFi and mobile communica-
tion technologies. Take NB-IoT as an example. It is shown
in [16] that an NB-IoT node can have a ten-year battery
life. Moreover, LPWAN has a longer communication range
than RFID, bluetooth and 6LoWPAN. In particular, LPWAN
technologies have the communication range from 1km to
10 km. Furthermore, they can also support a large number
of concurrent connections (e.g., NB-IoT can support 52,547
connections as shown in [16]). However, one of limitations of
LPWAN technologies is the low data rate (e.g., NB-IoT can
only support a data rate upto 250 kps). Therefore, LPWAN
technologies should complement with conventional RFID,
6LoWPAN and other wireless technologies so that they can
support the various data acquisition requirements.
B. Data preprocessing and storage
1) Data preprocessing: Data acquired from MIoT has the
following characteristics:
• Heterogeneous data types. The whole manufacturing
chain generates various data types including sensory data,
RFID readings, product records, text, logs, audio, video,
etc. The data is in the forms of structured, semi-structured
and non-structured.

• Erroneous and noisy data. The data obtained from in-
dustrial environment is often erroneous and noisy mainly
due to the following reasons: (a) interference during
the process of data collection especially in industrial
environment, (b) the failure and malfunction of sensors
or machinery, (c) intermittent loss or outage of wireless
or wired communications [18]. For example, wireless
communications are often susceptible to harsh indus-
trial environmental factors like blockage, shadowing and
fading effects. Moreover, data transmission may fail in
industrial WSNs due to the depletion of batteries of
sensors or machinery.
• Data redundancy. Data generated in MIoT often contain
excessively redundant information. For instance, it is
shown in [19] that there are excessive duplicated RFID
readings when multiple RFID tags were scanned by
several RFID readers at different time slots. The data
redundancy often results in data inconsistency.
Data preprocessing approaches on MIoT data include data
cleaning, data integration and data compression as shown
in Figure 3. In industrial environment, sensory data is usu-
ally uncertain and erroneous due to the depletion of battery
power of sensors, imprecise measurement of sensors and com-
munication failures. There are several approaches proposed
to address these issues. For example, [20] proposed RFID-
Cuboids approach to remove redundant readings and eliminate
the missing values. Moreover, an Indoor RFID Multi-variate
Hidden Markov Model (IR-MHMM) was proposed to deter-
mine uncertain data and remove duplicated RFID readings as
shown in [21]. Furthermore, a machine-learning based method
was proposed to filter out the invalid RFID readings [22]. In
addition, the study of [23] proposed an auto-correlation based

6
Well-formed data
Raw data
Data cleaning
Data integration
Data compression
reduce the noise
interpolate missing value
mitigate inconsistency
normalize data
aggregate data
construct data attribute
normalize data
aggregate data
construct data attribute
reduce no. of variables
balance skewed data

compress data
Fig. 3. Data preprocessing techniques
scheme to remove duplicated time-series temperature data. In
[24], a novel data cleaning mechanism was proposed to clean
erroneous data in environmental sensing applications. Besides
duplicated readings, there also exist missing values in MIoT
data. In [25], an interpolation method was proposed to recover
the missing values of smart grids data. Moreover, energy-
saving is a critical issue in data-cleaning algorithms used in
MIoT. In [26], an energy-efficient data-cleaning scheme was
proposed.
2) Data storage: Data storage plays an important role in
big data analytics for MIoT. We summarize the solutions of
data storage in two aspects: 1) storage infrastructure and 2)
data management software.
Storage infrastructure consists of a number of intercon-
nected storage devices. Storage devices typically include:
magnetic Harddisk Drive, Solid-State Drives, magnetic taps,
USB flash drives, Secure Digital (SD) cards, micro SD cards,
Read-Only-Memory (ROM), CD-ROMs, DVD-ROMs, etc.
These storage devices can be connected together (via wired
or wireless connections) to form the storage infrastructure for
MIoT in industrial environment.
Besides storage infrastructure, data management software
plays an important role in constructing the scalable, effective,
reliable storage system to support big data analytics in MIoT.
As shown in Figure 1, the data management software consists
of three layered components:
• Distributed file systems. Google File System (GFS) was
proposed and developed by Google [27] to support

the large data intensive distributed applications such as
search engine. Moreover, Hadoop Distributed File System
(HDFS) was proposed by Apache [28] as an alternative to
GFS. In addition, there are other distributed file systems,
such as C# Open Source Managed Operating System
(Cosmos) proposed by Microsoft [29], XtreemFS [30]
and Haystack proposed by Facebook [31]. Most of them
can partially or fully support the storage of large scale
data sets. Therefore, most of them can offer the support
for large scale data storage of MIoT data.
• Database management systems (DBMS). DBMS offers
a solution to organize the data in an efficient and ef-
fective manner. DBMS software tools can be roughly
categorized into two types: traditional relational DBMS
(aka SQL databases) and non-relational DBMS (aka Non-
SQL databases). SQL databases have been a primary data
management approach, especially useful to Material Re-
quirements Planning (MRP), Supply Chain Management
(SCM), Enterprise Resource Planning (ERP) in the whole
manufacturing chain. Typical SQL databases including
commercial databases, such as Oracle, Microsoft SQL
server and IBM DB2, and open-source alternatives, such
as MySQL, PostgreSQL and SQLite. SQL databases
usually store data in tables of records (or rows). This
storage method neverthless leads to the poor scalabil-
ity of databases. For example, when data grows, it is
necessary to distribute the load among multiple servers.
One of benefits of SQL databases is that most of SQL
databases can guarantee ACID (Atomicity, Consistency,
Isolation, Durability) properties of database transactions,
which is crucial to many commercial applications (e.g.,
ERP and inventory management). Different from SQL
databases, NoSQL databases support various types of
data, such as records, text, and binary objects. Compared

with traditional relational databases, most of NoSQL
databases are usually highly scalable and can support the
tremendous amount of data. Therefore, NoSQL databases
are promising in managing sensory data, device data,
RFID trajectory data in MIoT [4].
• Distributed computing models. There are a number of
distributed computing models proposed for big data an-
alytics. For example, Google MapReduce [32] is one of
the typical programming models used for processing large
data sets. Hadoop MapReduce [33] is the open source
implementation of Google MapReduce. MapReduce …
1
PHY 4822L (Advanced Laboratory):
Analysis of a bubble chamber picture
Introduction
In this experiment you will study a reaction between
“elementary particles” by analyzing their
tracks in a bubble chamber. Such particles are everywhere
around us [1,2]. Apart from the standard
matter particles proton, neutron and electron, hundreds of other
particles have been found [3,4],
produced in cosmic ray interactions in the atmosphere or by
accelerators. Hundreds of charged
particles traverse our bodies per second, and some will damage
our DNA, one of the reasons for the
necessity of a sophisticated DNA repair mechanism in the cell.

2
Figure 1: Photograph of the interaction between a high-energy
π--meson from the Berkeley
Bevatron accelerator and a proton in a liquid hydrogen bubble
chamber, which produces two neutral
short-lived particles Λ0 and K0 which decay into charged
particles a bit further.
Figure 2: illustration of the interaction, and identification of
bubble trails and variables to be
measured in the photograph in Figures 3 and 4.
The data for this experiment is in the form of a bubble chamber
photograph which shows bubble
tracks made by elementary particles as they traverse liquid
hydrogen. In the experiment under
study, a beam of low-energy negative pions (π- beam) hits a
hydrogen target in a bubble chamber.
A bubble chamber [5] is essentially a container with a liquid
kept just below its boiling point (T=20
K for hydrogen). A piston allows expanding the inside volume,
thus lowering the pressure inside
the bubblechamber. When the beam particles enter the
detector a piston slightly decompresses the
liquid so it becomes "super-critical'' and starts boiling, and

bubbles form, first at the ionization
trails left by the charged particles traversing the liquid.
The reaction shown in Figure 1 shows the production of a pair
of neutral particles (that do not leave
a ionized trail in their wake), which after a short while decay
into pairs of charged particles:
π - + p → Λo + Ko,
3
where the neutral particles Λo and Ko decay as follows:
Λo → p + π-, Ko → π+ + π-.
In this experiment, we assume the masses of the proton (mp =
938.3 MeV/c2) and the pions (mπ+ =
mπ- = 139.4 MeV/c2) to be known precisely, and we will
determine the masses of the Λ0 and the K0,
also in these mass energy units.
Momentum measurement
In order to “reconstruct” the interaction completely, one uses
the conservation laws of (relativistic)
momentum and energy, plus the knowledge of the initial pion
beam parameters (mass and
momentum). In order to measure momenta of the produced
charged particles, the bubble chamber is
located inside a magnet that bends the charged particles in
helical paths. The 1.5 T magnetic field is

directed up out of the photograph. The momentum p of each
particle is directly proportional to the
radius of curvature R, which in turn can be calculated from a
measurement of the “chord length” L
and sagitta s as:
r = [L2/(8s)] + [s/2] ,
Note that the above is strictly true only if all momenta are
perfectly in the plane of the photograph;
in actual experiments stereo photographs of the interaction are
taken so that a reconstruction in all
three dimensions can be done. The interaction in this
photograph was specially selected for its
planarity.
In the reproduced photograph the actual radius of curvature R of
the track in the bubble chamber is
multiplied by the magnification factor g, r = gR. For the
reproduction in Figure 3, g = height of
photograph (in mm) divided by 173 mm.
The momentum p of the particles is proportional to their radius
of curvature R in the chamber. To
derive this relationship for relativistic particles we begin with
Newton's law in the form:
F = dp/dt = e v×B (Lorentz force).
Here the momentum (p) is the relativistic momentum m v γ,
where the relativistic γ-factor is defined
in the usual way
γ = [√(1- v 2/c2)]-1.

Thus, because the speed v is constant:
F = dp/dt = d(mvγ)/dt = mγ dv/dt = mγ (v2/R)(-r) = e v B (-r) ,
where r is the unit vector in the radial direction. Division by v
on both sides of the last equality
finally yields:
mγv /R = p /R = e B ,
identical to the non-relativistic result! In “particle physics
units” we find:
p c (in eV) = c R B , (1)
4
thus p (in MeV/c) = 2.998•108 R B •10-6 = 300 R (in m) B
(in T)
Measurement of angles
Draw straight lines from the point of primary interaction to the
points where the Λ0 and the K0
decay. Extend the lines beyond the decay vertices. Draw
tangents to the four decay product tracks at
the two vertices. (Take care drawing these tangents, as doing it
carelessly is a source of large
errors.) Use a protractor to measure the angles of the decay
product tracks relative to the parent

directions (use Fig. 3 or 4 for measurements and Fig. 2 for
definitions).
Note: You can achieve much better precision if you use graphics
software to make the
measurements, rather than ruler and protractor on paper.
Examples of suitable programs are
GIMP or GoogleSketchUp, both of which can be obtained for
free.
Analysis
The laws of relativistic kinematics relevant to this calculation
are written below. We use the
subscripts zero, plus, and minus to refer to the charges of the
decaying particles and the decay
products.
p+sinθ+ = p-sinθ-
(2)
p0 = p+cosθ+ + p-cosθ-
(3)
E0 = E+ + E-,
where E+ = √(p+2c2 + m+2c4) , and E- = √(p-2c2 + m-
2c4)
m0c2 = √(E02 - p02c2)
Note that there is a redundancy here. That is, if p+, p-, θ+, and
θ- are all known, equation (2) is not
needed to find m0. In our two-dimensional case we have two
equations (2 and 3), and only one
unknown quantity m0, and the system is over-determined. This

is fortunate, because sometimes (as
here) one of the four measured quantities will have a large
experimental error. When this is the case,
it is usually advantageous to use only three of the variables and
to use equation (2) to calculate the
fourth. Alternatively, one may use the over-determination to
"fit'' m0, which allows to determine it
more precisely.
A. K0 decay
1. Measure three of the quantities r+, r-, θ+, and θ-. Omit the
one which you believe would
introduce the largest experimental error if used to determine
mK. Estimate the uncertainty of
your measurements.
2. Use the magnification factor g to calculate the actual radii R
and equation (1) to calculate the
momenta (in MeV/c) of one or both pions.
3. Use the equations above to determine the rest mass (in
MeV/c2) of the Ko.
4. Estimate the error in your result from the errors in the
measured quantities.
5.
5
Β. Λo decay:
1. The proton track is too straight to be well measured in

curvature. Note that θ+ is small and
difficult to measure, and the value of mΛ is quite sensitive to
this measurement. Measure θ+, r-
and θ-. Estimate the uncertainty on your measurements.
2. Calculate mΛ and its error the same way as for the Ko.
3. Estimate the error in your result from the errors in the
measured quantities.
4. Finally, compare your values with the accepted mass values
(the world average) [3], and
discuss.
C. Lifetimes:
Measure the distance traveled by both neutral particles and
calculate their speed from their
momenta, and hence determine the lifetimes, both in the
laboratory, and in their own rest-frames.
Compare the latter with the accepted values [3]. Estimate the
probability of finding a lifetime value
equal or larger than the one you found.
References:
[1] G.D. Coughlan and J.E. Dodd: “The ideas of particle
physics”, Cambridge Univ. Press,
Cambridge 1991
[2] “The Particle Adventure”, http://particleadventure.org/
[3] Review of Particle Physics, by the Particle Data Group,
Physics Letters B 592, 1-1109
(2004) (latest edition available on WWW: http://pdg.lbl.gov )

[4] Kenneth Krane: Modern Physics, 2nd ed.; John Wiley &
Sons, New York 1996
[5] see, e.g. K. Kleinknecht: “Detectors for Particle Radiation”,
Cambridge University Press,
Cambridge 1986;
R. Fernow: “Introduction to Experimental Particle Physics”,
Cambridge University Press,
Cambridge 1986;
W. Leo: Techniques for Nuclear and Particle Physics
Experiments :
A How-To Approach; Springer Verlag, New York
1994 (2nd ed.)
Note: Experiment adapted from PHY 251 lab at SUNY at Stony
Brook (Michael Rijssenbeek)
6
Figure 3 : Photograph of the interaction between a high-energy
π--meson from the Berkeley
Bevatron accelerator and a proton in a liquid hydrogen bubble

chamber. The interaction produces
two neutral particles Λ0 and K0, which are short-lived and
decay into charged particles a bit further.
The photo covers an area (H•W) of 173 mm • 138 mm of the
bubble chamber. In this enlargement,
the magnification factor g = (height (in mm) of the photograph
)/173 mm.
7
Fig. 4: Negative of picture shown in Fig. 3
Applied Medical Informatics
Review Vol. 41, No. 2 /2019, pp: 53-64
53
[
A Systematic Review of Big Data Potential to Make Synergies
between Sciences for Achieving Sustainable Health: Challenges
and

Solution
s
Shirin AYANI1, Khadijeh MOULAEI2,*, Sarah DARWISH
KHANEHSARI1, Maryam
JAHANBAKHSH3, Faezeh SADEGHI4
1 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: [email protected]
* 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

Shirin AYANI, Khadijeh MOULAEI, Sarah DARWISH
KHANEHSARI, Maryam JAHANBAKHSH, Faezeh
SADEGHI
54 Appl Med Inform 41(2) June/2019
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).
A Systematic Review of Big Data Potential to Make Synergies
between Sciences for Achieving Sustainable
Health: Challenges and

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