Big Data Challenges in the IoT and Clouds

This report discusses the challenges faced by big data in the cloud and IoT. It defines the problems that might arise while handling big data in the cloud and the IoT. The report provides a brief discussion about the various problems faced by big data in the IoT and the clouds.

Big data Challenges in the IoT and Clouds
[Name of the Author]

ABSTRACT – The report mainly discusses about the various challenges that is faced by big data challenges in the cloud and
IoT. IoT or internet of things can be stated as the system, which consists of interrelated computing devices, mechanical and
digital machines, objects and many more along with a unique identifier. The IoT devices have the capability of transferring data
over the network without any type of human-to-human or human-to-computer interactions. This report also defines the
problems that might arise while handling big data in the cloud and the IoT. The report provides a brief discussion about the
various problems faced by big data in the IoT and the clouds. The report also consists of the literature review about the various
past and present work with this technology.
1. INTRODUCTION (15 MARKS)
The report discusses about the various problems that
might arise while using the big data in the cloud or the IoT.
IoT has evolved due to the convergence of the various
wireless technologies, micro electromechanical systems,
micro services and the internet. This convergence has helped
a lot in tearing down of the silo walls that exists between the
operational technology and the information technology [1].
This has initially allowed the analysis of the unstructured data
generated by the machine for the insights to drive the
improvement process. IoT devices are generally associated
with providing of large amount of data and if the data
produced, is analyzed in a proper way then those data can be
extremely valuable. The various challenges faced by the big
data in the IoT and the clouds have been described in the
following section of the report.
IoT or internet of things has many benefits but still there
exists some challenges regarding the big data. Big data and
IoT can be considered as the outputs of the demands of
business, which is responsible for driving the changes in an
application. Firstly the thing which comes in mind is that
when big data and IoT are used together the volume of data
increases which gives rise to the challenges regarding the
storage of the data. In addition, this can be tackled by making
use of additional data centers, which will handle the extra
load. So taking into consideration the enormous Impact of IoT
on the data storages the total system can be moved into the
usage of platform-as-a-service, which is a cloud based
solution. Along with this, there exists security challenges like
breach of data, loss of data many more. As Iot is an emerging
technology so it can be stated that it is still a new form of
technology for the security professionals and they lacks the
experience for handling of the security threats. Because of this
reason the risk increases and along with this the risk is
regarding not only the data but also it can also damage the
devices, which are connected to the network. There is a need
of making crucial changes in the security landscape of the IoT
technology.
2. MAJOR CHALLENGES OF BIG DATA IN THE IOT AND
CLOUDS
There exists various types of challenges, which are faced
by the big data in the IoT and clouds. Some of the major
challenges are listed below:
2.1 Data challenges
2.1.1 Massive amount of data
The IoT system consists of several devices and
they are associated with providing of huge
amount of information. The huge amounts of data
produced by the IoT devices acts as one of the
major reason behind the problem of transmission,
storage and processing of the data. The volume of
data is increasing day by day as many objects
present in today’s world is tracked and recorded.
It has been seen that the large data or the big data
is being stored but the processing of such huge
datasets is very much difficult [26]. This is
ultimately resulting in the decreased amount of
processed data, which is responsible for the
forming of blind zones.
2.1.2 Various Forms of Data Collected
Data is collected from various sensors, this
sometimes combines with the other unstructured
data and for this reason, and there exists a strong
relationship between the sensors and the other
unstructured data. The big data is responsible for
storing of various forms of data collected, which
might include the structured data, Semi-structured
data and the unstructured data. Only 20% of the
data is processed amongst the total amount of
data collected [27]. The rest 80% of the data
cannot be analyzed and processed by making use
of the traditional methods. This ultimately helps
in concluding to the fact that most of the data
collected cannot be used for the purpose of
making decisions.
2.1.3 Data Transmission Speed
The speed at which the data gets transmitted in
the internet can also be termed as velocity. The
exploring and acquisition of insights about the
data is only possible if analysis of the data is done
at high speed and on a real-time basis [28]. The
software’s which are existing in today’s world is
capable of generating data streams at very high
speed which initially results in the formation of
various types of difficulties during analysis on a
real-time basis.
2.1.4 Time Series for Data Analysis
The sensors are associated with capturing
events in a specific point of time but it has been
seen that this capturing is sometimes useless. But
in case of serious situations it is necessary to
record and address. This analysis is very much
difficult for most of the companies.

2.1.5 Security and Privacy
Transferring of the data is done by making use
of the wireless medium in the IoT. Therefore, it is
very much essential to ensure the security and the
privacy of the information. There could exists a
number of problems, which might include the
physical attack or the wireless information attack
[29]. This can have a very devastating effect over
the security and the authenticity of the
information, which is being transmitted. It is
possible for the attackers to attack the IoT devices
physical and steal the information while the data
is being transmitted. Along with this, there exists
various IoT devices, which do not accept the
security packages, and this is the main reason
behind low self-defense.
2.2 Process Challenges
2.2.1 Selective Data Acquisition
The data are generally acquired by making use
of the sensors but not all the data that is collected
is important. Due to this reason, there is a need of
filtering and compressing this data. These filters
are associated with determining the data that are
to be accepted and discarded [30]. The task of
designing a smart filter is very much complicated
and challenging.
2.2.2 Data Extraction
The data that is collected is of different forms.
Therefore the utilization of this data can be done
in an effective manner if they are converted into a
single structured format [31]. So there exists the
need of designing a new process for the
extraction of the required information and this
designing of the new processes is very much
challenging.
2.2.3 Data Heterogeneity
Data is generally collected from different types
of sources so they are heterogeneous in nature.
Therefore it can be stated that processing of the
data is not a straight forward process. This
happens due to the reason that finding,
identifying and understanding the information is
very difficult for the type of data which cannot be
integrated seamlessly [32]. It becomes very much
difficult to analyse the heterogeneous data
because they are having different structures and
different schematics as well. The integration of
the heterogeneous data in order to process then on
a real time basis acts as a major challenge.
2.3 Analytic challenges
2.3.1 Analytics Challenges over Unstructured Data
The analysis of the unstructured data is very
difficult. Transformation of the large volumes of
data into meaningful abstracts, which is
associated with supporting cue-based decision-
making, is very much challenging. Another major
challenge faced by video analytics includes the
enormity of the video data [33]. The major
challenge faced by social analytics is its data-
centric nature and it’s indisciplinary research
2.3.2 Visualization Challenges
The challenges of visualization includes its
applicability for the large volumes of data, along
with this the possibility of visualization of the
data that is being presented in different types of
data format, speed and the effectiveness of the
data presentation.
2.4 Schematic data challenges
2.4.1 Data Semantics Challenges
Data schematics acts as major element of the
data analysis process and it is very much
challenging to deal with the different structure of
the data and the different types of information
and analysis of the data as the structure of the
information is very much complex [34]. The
integration of various heterogeneous collections
of data has become a colossal issue. This has
mainly happened because data sources are very
sparse and incomplete. For this reason, it is an
onerous task to find a logical connection between
the data.
2.4.2 Data Scalability Challenges
The data engineers are associated with facing
challenge while creating a domain knowledge
models and schematic annotation frameworks
which can be used so as to describe the huge
number of devices in the IoT. It is essential that
the domain knowledge is associated with the
sematic description of the data and this happens
due to the fact that IoT devices are capable of
referring with the separate phenomenon.one of
the major challenge is regarding the description
of the granularity which means that the terms and
concepts are very specific and the domain
knowledge is extensive [35]. However, the
challenges in handling semantic data are the scale
of data developed by IoT resources, the changing
status of resources and data, and the volatility of
the IoT environment.
2.4.3 Data Fusion Challenges
Data fusion is generally associated with
improving the quality of the data. The schematic
data fusion is very much challenging due to the
fact that data is generally collected from different
sources and along with this different types of
algorithm is used for the purpose of improving
the quality of the service as well as the accuracy
[36].
2.4.4 Data Discovery Challenges
Handling of the data and the storage in an
efficient way is becoming more and more
difficult and along with time, the volume of the
data and the schematic description is increasing
[37]. The major challenge included in this are the

designing and developing of repositories,
publishing the semantic data, accessing the
semantic data in distributed environments, and
developing effective indexing and discovery
mechanisms.
2.4.5 Quality and trustworthiness challenges:
The sensor devices, which are associated with
generating IoT data, has errors as well as quality
issues. The data collected by making use of these
sensors varies according to time. It is not possible
avoid the inaccuracy in the data of IoT. Another
issue is the trust [38]. There exists several issues
which needs to be addressed and which mainly
includes the development of a trust model,
feedback, and the development of a verification
mechanism.
3. BACKGROUND/LITERATURE REVIEW
Big data is generally produced by a diverse and large
number of data sources which might include the IoT and the
cloud. This data is not only huge in size but is also very fast
and complex in order to be processed and stored by making
use of the traditional methods. This growth of the data has
driven the industries and has also attracted numerous
researchers in order to develop various models and scalable
tools for the purpose of handling the big data. This section of
the report is mainly associated with reviewing the various
studies that has been investing the big data tools [7].
A numerous studies has been conducted in order to review
the existing popular big data stack tools along with comparing
them [10] [6]. This is done in order to present the various
advantages as well as the disadvantages of the tools in the
stacks. The Apache Hadoop stands out to be a well-known
source framework for the analytics of the big data [11]. This
are generally designed for working in a seamless way along
with the stack open-source tools in order to enable the storage
as well as the processing of the significant amount of the data
by making use of the clusters of commodity hardware. There
is the existence of distributed file systems, cluster
management, storage, distributed processing and
programming, data analysis, data governance and data pre-
processing tools in the Hadoop stack. Another big data satck
tool was proposed by the AMPLab at the University of
California, Berkeley and was known as the BDAS [12]. This
is associated with integrating the open-source big data tool in
order to make sense of the big data. There is also a presence of
distributed file system, cluster management, distributed data
processing and programming, data analysis, data tools and in-
house developed big applications. Another similar study has
been associated with reviewing the current state-of-the-art in
open-source big-data analytics tools for the purpose of
machine learning and is also associated with providing certain
recommendations in order to evaluate the tools [2].
The management of the IoT big data and discovery of the
knowledge acts as one of the key research challenge in various
automation applications. The management of the big data
mainly includes several managerial activities and this might
include the collection of the data, integration, cleaning,
storage, processing and many more and this are actually
implemented by making use of various systems, models, and
frameworks [22] [23] [24] [25]. Stankovic (2014) provided a
brief research direction regarding the management of data and
discovery of the knowledge prospective in the platform of IoT
big data management and in this there exists three activities
which includes the association of the data, inference and the
discovery of knowledge [25]. The work done by Wu et al.
(2014) a special emphasis was put upon the cognitive IoT
framework for the purpose of making decisions in an effective
way along with discovering the knowledge’s [19]. The work
done by Jin et al. (2014) mainly emphasizes on the cognitive
IoT framework for the purpose of making certain decisions
and discovering the knowledge’s and this was done in order to
enhance the decision making process [18]. The designing of
the information management framework is done in order
collect the data and extraction of information on real time
basis in order to meet the various needs of the applications.
Some other like Chonggang et al. (2013) also suggested some
ontology based modelling mechanism in order to enhance the
discovering of the knowledge from the IoT big data.
Various IoT big data researches has been associated with
using the non-cognitive framework in order to perform
various data management activities however the management
of the data and discovery of the knowledge always needs a
cognitive framework for performing various self-regulated
operations in cases of emergencies without any type of human
interventions [20]. For applications in industrial automation
the management of cognitive data and discovery of knowledge
framework would have a significant role in self-regulates
monitoring information. For this reason a COIB framework
can be used for effective data management and knowledge
discovery within industrial automation environment [9].
Other studies by Fox et al. (2015) and Qiu et al. (2014)
proposed a high-performance computing stack for big data
analytics and has also summarized the various capabilities of
the stacks in 21 architecture layers which have been identified
[1] [4] [18]. They have been associated with reviewing more
than 300 software packages in order to define the tool stack.
Other studies by Grover & Kar, (2017) and Sivarajah et al.
(2017) has reviewed the literatures in a systematic way in
order to analyse the current state as well as the research
direction of the big data [5] [8]. Along with this the study
done by Grover and Kar (2017) has moved beyond the
systematic literature review which presents a limited number
of big data tools which are conventional in nature in order to
provide an overview after that [5]. A recent survey by
Qussous et al. (2017) provides the global view of state-of-art
big data technologies and long with comparing technologies
which are present in the different layers of the system [3]. The
main focus of the study includes the discussion about the
Hadoop framework and the tools and the tools which are
developed earlier and also the commercial Hadoop
distribution like the Cloudera, Hortonworks, MapR and many

more, a similar study by Inoubli et al. (2017) provided an
overview about the widely used big data technologies in order
to identify the various key features of this technology [13].
But the study did not provideany such information regarding
the ways by which the tool can be reviewed systematically, or
the nature of the criteria which includes the tool in the stack or
how the tools can be brought together in order to define the
big data architecture.
Some other studies done by Nadal et al. (2017), Pääkkönen
and Pakkala (2015), Greerdink (2013) and Klein et al. (2016)
proposed few architectures for the analytics of the big data
[14] [15] [16] [17]. Nadal et al. (2017) in their study presented
a reference architecture regarding the semantically aware big
data system and this was doen by taking into account the
various characteristics of the big data [14]. Pääkkönen and
Pakkala (2015) proposed a reference architecture for the big
data which is not dependent on the technology [15]. Along
with this the author also classified the commercial big data
technologies and the products which are generally based upon
the analysis of the use-cases which has been already been
published. Besides this there also exists some domain specific
solutions in order to present the reference architecture for the
big data analytics. The study done by Geerdink (2013) has
proposed a reference architecture in order to guide the
software architects which mainly includes the providing of
definition for the big data analytics solutions in order to
predict the analytics by making use of the qualitative data
analysis [17]. Besides this evaluation of the quality criteria is
done by making use of the questionnaire. The author has also
demonstrated the way in which the proposed reference
architecture would be defining the concrete architecture of the
use case. All this study is associated with focusing on the
various use-case-specific architectural solutions from the
perspective of the technology.
4. Methodology:
The COIB framework is best suited for future experiments.
In order to implement the COIB framework, it is to be made
syre that the IoT environment has high internet connectivity
amongst all the networked IoT objects which are generally
fabricated in different devices. This is to be doen in order to
regulate the functional as well as the operational efficiency.
The Functional aspects of the COIB framework has been
provided in the figure provided below:
The total IoT environment can be divided into various
segments in order to cope up with the different standard
configuration of the network management. The various IoT
segments are generally responsible for the IoT raw Data
streams and along with this it also acts as the source of raw-
data for the respective segments. The IoT big data sources are
totally unstructured and generally happens with respect to the
name, level, scale, abstraction and many more. This also
ensures the high rate of inconsistency, incompleteness,
redundancy and the other anomalies. For this reason the big
data aggregators are associated with the fusion of the data
over the large data streams. Some standard data schematics
are used in the process of data fusion operations. This is
generally doen in order to eliminate the various anomalies so
as to produce a clean data associated with a total quality
management. The classifiers of the big data are associated
with the splitting of the clean data into various clustures
depending on various aspects which might include the
behaviour, characteristics and the domains. This is to be done
in order to make sure that there exists easiness of accessing,
easiness of understanding and easiness of usage. For the
application of IoT the domain of data includes various
operational data, production data, status data and many more.
The Hbase storage is associated with taking the responsibility
of scaling and storing the clusters of data in the multiple
number of storage nodes. In the HBase system the tables can
be set which would be like the relational database. This would
be done in such a way that each of the table contains the row
and columns. Along with this there should exist an element
which can be defined as the primary key. In order to have a
proper surveillance and access, the master node is associated
with holding the control over the various nodes which are
responsible for the storage. The nodes responsible for storage
are associated with hording the actual data clusters and along
with the the master node is associated with storing of the
metadata which means all the access paths for reaching the

storage nodes. By this it can be understood that in the HBase
system the big data of IoT would be scaled in a much effective
and efficient way. The big-data analysis of the IoT is one of
the important aspect responsible for the regulation of the data
management and issues related to the discovery of knowledge
by making use of the cognitive and the computational
intelligent tools. For an IoT environment different types of
standard cognitive data analytic tools can be used for the
purpose of producing a cognitive decision, plan and actuations
in order to control the entire automation environment. From
this it can be concluded that for the purpose of achieving
success in the implementation of the COIB framework in the
IoT environment , the IoT big data generation, aggregation,
classification, storage and the analysis must be synchronised
on real-time basis.
The implementation architecture of the COID framework
has been provided below:
In the figure provided above consist of the data centre
which is reponsible for the execution of the aggregation of the
data, classification of the data and operations related to the
storage. For each server a dedicated and powerful server can
be used in order to carry out the various operations. The
knowledge production centre is associated with acccessing the
data which are precise and this is followed by the generation
of the explicit kowledge by making use of the cognitive CI
tool. The actuation centre is associated with receiving the
knowledges and identifying the important actions along with
the prioritization of the actions and this doen by making use of
the action queue. This is followed by sending it to the
regulatory centre in order to take immediate actions. The
regulatory centre is associated with generating the regulatory
instructions automatically based upon the actions and is aslo
associated with the transferring of the IoT based
microcontroller objects in order to certain actions against
specific operations. The IoT big data management is
associated with incorporation of the management of data and
process of discovering the knowledge in order to focus on the
major four subsystems which are to be managed under the
COIB framework. For the purpose of improving the
efficiency, the management of the data and discovery of the
knowledge the four major subsystem palys a very important
role.
The figure provided below shows the IoTbig data
management subsystem:
The IoT applications needs a complete architecture in order
to implement the COIB framework. For this reason the
discussion has been made on the overall IoT big data layering
architecture having an purpose of implementing it on a wide
basis. The figure provided below shows the overall
architecture:
The pyramidal architecture consist f the knowledge
processing layer which is specially introduced in order to have
an effective production of knowledge and utilization in the
application layer. This has been done in order to manage the
vrious applications of IoT. The production of the knowledge
in an effective way is mainly associated with targeting the
performance of the cognitive tool which might include the
fuzzy ontology, semantics of data, neural computing and
many more. In the application of the IoT, the IoT netwrok is
associated with the usage of the internet in order to integrate
the multiple IoT devices with the intelligent tools. This is t be
done in order t regulate the various automatic operations. The
Internet is associated with integrating the various diversified
technologies which might include the machine-to-machine
communication, machine learning, IoT devices, Big-data,
cloud computing and computational intelligence for various
IoT applications. The IoT middleware would be responsible
for processing of the data and management of the activities
alsong with the maintainance of the perturbation of the data
which exsists between the layers.
Organization of the data is aso an important factor. The
data produced by the IoT devices are in bulk amount and this
bulk of data is generally organized in the form of NoSQL
database. The IoT big-data can be considered as an spatio-

temporal database which is dependent on the time and the
location. IoT big data mainly consist of more number of rows
and less number of columns. Due to this reason the column
oriented data-repository can be used for the purpose of
improving the performance of the IoT big data related to the
accessibility of the data and processing of the queries. It is not
possible to store any kind of heteregenous IoT big data in any
type of relational database. The organization of data therefore
can be considered as a very important aspect in the
impelmentation of the architecture for handing the IoT big
data. Due to this reason the implementation of the IoT big data
system on a large scale application is needed. A framework
has been presented below which shows a IoT big data system.
The figure provided above shows the framework where a
N number of IoT devices have been compounded with the IoT
big data system and s regulated under the COIB framework.
In each device there also exists P number of IoT objects which
have been embedded in such a way that each of tehm is
responsbel for sensing of the data and sending a n-muber of
data related to various events to the COIB framework. In this
figure the Tn element is associated with showing the nth event
data recording time stamp.
5. Conclusion:
This report has been associated with discussing about the
COIB framework in order to manage the data in an effective
way and discovering the knowledge’s over the IoT big data.
This report also discusses about an architecture associated
with the total IoT big data layering architecture which would
help in promoting the implementation of the feasibility of the
COIB framework in an IoT environment in order to manage
the big data. The COIB framework is associated with
following the principles of the data centric architecture in
order to incorporate the big data streams which are large in
size and this is to be done by considering the data access paths
in an efficient way and this might be adequate in the
management of the queries related to the spatio-temporal
aspect which are large in size. Along with this the pyramidal
IoT big data management has also been discussed in order to
highlight the important subsystems which are associated with
rendering from the IoT object management to the real world
management of the applications. Lastly a data organization
framework has also been provided which is associated with
providing a data organization framework which can be
implemented in order big data stream management which are
provided by the IoT devices. Along with this the integration of
the total architecture and framework is associated with
providing a real time platform for the management of IoT big
data and discovery of the knowledge for large scale automaton
purposes. The report has been associated with discussing
about the various problems which are faced by the bif data
and along with this there might also occur future issues. The
future issues might include the management of the data based
upon the time schedule, mining along with the analysis of the
large data streams which are mainly generated by millions or
trillions of IoT devices which are employed in the
heterogeneous applications and lastly the security and the
perturbation issues which are associated with the management
of the sub-systems of the big data.
From the above discussed report it can be concluded that,
there are big data challenges in Internet of Things and Cloud.
The various types of big data challenges have been provided
in this report for the purpose of analyse the impact of big data
in the clouds and the Internet of Things. Different types of
challenges have been described in this report that are privacy
and security issues, data mining issues and data integration
issues and data storage issues. This report is also associated
with discussing the various technologies which are used with
the big data, cloud and Internet of Things. This technologies
acts as an important factor while overcoming the challenges
faced by the dig data in the IoT and clouds. The report is
associated with showing the fact about the various challenges
which needs to be looked upon with proper emphasis on each
technology and tools. The methodologies to solve these
challenges has been discussed in this report to propose a
solution for the big data challenges. The big data challenges in
Internet of Things and Cloud shows that the challenges related
to Internet of Things and Cloud that are essential to be taken
into account. The possible strategies in the research paper
shows that there are various technologies that have been
previously deployed and are on a verge of deploying
technologies. The methodologies are helpful to support big
data challenges in Internet of Things. The best methodologies
are combination of Hadoop along with Map Reduce, Hive and
Mahout is necessary to minimize big data challenges in
Internet of Things and Cloud. This combination is the possible
and best solution for the present and future scenario with
adding up of more advanced and new technologies. These
methodologies has several advantages which are beneficial for
big data challenges in Internet of Things and Cloud. These
methodologies provide various benefits such as reduction in
cost for future opportunities, scalability and flexibility of
methodology to improve big data challenges in Internet of
Things and Cloud. However, there are advantages and
disadvantages of these three technologies that is essential to
be taken into account before and after adopting these
technologies. The several drawback of big data challenges in
Internet of Things are compatibility issues and power failure
in events of disruption. Hence, it can be concluded that the
challenges can be minimized with proper use of these
methodologies. The future research shows that if these
technologies maintained properly then it can have major boost
in performance of every industry using combination of big
data, Internet of Things and Cloud. Therefore, big data

challenges in Internet of Things and Cloud are the factors that
needs central attention and valuable strategies to manage and
maintain the technologies.
References:
[1] Chen, C. H., Hsu, C. L., & Tsai, K. Y. (2017, July). Survey on Open
Source Frameworks for Big Data Analytics. In Third Int. Conf.
Electron. Softw. Sci (p. 74).
[2] Landset, S., Khoshgoftaar, T. M., Richter, A. N., & Hasanin, T. (2015).
A survey of open source tools for machine learning with big data in the
Hadoop ecosystem. Journal of Big Data, 2(1), 24.
[3] Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2017). Big
Data technologies: A survey. Journal of King Saud University-
Computer and Information Sciences.
[4] Fox, G. C., Qiu, J., Kamburugamuve, S., Jha, S., & Luckow, A. (2015,
May). Hpc-abds high performance computing enhanced apache big data
stack. In Cluster, Cloud and Grid Computing (CCGrid), 2015 15th
IEEE/ACM International Symposium on (pp. 1057-1066). IEEE.
[5] Grover, P., & Kar, A. K. (2017). Big data analytics: a review on
theoretical contributions and tools used in literature. Global Journal of
Flexible Systems Management, 18(3), 203-229.
[6] Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2017). Big
data analytics capabilities: a systematic literature review and research
agenda. Information Systems and e-Business Management, 1-32.
[7] Gökalp, M. O., Kayabay, K., Zaki, M., Koçyiğit, A., Eren, P. E., &
Neely, A. (2017). Big-Data Analytics Architecture for Businesses: a
comprehensive review on new open-source big-data tools.
[8] Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017).
Critical analysis of Big Data challenges and analytical methods. Journal
of Business Research, 70, 263-286.
[9] Singh, D., & Reddy, C. K. (2015). A survey on platforms for big data
analytics. Journal of Big Data, 2(1), 8.
[10] Apache Hadoop, (n.d.). http://hadoop.apache.org/ (accessed SApril 27,
2018).
[11] AMPLab – UC Berkeley, (n.d.).
https://amplab.cs.berkeley.edu/software/ (accessed April 27, 2018).
[12] Qiu, J., Jha, S., Luckow, A., & Fox, G. C. (2014). Towards HPC-ABDS:
an initial high-performance big data stack. Building Robust Big Data
Ecosystem ISO/IEC JTC, 1, 18-21.
[13] Inoubli, W., Aridhi, S., Mezni, H., Maddouri, M., & Nguifo, E. M.
(2016). An Experimental Survey on Big Data Frameworks. arXiv
preprint arXiv:1610.09962.
[14] Nadal, S., Herrero, V., Romero, O., Abelló, A., Franch, X.,
Vansummeren, S., & Valerio, D. (2017). A software reference
architecture for semantic-aware Big Data systems. Information and
software technology, 90, 75-92.
[15] Pääkkönen, P., & Pakkala, D. (2015). Reference architecture and
classification of technologies, products and services for big data
systems. Big Data Research, 2(4), 166-186.
[16] B. Geerdink, A Reference Architecture for Big Data Solutions, 8th Int.
Conf. Internet Technol. Secure. Trans. (2013) 71–76.
[17] Klein, J., Buglak, R., Blockow, D., Wuttke, T., & Cooper, B. (2016,
May). A reference architecture for big data systems in the national
security domain. In Big Data Software Engineering (BIGDSE), 2016
IEEE/ACM 2nd International Workshop on (pp. 51-57). IEEE.
[18] Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An
information framework for creating a smart city through internet of
things. IEEE Internet of Things Journal, 1(2), 112-121.
[19] Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K.
(2014). Cognitive internet of things: a new paradigm beyond
connection. IEEE Internet of Things Journal, 1(2), 129-143.
[20] Zhou, J., Hu, L., Wang, F., Lu, H., & Zhao, K. (2013). An efficient
multidimensional fusion algorithm for IoT data based on
partitioning. tsinghua science and technology, 18(4), 369-378.
[21] Mozumdar, M., Shahbazian, A., & Ton, N. Q. (2014, March). A big data
correlation orchestrator for Internet of Things. In Internet of Things
(WF-IoT), 2014 IEEE World Forum on (pp. 304-308). IEEE.
[22] Ding, Z., Gao, X., Xu, J., & Wu, H. (2013, August). IOT-StatisticDB: a
general statistical database cluster mechanism for big data analysis in
the internet of things. In Green Computing and Communications
(GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom),
IEEE International Conference on and IEEE Cyber, Physical and Social
Computing (pp. 535-543). IEEE.
[23] Zhou, H., Wang, Y., & Cao, K. (2013, December). Fuzzy DS theory
based fuzzy ontology context modeling and similarity based reasoning.
In Computational Intelligence and Security (CIS), 2013 9th
International Conference on (pp. 707-711). IEEE.
[24] Cai, Z., Bourgeois, A., & Tong, W. (2017). Guest editorial: Special
issue on Internet of Things. Tsinghua Science and Technology, 22(4),
343-344.
[25] Stankovic, J. A. (2014). Research directions for the internet of
things. IEEE Internet of Things Journal, 1(1), 3-9.
[26] Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2013). Sensing as a
service and big data. arXiv preprint arXiv:1301.0159.
[27] Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., &
Khan, S. U. (2015). The rise of “big data” on cloud computing: Review
and open research issues. Information Systems, 47, 98-115.
[28] Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and
challenges of big data research. Big Data Research, 2(2), 59-64.
[29] Xiaofeng, M., & Xiang, C. (2013). Big data management: concepts,
techniques and challenges [J]. Journal of computer research and
development, 1(98), 146-169.
[30] Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2014, August).
On the integration of cloud computing and internet of things. In Future
internet of things and cloud (FiCloud), 2014 international conference on
(pp. 23-30). IEEE.
[31] Rao, B. P., Saluia, P., Sharma, N., Mittal, A., & Sharma, S. V. (2012,
December). Cloud computing for Internet of Things & sensing based
applications. In Sensing Technology (ICST), 2012 Sixth International
Conference on (pp. 374-380). IEEE.
[32] Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M.,
Mateos, G., ... & Andreescu, S. (2015, June). Health monitoring and
management using Internet-of-Things (IoT) sensing with cloud-based
processing: Opportunities and challenges. In Services Computing
(SCC), 2015 IEEE International Conference on (pp. 285-292). IEEE.
[33] Biswas, A. R., & Giaffreda, R. (2014, March). IoT and cloud
convergence: Opportunities and challenges. In Internet of Things (WF-
IoT), 2014 IEEE World Forum on (pp. 375-376). IEEE.
[34] Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and
big data analytics for smart and connected communities. IEEE Access,
4, 766-773.
[35] Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014, January).
Cloud of Things: Integrating Internet of Things and cloud computing
and the issues involved. In Applied Sciences and Technology
(IBCAST), 2014 11th International Bhurban Conference on (pp. 414-
419). IEEE.
[36] Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration
of cloud computing and internet of things: a survey. Future Generation
Computer Systems, 56, 684-700.
[37] Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications,
investments, and challenges for enterprises. Business Horizons, 58(4),
431-440.
[38] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile
networks and applications, 19(2), 171-209.
[39] Bessis, N., & Dobre, C. (Eds.). (2014). Big data and internet of things: a
roadmap for smart environments (Vol. 546). Springer International
Publishing.