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The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.
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Emerging Science Journal
Vol. 2, No. 1, February, 2018
Page | 1
Big Data: Concept, Potentialities and Vulnerabilities
Fernando Almeida a
a University of Porto, INESC TEC, Portugal
The evolution of information systems and the growth in the use of the Internet and social networks has
caused an explosion in the amount of available data relevant to the activities of the companies.
Therefore, the treatment of these available data is vital to support operational, tactical and strategic
decisions. This paper aims to present the concept of big data and the main technologies that support
the analysis of large data volumes. The potential of big data is explored considering nine sectors of
activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and
media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy
challenges of big data are discussed. It was possible to conclude that despite the potential for using the
big data to grow in the previously identified areas, there are still some challenges that need to be
considered and mitigated, namely the privacy of information, the existence of qualified human
resources to work with Big Data and the promotion of a data-driven organizational culture.
Big Data;
Distributed Computing;
Data Analysis;
Predictive Analytics;
Article History:
1- Introduction
Information is now increasingly important and a successful differential, as the whirlwind of external events forces
organizations to face new situations. The information becomes fundamental for the discovery and introduction of new
technologies, as well as for exploring opportunities investment. It has the power to detect new opportunities, sign of the
threats and reduces uncertainties during the decision-making process and, consequently, increases its quality. In this
sense, the differential of companies and professionals is directly related to the value they give to information, knowledge
and how they use it in meeting the demands of the market and in the search for innovative solutions.
The process of decision-making is complex and rational, contemplating factors such as intuition, experiences and
knowledge. Business managers are constantly going through situations where they are faced with a number of different
paths, and must choose the one that leads the organization to achieve its results. Therefore, information plays a
fundamental role in the decision-making process, in order to identify the various alternatives and their consequences.
However, capturing relevant information for the company is a complex and difficult task. Useful data can come from
anywhere and there is an increasing number of heterogeneous numbers of devices that capture data from different
sources. The compilation and sharing of detailed information is only possible through the use of information and
communication technologies (ICT), and this data can come from suppliers, consumers, partners and competitors. To this
large volume of data coming from multiple heterogeneous sources we call Big Data, which is the next frontier for
business innovation and productivity. For that reason, companies should be aware of the potentialities and vulnerabilities
of Big Data and create strategies to handle large volumes of data in order to take advantage of its many potentialities.
2- Concept of Big Data
Although the term "big data" is relatively new, the act of collecting and storing large amounts of information for
eventual data analysis is quite old. Companies in diverse sectors of activity, mainly those of bigger dimension and with
greater volume of data, have developed solutions of business intelligence (BI) to support business management
processes. BI is characterized by the use of a set of methodologies, processes, structures and technologies that transform
a large amount of raw data into useful information for making strategic decisions [1]. Table 1 performs a comparative
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analysis between the concept of BI and big data analytics.
Table 1. Traditional Analytics (BI) vs. Big Data Analytics.
Traditional Analytics (BI)
Big data Analytics
Data sets
Descriptive analytics and diagnosis analytics
Limited data sets with structured data.
Adoption of simple data models
Looks to what happened, and why?
Predictive analysis
Large scale data sets with more types of data. Adoption
of complex data models
Provide new insights and forecasts
Big data was initially defined based on the following three Vs [2]:
Volume: organizations collect data from a wide variety of sources, including business transactions, social
networks, and information from sensors or data transmitted from machine to machine. In the past, storing such
a large amount of information would have been a problem, but the emergence of new technologies, such as
Hadoop , have alleviated the burden;
Velocity: data flow at a huge rate and must be handled in a timely manner. RFID tags, sensors, cell phones and
smart meters are driven by the need to deal with huge amounts of data in real time;
Variety: data is generated in all types of formats, from structured data, numerical data in relational databases, to
unstructured text documents, to e-mail, video, audio, stock quotes, and financial transactions.
However, over the time, six new dimensions have been added to the big data concept [3-7]:
Variability: in addition to the increasing speed and variety of data, data flows can be highly inconsistent with
periodic peaks. On a daily basis, seasonal peaks or peak events generated can be very challenging to manage,
particularly when we deal with unstructured data;
Value: it should also be recognized that large data must have value. In this sense, it is important to ensure the
return on investment and guarantee that the insights generated are based on accurate data that lead to measurable
Veracity: it is important to ensure that there is a high data quality and document the data provenance in terms of
inputs, entities, systems, and processes that influence data of interest;
Validity: valid data is essential for making right and accurate decisions. Therefore, we should avoid to used
corrupted data in analytical exploration of data;
Visualization: the presentation of the data must be intuitive and graphically appealing. In fact, as we have more
data, the more important is the ability to visualize them, since otherwise it would be very difficult to intuitively
identify patterns or correlations between the data;
Value: consideration should be given to the true value of data, in which data value must exceed its cost or
management. Therefore, storage and data access may be cost effective.
The characteristics of Big Data can be grouped into five categories [7]: (i) collecting data; (ii) processing data; (iii)
integrity data; (iv) visualization data; and (v) worth of data. These nine dimensions are mapped in each of these
categories, as indicated in Table 2.
Table 2. Categories of Big Data [7]
Big Data Characteristics
Collecting data
Veracity, Variety
Processing data
Velocity, Volume
Integrity data
Validity, Variability, Volatility
Visualization data
Worth of data
These nine dimensions don’t need to be taken together as the perfect definition. There are authors that argue that the
combination of “volume + velocity + variety” is sufficient to convey an acceptable notion of big data. From this
perspective the additional four dimensions are implicitly present in the above definition.
The process of dealing with large volumes of data is complex, and traditional programming approaches and data
organizations models are no longer suitable for working with large volume of data, particularly when most of these data
have no structure. Because of these characteristics, handling and processing big data requires specific tools and
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One of the most well-known frameworks is the Hadoop. It is an open source implementation of the Map-reduce
programming paradigm, introduced by Google to process and analyze large datasets. The programs that are developed
using this paradigm perform parallel processing of data sets and can therefore be run on servers without much effort.
The reason for the scalability of this paradigm is the intrinsically distributed nature of the solution's operation. A large
task is divided into several small tasks that are then executed in parallel on different machines and then combined to
arrive at the solution to the larger task that started it all [8]. Examples of using Hadoop are analyzing user patterns on e-
commerce sites and suggesting new products that they can purchase.
The Hadoop framework consists of two main components: storage and processing. The first is the Hadoop Distributed
File System (HDFS), which handles data storage between all the machines on which the Hadoop cluster is running [9].
The second, Map-Reduce, manipulates the processing part of the framework [10]. Figure 1 shows the high level
architecture of Hadoop.
Figure 1. High level architecture of Hadoop [11].
HDFS is a scalable, distributed file system whose design is heavily based on the Google File System (GFS), which
is also a distributed file system. Distributed file systems are needed once the data become too large to be stored on only
one machine. Because of this, all the complexity and uncertainties from the network environment come into play, which
makes network file systems more complex than ordinary file systems. HDFS stores all files in blocks. The default block
size is 64Mb. All files on the HDFS have multiple replicas, which help in parallel processing. HDFS clusters have two
types of nodes: (i) primary and secondary namenodes; and (ii) datanodes. Below we briefly introduce the mission of
these nodes [9]:
Primary namenodes: manages the file system namespace. It manages all files and directories. Namenodes have
the mapping between files and the blocks in which they are stored;
Secondary namenodes: this node is responsible for checking namenode information. In case of failure, these
nodes can be used to restart the system;
Datanode: stores the data as blocks. Datanodes report to namenodes about the files they have stored so that the
namenode is aware and the data can be processed.
Map-Reduce is a programming paradigm in which each task is specified in terms of mapping and reduction functions.
Map-Reduce offers a way to process large volumes of data by distributing the processing in many machines so that it is
processed in an acceptable time. This distribution implies parallel processing since the same function is applied on all
machines, but on different data sets in each of them. The approach adopted by Map-Reduce takes advantage of
parallelism to split the data load, rather than split the processing steps. Each component is responsible for completely
processing a small set of data, rather than processing all the data at a particular stage of computation.
Map-Reduce maps a dataset into a collection of tuples (key, value) and, after that, reduce all tuples with the same key
to produce the final output of the processing. This approach adopts the principles of abstracting all the parallelization
complexity of an application using only the Map and Reduce functions. Computing is distributed and controlled by the
framework, which uses its distributed file system and messaging exchange protocols to run a Map-Reduce application.
The processing is divided into three phases: (i) an initial mapping step, where several mapping tasks are performed; (ii)
an intermediate step where the data are collected from the mapping tasks, grouped and made available for the reduction
tasks; and (iii) a reduction step where various reduction tasks are performed by grouping the common values and
generating the output of the application [10]. The process of how Map-Reduce operates can be visualized in Figure 2.
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Figure 2. Visualization how Map-Reduce works [12].
3- Potentialities of Big Data
Several studies have reported the use of Big Data in a wide set of different contexts. In this sense, we approach some
of these projects by organizing this information by activity sector.
3-1- Financial Services
Financial services are good examples of how technology can help in the processing of large amounts of data. Big
data can be used to reduce churn rates, in order to anticipate possible customer exits. It can also be used to tighten
customer relationships and customize services. For example, understanding how customers use credit cards and what
kind of loans they need, help banks in creating new products that meet the customers’ needs.
Sarrocco et al. [13] advocate that unlike traditional business intelligence systems, new techniques and technologies
used with big data allow Chief Financial Officers (CFOs) to gain useful information at a much lower cost. The study
identified two groups of benefits: (i) tangible cost reductions; and (ii) intangible benefits. In the first group, we may find
significant cost reductions by the alignment of process, harmonized reporting and centralized data sources. In the second
group, we have more data-driven decision making, which will allow CFOs to discover new business opportunities.
Other studies complement the previous view, mentioning that financial services present high levels of big data, low
levels of knowledge assets, and high levels of competitive intelligence activity [14]. The challenge of dealing with high
levels of latency remains with the use of legacy services and critical operations that require a high level of operations’
serialization [15].
3-2- Retail Services
Big data is fundamental to the digital transformation of the retail services. Sales depend on the in-depth knowledge
of the target audience, which requires a systematic analysis of a large volume of information. The big data can be used
to implement multi-channel marketing actions, in which it becomes necessary to integrate all communication channels
to fully understanding the behavior of the client. This market awareness can be achieved through greater segmentation
of the target audience to understand their consumption habits and preferences. In addition, this information can be used
to create loyalty programs.
Shockley & Mercier [16] reported that around 62 percent of US retailers use information (including big data) and
analytics in creating competitive advantage for their organizations. They also describe the use of retail services in a
multi-channel shopping interaction. For example, a consumer might begin researching a product on a mobile app,
purchase it online and pick it up at a store. Coordinating this multi-channel shopping interaction requires the analysis of
a large volume of data to identify consumer preferences in each of these different channels of interacting with the retailer.
One channel that is expected to have a strong growth potential is e-commerce through mobile devices. At this level,
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there are studies that intend to understand emerging mobile checkout scenarios and customer reactions to these scenarios
The potential for applying Big Data to retailing is also proven by Grewal et al. [18] that consider five drivers in the
future of retailing, respectively: (i) technology and tools to facilitate decision making; (ii) visual display and merchandise
offer decisions; (iii) consumption and engagement; (iv) Big Data collection and usage; and (v) analytics and profitability.
3-3- Healthcare Services
Digital Health is the convergence of digital revolutions with the health field, genetic advances, and the speed of
technological improvements. Digital Health is very important for the health industry, as well as for society in general,
and it is only possible to reach them with the use of the big data. Digital Health requires the use of information systems
to collect, aggregate and work structured and unstructured data linked to the health sector, generating clinical
information that strengthens precision medicine. The big data can be used to prevent epidemics by monitoring a
population on social networks. In conjunction with the use of statistical analysis, it can help in the early visualization of
the possibility of an outbreak of an epidemic, giving health institutions time to adjust to sudden increases in demand for
care and medication. On the other hand, it can also be used in telemedicine, through the use of information technologies
and large data analysis in the provision of clinical information of patients.
Wu et al. [19] studied the adoption of wearable device together with big data technology. They provide practical
guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on
potential policy-making to promote better healthcare services. Dimitrov [20] suggests the use of medical devices and
mobile apps in telemedicine solutions via medical Internet of Things (mIoT).
Despite the existence of several studies that report on the successful use of Big Data in Healthcare Services, some
obstacles persist. Kruse et al. [21] report issues of data structure, security, data standardization, storage and transfers,
and managerial skills such as data governance. Actually, Big Data security and privacy is still the obstacle most widely
considered in scientific studies, in which several user's accessing mechanisms and strategies are proposed [22, 23].
3-4- Transport Services
Transport infrastructures are increasingly involved in technology. Travel and transportation companies must consider
an increasing amount of information to make strategic decisions. These data come from multiple sources such as
transactional data, asset data, supply chain data, call center logs, competitor pricing, weather data, NFC or RFID.
Therefore, in order to deliver “smart data”, more elaborate and sophisticated algorithms are needed. These algorithms
can be used in the optimization process of routes, resolution of traffic issues, road prevention, among others.
Kochhar [24] states that big data is already transforming both the plan phase and operations phase of the rail
transportation. He suggests that big data can be used in the following phases: (i) planning and demand modeling; (ii)
predictive maintenance; (iii) event response; and (iv) personalized services. For its part, Kanniyappan and McQueen
[25] predict what can be the future truck of 2025. They suggest that it will communicate with other vehicles and connect
to growing sources of online information as big data balloons on the road. Special cameras, wireless Lan, and multiple
radar system can be used to watch the road, the side of the road, and cars and trucks behind the vehicle. Additionally,
computerized controls of the vehicle will make it more fuel efficient.
3-5- Agriculture Sector
Precision farming has gained prominence in several countries, such as USA, Brazil or Australia. The use of big data
technologies generates a new agricultural revolution, guaranteeing an increase in productivity and production without
the need to increase the area. The decision on the type of seed to plant, how to plant, how to fertilize, how to use
defensive, harvest time, storage, transportation, and marketing decisions that need to be made by the farmer are already
being made by technology. Wolfert et al. [26] state that big data is expected to have a large impact on smart farming and
in the whole supply chain. It will also cause major shifts in roles and power relations among traditional and non-
traditional players.
Bronson and Knezevic [27] reported the use of Integrated Field Systems (IFS) on the agri-food sector in the North
American food system. The IFS can be used to collect information about soil conditions, weed varieties and weather. It
can also be used to identify weeds and map weed pressures in a digital mapping tool, which will help framers identifying
chemical needs and therefore areas of possible investment in research and development. This information will help
framers to minimize risk and streamline decision-making.
3-6- Energy Sector
The potentialities of using big data analytics for companies in the energy sector are expressive. It can be used in the
improvement of marketing campaigns, in operational fields and to explore new business opportunities. The advantages
offered by the adoption of big data are not restricted to very competitive environments, but also in more regulated
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markets. Big data applications can be present along the entire value chain, from the generation to the commercialization
of services in real time, in the process of back-end and front-end optimizations, in the management of clients through
the analysis of the profile of consumption, and in the distribution energy process to ensure a more balanced between
supply and demand data.
Porter [28] describes the adoption of big data using different scenarios in the energy sector. For example, generators
use market data to optimize their dispatch, ramping flexible assets up and down in response to real time supply and
demand forecasts. This can be done by collecting information regarding plant operational performance, availability and
technical parameters. Other example is the adoption of smart meters to provide both users and suppliers with granular
consumption data. This can be done by integrating a wide range of IoT devices and sensors.
In addition to these scenarios, two areas emerge as presenting a high potential for Big Data usage. One of them is the
adoption of smart girds, which allow optimize the process of generation, distribution and consumption of electricity
through the introduction of information and communication devices and technologies [29]; another area is energy
efficiency through creation of energy efficient buildings in the process of managing temperature and solar exposure
3-7- Manufacturing Sector
In recent years manufacturers have been able to reduce waste and variability in their production processes by
introducing new management practices in the fight against waste. In addition to cost savings, these practices provided
significant improvements in the final product quality. Big data can be used across multiple domains. One of them is the
integration of the supply chain to achieve better condition in the supply of raw material and to launch real-time orders
according to the needs of customers. Furthermore, it can be used in the analysis of historical process data to identify
patterns and relationships between various internal processes.
Agrawal [31] reported that one of the areas that will benefit more from the growth of big data market is the
manufacturing industry, with revenues projected to reach $39 billion by 2019. Delgado [32] emphasizes the role of big
data in improving quality and safety. He refers that companies can install computerized sensors on assembly lines to
allow that generated data could be used to enable the improvement of the quality and safety of the manufacturing process.
Smart manufacturing appears as a new paradigm in the development of the manufacturing sector, which intends to
take advantage of information technologies to increase the degree of autonomy and flexibility of the production process.
Associated to this concept arises the Industry 4.0 concept that intends to change the paradigm of the productive process,
in which we have data-driven decisions. To this end, and in order to concretize the Industry 4.0 revolution, the use of
Big Data becomes essential [33-35].
3-8- Media and Entertainment Sector
Media and entertainment companies seek to increase the value of their media assets by combining creativity with
technological advancements. This offers significant opportunities for broadcasters, publishers, advertising agencies,
owners of digital platforms, and content provides capable of understanding the impact of this change. Nowadays, many
audiences are becoming one’s audience, forcing the media sector to become more data-driven. Digital and media groups
need to understand changes in consumption patterns, including what programs and content are viewed through digital
and traditional channels, as well as other content sources.
Lippell [36] considers that media companies are an early adopter of big data technologies, because it enables them to
drive digital transformation, exploiting new sources of data from both inside and outside the organization. Almost every
media associated business with large volumes of data can leverage big data, namely [37]:
Video publishers independent or private video creates who publish content in different types of format;
Media owners businesses that sell content through retail or mass content distribution mediums;
Gaming companies video game makers that can log gamer reaction and customize the experience of the game
according to the experience of players’ use;
TV channels television channels that can tailor content and advertising according to the audience at different
hours of the day.
3-9- Public Sector
Decisions by public managers involve difficult daily budget choices, program prioritization, natural disaster
prevention and epidemics, as well as infrastructure investments. Organizing all this operational and strategic action plans
based solely on intuition invariably generates administrative collapse. Big data for public management can help public
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managers in areas such as combat against corruption, strengthening the implementation of smart cities and monitoring
the level of population satisfaction.
The benefits of big data in the public sector can be grouped into three major areas [38]: (i) improvements in efficiency;
(ii) improvements in effectiveness; and (iii) learning from the performance of public services. Lauchlan [39] describes
the adoption of big data to deliver social benefits. The Home Office Child Abuse Image Database in the UK has benefited
from the use of big data in the investigation of child abuse crimes and child protection. Using the image’s unique
identifiers and metadata, local protection authorities can check devices the0ve seized from suspects against the material
on the database much more quickly. Previously, a process containing 10000 images would take around 3 days to review,
can be reviewed only in an hour, which originates a cheaper, less labor-intensive and more efficient.
4- Vulnerabilities of Big Data
In addition to the many potentialities offered by the big data, there is also a set of vulnerabilities that need to be
known and understood. Three types of vulnerabilities associated to big data management and processes were identified:
(i) security; (ii) privacy; and (iii) absence of standards.
Big data users must be concerned about the potential vulnerability of their data, and take a proactive approach to
implement security mechanisms. The importance of the data for the organization, the legal requirements to process and
store data, and whether the data storage is totally secure against unauthorized access should be questioned. According
to Moreno et al. [40] the major security flaws that can be found in big data are related to the lack of authentication
mechanisms, such as user id and password, and the lack of secure channels for access cloud databases.
The big data computing process is distributed across multiple servers or nodes. A set of data running in the Hadoop
is composed of several nodes, sometimes in the order of hundreds or thousands, and a large volume of information is
constantly migrated between each of these nodes. This highly dynamic environment of distributed computing, and the
enormous amounts of elastic data involved, makes big data security a major challenge. Many traditional security
approaches, such as firewalls and intrusion detection systems (IDS), are not designed to protect distributed file systems
and handle this huge amount of data.
Hadoop currently supports security mechanisms to mitigate these risks, including the use of Kerberos, the adoption
of firewalls, and the implementation of basic HDFS permissions [41]. However, Kerberos is not a mandatory
requirement for a Hadoop cluster, which makes it possible to run entire groups of data without implementing any
security. In addition, Kerberos is difficult to install and configure in the cluster, and the integration of Active Directory
and Lighweight Directory Access Protocol (LDAP) is also a challenge. Therefore, other security practices have emerged,
such as the implementation of security controls closer to the data, and the implementation of cryptographic models in
all data, both statically and in transit.
Big Data, social media, and predictive algorithms open up a new field of privacy discussion. All technology is neutral.
However, what we do about it can have a positive or negative impact on society. Companies can use the vast amount of
data that they have on their customers for a promotion that significantly improves their customers' experience, or it can
be used unlawfully to violate their privacy. Big Data unquestionably increases the risk of privacy breaches in various
domains, particularly in the Internet and social networks contexts. Data extracted from social networks such as
Facebook, Linkedin or Twitter allow companies to obtain very diverse information about their users. For example, it
would be possible to know patterns of travel, where they eat lunch every day, entertainment options, etc.
The question of privacy is associated with the concept of what will be ethically acceptable or not. In addition to the
need for a set of more explicit and comprehensive laws on this subject, it is essential that companies, governments and
public entities adopt a code of conduct. This code of conduct should unambiguously identify the purpose of the data
collection, its scope and the limits for its collection. Pentland from MIT advocates the existence of a "New Deal" of
data, in which personal data are treated as a good, where people would have their rights assured over their own data
[42]. That is, regardless of who collects the data about the user, they will belong to the user and he/she can access to the
data whenever he/she wants.
Finally, one point of vulnerability lies in the absence of widely accepted standards in the big data domain. Standards
are used to enable, to promote, to measure and govern the use of technology across a wide spectrum of communities.
Standardization is essential in any mass-deployed system, considering that it increases independent use and comparative
evaluations of the technology [43].
Standard communication protocols are also needed to build the new generations of smart appliances. Currently, there
are a large number of communication protocols in various application domains of the big data, which creates
interoperability and large-scale customization issues. The lack of standardization inhibits innovation and creates barriers
to the adoption of big data by users, since it becomes necessary to know a great number of different protocols. Therefore,
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there is a need for greater collaboration between industry and government entities that simultaneously promote
standardization and competition in the market.
5- Big Data: Opportunities and Privacy Challenges
The ability to process and analyze a large volume of data was only available to a minority of large companies until
few years ago. However, with technological advances, the emergence of open source frameworks and tools has enabled
data to be exploited by more organizations effectively and efficiently. It is also important to recognize that the
popularization of electronic devices and the advent of IoT have exponentially increased the volume of data generating
new market opportunities. Research has been carried out on the proposed models that allow IoT to encrypt data to
enforce data privacy [44-45].
Data emerge as a new driver of productivity, work, and innovation. However, most of the business models in the
various sectors of activity mentioned in the previous section do not fully exploit the potential value of these data.
Therefore, Big Data present a high potential still unexplored to get better business results.
The fear of investing in technology still in the embryonic stage and the need of changing organizational work
processes are considered major issues that still delay the entry of many companies into Big Data [46-47]. It should also
be mentioned that when we talk about Big Data, we are also addressing the topic of Cloud Computing, with many
managers claiming the fear of sharing important information in an emergent field, and consequently considered by many
as insecure [48]. In addition to this challenge, there are also some fears of companies on adopting a technology with
limited availability. In fact, the use of the Internet can cause delays and unavailability of services, since the architecture
of the Internet was not designed to offer Quality of Service (QoS) [49].
Privacy at Big Data is a growing business concern. Several models have been proposed and developed for privacy
protection at different stages of the Big Data life cycle, such as data generation, data storage, and data processing [50].
Despite this, there are still few companies that have a Chief Privacy Officer, enough Big Data skills, and concrete
measures to audit the processes [51]. All technology is neutral by itself, so its impact depends on how we use it.
Nowadays new tools for manipulating and exploiting Big Data appear, but these technological advances must also
be aligned with the organizational culture of companies. It is essential to create a culture of data-based decision-making,
the culture of transforming information into knowledge, which implies the creation of a data-driven culture in the
6- Conclusion
The ultimate goal of big data techniques is to be able to identify useful and usable information in a timely fashion. In
order to produce actionable analytics there is a need to analyze lots of disparate data and the ability to discover, access,
store and retrieved lots of data. The solutions that enable big data analytics processes are systems that involve technical
aspects, both at the time of collection, processing and presentation of unstructured obtained data. A central point of the
today’s big data evolution is the Hadoop framework, which offers an efficient file system and an ecosystem of solutions
for storing and analyzing large data sets.
Big data can help companies in the process of taking operational, tactical and strategic decisions. There is a large
panoply of potentialities in the use of big data in various sectors of activity, such as financial, retail, healthcare, transport,
agriculture, energy, manufacturing, media and entertainment, and public sector. However, despite the many
potentialities offered by the big data, there are also vulnerabilities related to data security, privacy, and standardization
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... Fig. 1 shows the order of occurrence of each feature. Classic database software tools for collecting, storing, organizing, and evaluating data cannot handle big data because of its size [6,8]. Traditional database tools, such as Structured Query Language (SQL), also cannot control and evaluate big data [7,8,9,16]. ...
... Classic database software tools for collecting, storing, organizing, and evaluating data cannot handle big data because of its size [6,8]. Traditional database tools, such as Structured Query Language (SQL), also cannot control and evaluate big data [7,8,9,16]. Authors belonging to classical database and traditional database groups have focused only on the volume aspect of data when describing big data. ...
... Given the volume, velocity, and veracity of big data, volatility must be considered wisely and carefully [23,42]. In general, these characteristics of big data are categorized into five groups [8]: 1collection of data, 2processing of data, 3data integrity, 4data presentation, and 5data worth. We add another important category that must be considered in the present era of advanced technology, i.e., 6data security. ...
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Dealing with big data at present involves several procedures, including storage, processing, indexing, integration, and governance; thus, it poses a major challenge to application developers and research scientists. The challenge increases when accurate results must be obtained for queries in decision-making and cognitive applications regarding various disciplines and industries. A variety of literature on big data is available. However, big data must still be understood through various aspects, such as features, classification, analytics, research challenges, and future trends. This study provides a deep understanding of big data features, classification, and manipulation. An improved understanding of big data analytics and recent research challenges is also provided. Additionally, future trends are discussed for researchers. In conclusion, a vision to wisely and accurately improve information retrieval or search is presented to make better decisions using big data.
... On one hand, and with the emergence of digital information, communication, sensing and characterization technologies, the manufacturing processes are generating more and more deluge of complex and heterogeneous data flowing from anywhere, any-time, and any-device generating so-called "Big Data" (He and Wang, 2018;Sivarajah et al., 2017;Wamba et al., 2017). Undeniably, big data is a pivotal phenomenon for the digital transformation of advanced manufacturing systems (Almeida, 2018). Furthermore, the growth of big data raises the immense need for companies to develop BDA capabilities to retrieve meaningful insights from data (Belhadi et al., 2019). ...
... Seven dimensions viz., volume, velocity, variety, veracity, variability, volatility, and value are used to characterize big data (Belhadi et al., 2019). Usually, Big data is related to data analytics that use statistical knowledge to support decision-making (Almeida, 2018). Further, Resources Based View (RBV) has been used as the underpinning theory to conceptualize BDA capabilities in most of the studies (Kamble and Gunasekaran, 2019a). ...
With the advent of Big Data Analytics (BDA) alongside the maturity of specific improvement approaches such as Lean Six Sigma (LSS) and Green Manufacturing (GM), the integration of these initiatives to achieve higher environmental performance (EP) is gathering the interest of both researchers and practitioners. The present study builds on the dynamic capabilities view to propose and empirically test a model exploring whether LSS and GM mediate the relationship between BDA capabilities and EP. A two-stage hybrid Factorial Analysis - Structural Equation Modelling is used to draw insights from 201 industry practitioners from North African companies. The findings confirm the direct influence of BDA on EP and also identify LSS and GM as significant mediating variables that act as a catalyst to boost indirect impacts of BDA on EP. This study can help researchers and practitioners to fully understand and benefit from BDA capabilities and improvement initiatives such as LSS and GM while managing environmental issues. The study discusses theoretical and managerial implications for enhancing the environmental performance of the manufacturing organizations. Keywords Environmental Performance, Big Data Analytics, Lean Six Sigma, Green Manufacturing, Structural equation modeling
... These findings are aligned with the results of recent studies published in the literature, which refer that the incorporation of new technologies in a physical store positively affects both the experience and the buying intention (Foroudi et al., 2018;Mosquera et al., 2018). Additionally, Grewal et al. (2017) and Almeida (2018) mention the use of these technologies in fostering big data collection, contribute to a better analytical analysis of business profitability, and provide new insights and forecasts using predictive analysis models. ...
M-commerce is a fast-growing opportunity and is acting as an innovative lever for achieving the purpose of increasing sales while better interacting with the clients. Simultaneously, several emerging technologies have appeared in the market and promise to change the current m-commerce paradigm. Therefore, this chapter plans to explore a set of new trend technologies that can plan to build a more efficient relation between the consumer and the m-commerce platform. This study conducted surveys with several market players like marketers and IT leaders to understand their point of view, perceive the relevance and impact of these emergent technologies in m-commerce, identify resistance and challenge points to the proposed change, and look how to allow cohabitation between this new e-commerce paradigm and the traditional physical trade. The main novelty of this study is the inclusion of multiple points of view on the evolution of m-commerce which will allow companies and citizens to perceive the impact of emerging technologies in the future of m-commerce.
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Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.
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The Sea-wave Slot-cone Generator (SSG) wave-energy device is a type of electric energy converting structure that converts energy from sea waves, and which is designed and installed based on wave-overtopping in areas. Most of the previous studies have evaluated SSG systems based on hypothetical waves, considering the system geometry variations. However, it is important to consider the real wave conditions. This paper presents the results of a numerical study to investigate the performances of an SSG system in the context of the Persian Gulf and Oman Sea, where there is a strong need for renewable energies. The computational fluid dynamic (CFD) code Flow-3D was applied. First of all, available experimental data were applied to calibrate and evaluate the accuracy of the numerical model. Then, the real wave conditions on the coasts of the Persian Gulf and Oman Sea were imposed on the JONSWAP spectrum for the numerical modeling. Results of the study demonstrated that the hydraulic efficiency of the SSG system in the Persian Gulf and Oman Sea was low for wave heights lower than 0.5 m. The nominal efficiency of the system was relatively more than 60% for wave heights higher than 1 m; thus, the performance of the SSG system was suitably evaluated. Finally, the numerical results demonstrated that the most optimal conditions, with a nominal efficiency of 97%, were obtained for incident waves that had a height of 2 m and a period of 5.6 s. In this case, the hydraulic performance of the system was maximum.
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The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.
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Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare. Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. However, deciding on the allowable uses of data while preserving security and patient’s right to privacy is a difficult task. Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed. To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed the state-of-the-art security and privacy challenges in big data as applied to healthcare industry, assessed how security and privacy issues occur in case of big healthcare data and discussed ways in which they may be addressed. We mainly focused on the recently proposed methods based on anonymization and encryption, compared their strengths and limitations, and envisioned future research directions.
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The ever-increasing integration of highly diverse enabled data generating technologies in medical, biomedical and healthcare fields and the growing availability of data at the central location that can be used in need of any organization from pharmaceutical manufacturers to health insurance companies to hospitals have primarily make healthcare organizations and all its sub-sectors in face of a flood of big data as never before experienced. While this data is being hailed as the key to improving health outcomes, gain valuable insights and lowering costs, the security and privacy issues are so overwhelming that healthcare industry is unable to take full advantage of it with its current resources. Managing and harnessing the analytical power of big data, however, is vital to the success of all healthcare organizations. It is in this context that this paper aims to present the state-of-the-art security and privacy issues in big data as applied to healthcare industry and discuss some available data privacy, data security, users’ accessing mechanisms and strategies.
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The internet of things (IoT) is a technology that has the capacity to revolutionise the way that we live, in sectors ranging from transport to health, from entertainment to our interactions with government. This fantastic opportunity also presents a number of significant challenges. The growth in the number of devices and the speed of that growth presents challenges to our security and freedoms as we battle to develop policies, standards, and governance that shape this development without stifling innovation. This paper discusses the evolution of the IoT, its various definitions, and some of its key application areas. Security and privacy considerations and challenges that lie ahead are discussed both generally and in the context of these applications.
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Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology.
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A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.
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In a cloud environment, uncertainty and resource dispersion lead to problems with resource allocation due, for instance, to heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient workload performance and applications, these issues must be addressed effectively. The authors offer a broad, methodical literature analysis of resource management in cloud computing, including resource provisioning, resource scheduling, and autonomic resource provisioning and scheduling. They describe the current status of resource management in cloud computing and provide further analysis of its techniques as developed by various industry and academic groups. They also look at possible future directions for resource management in cloud computing.
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The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.
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