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Chapter 9 Survey on Big Data Applications

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The goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security, telecommunications, and manufacturing. In response to the problems of analyzing large-scale data, different tools, techniques, and technologies have bee developed and are available for experimentation. In our analysis, we focused on literature (review articles) accessible via the Elsevier ScienceDirect service and the Springer Link service from more recent years, mainly from the last two decades. For the selected industries, this chapter also discusses challenges that can be addressed and overcome using the semantic processing approaches and knowledge reasoning approaches discussed in this book.
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Chapter 9
Survey on Big Data Applications
Valentina Janev1(B
), Dea Puji´c1, Marko Jeli´c1,
and Maria-Esther Vidal2
1Institute Mihajlo Pupin, University of Belgrade, Belgrade, Serbia
2TIB Leibniz Information Centre For Science and Technology, Hannover, Germany
Abstract. The goal of this chapter is to shed light on different types
of big data applications needed in various industries including health-
care, transportation, energy, banking and insurance, digital media and
e-commerce, environment, safety and security, telecommunications, and
manufacturing. In response to the problems of analyzing large-scale data,
different tools, techniques, and technologies have bee developed and are
available for experimentation. In our analysis, we focused on literature
(review articles) accessible via the Elsevier ScienceDirect service and
the Springer Link service from more recent years, mainly from the last
two decades. For the selected industries, this chapter also discusses chal-
lenges that can be addressed and overcome using the semantic processing
approaches and knowledge reasoning approaches discussed in this book.
1 Introduction
In the last decade, the Big Data paradigm has gain momentum and is gener-
ally employed by businesses on a large scale to create value that surpasses the
investment and maintenance costs of data. Novel applications have been created
for different industries allowing (1) storing as much data as possible in a cost-
effective manner (volume-based value); (2) rapid analysis capabilities (velocity-
based value); (3) structured and unstructured data to be harvested, stored,
and used simultaneously (variety-based value); (4) accuracy of data processing
(Veracity-based value); etc. In the next decade, the amount of data will continue
to grow and is expected to reach 175 zetabytes in 2025 [85]. This will fundamen-
tally affect worldwide enterprises. This chapter is interested in identifying:
RQ1: What are the main application areas of big data analytics and the specific
data processing aspects that drive value for a selected industry domain?
RQ2: Which are the main tools, techniques, and technologies available for
experimentation in the field of big data analytics?
In December 2018, within the LAMBDA project framework, a literature review
was initiated that included an extensive and comprehensive analysis of journal
The Author(s) 2020
V. Janev et al. (Eds.): Knowledge Graphs and Big Data Processing, LNCS 12072, pp. 149–164, 2020.
150 V. Janev et al.
Fig. 1. Research methodology
articles from available sources such as (1) the Elsevier ScienceDirect service1and
(2) the Springer Link service2. Elsevier ScienceDirect is a website which provides
subscription-based access to a large database of scientific and medical research. It
hosts over 12 million pieces of content from 3,500 academic journals and 34,000
e-books. SpringerLink is the world’s most comprehensive online collection of scien-
tific, technological and medical journals, books and reference works printed from
Springer-Verlag. In parallel, the market of available commercial and open-source
tools was surveyed and monitored3.AsBig Data is a very active area of research
nowadays, we are also involved in analysis of different industry cases studies, as
is presented in the research methodology depicted in Fig. 1. This chapter outlines
the methodology and the process of selecting articles relevant for our research (see
Sect. 2) and discusses the main research trends in big data applications in different
industries (Sect. 3). In order to answer the second research question, the authors
established the catalog of big data tools that is available at the LAMBDA project
web page4.
2 Literature Review
This section presents the literature review approach that was adopted in order to
identify the relevant application areas of big data technologies. In April 2020, a
simple keyword based query on term Big Data Analytics returns:
180,736 results in ScienceDirect (or 3% more than in December 2019, 174,470
results), 10,042 of them review articles, where the oldest 2 papers are from 1989
and discuss the challenges to computational science and use of supercomput-
ers for conducting experiments in key scientific and engineering areas such as
Chapter 9 Survey on Big Data Applications 151
atmospheric science, astronomy, materials science, molecular biology, aerody-
namics, and elementary particle physics [467];
40,317 results in SpringerLink (or 7% more than in December 2019, 33,249
results), where the oldest publications dating from 1950s are related to math-
Big Data Analytics is a broad topic that, depending on the objectives of
the research, can be linked on the one hand to data science and machine learn-
ing, and on the other to data and software engineering. Being interested in the
role that analytics plays in business strategy, we limited our search to articles in
the domain of business intelligence. Business intelligence entails the analysis of
past and present data to create actionable insights for informed decision-making.
Thus, the search for review articles linked to Big Data Analytics and Business
Intelligence leads to 615 articles. The number is even smaller if we are looking
for Business Intelligence (BI) and NoSQL solutions– see Table 1. That means
that the concept of Business Intelligence still prevails in the scientific litera-
ture but is based on relational database-driven applications. Further on, looking
for the year of publication, the authors have found that there are articles from the
1930s also linked to the topic Big Data albeit mainly related to medical studies.
In our analysis, we focused on review articles from more recent years, mainly from
the last two decades.
Table 1. Number of review articles in ScienceDirect database
Keywords 1995–1999 2000–2005 2006–2009 2010–2015 2016–2020 T otal
BDA 388 718 1349 2190 4,605 10,042
BDA and BI 12 15 45 80 437 615
BDA and BI
and NoSQL
331 35
BDA and Apps
and NoSQL
846 54
Hence, in order to identify the main application area, we first identified jour-
nals (using ScienceDirect service) that most frequently publish research arti-
cles about Big Data Analytics,Business Intelligence,andApplications in
Industry. ThetTable below points to a number of articles published in interna-
tional journals between 2015 and 2019, as well as the journals relevant for the
search criteria. What can be noticed is that there are three times more articles
related to Big Data and Applications, then to Big Data Analytics and
Applications. The number of retrieved results is drastically smaller if we intro-
duce the topic ‘Business Intelligence’.
Some of the journals listed in Table 2refer to scientific fields that are not
in direct relation to the research conducted in the LAMBDA project, such as
Nuclear Physics and Astrophysics, Materials Science, Construction and Architec-
ture, Chemistry and Chromatography. Big data research is conducted in these
152 V. Janev et al.
Table 2. Journals that match the search criteria
‘Big Data’ and
Neurocomputing, Journal of Cleaner Production, Procedia
Computer Science, IFAC Proceedings Volumes, Expert
Systems with Applications, Physica A: Statistical Mechanics
and its Applications, Sensors and Actuators B: Chemical,
Journal of Chromatography A, Nuclear Physics B, European
Journal of Operational Research
‘Big Data’ and
‘Industry’ (59,734)
Journal of Cleaner Production, Future Generation Computer
Systems, Energy Policy, Journal of Membrane Science,
Expert Systems with Applications, Procedia Computer
Science, Journal of Banking and Finance, Research Policy,
European Journal of Operational Research
‘Big Data
Analytics’ and
Journal of Cleaner Production, Future Generation Computer
Systems, Neurocomputing, Journal of Chromatography A,
IFAC Proceedings Volumes, Physica A: Statistical Mechanics
and its Applications, Sensors and Actuators B: Chemical,
Analytica Chimica Acta, Journal of Membrane Science,
Nuclear Physics B
‘Big Data
Analytics’ and
Future Generation Computer Systems, Procedia Computer
Science, Technological Forecasting and Social Change,
Expert Systems with Applications, Decision Support
Systems, IFAC Proceedings Volumes, Accounting,
Organizations and Society
disciplines and there is a need for enhanced statistical algorithms, modeling and
simulation approaches; however, these scientific areas are currently beyond the
scope of our research and will not be discussed in the following sections.
Trends: Detailed analysis of the retrieved surveys on BDA and Apps and
NoSQL (54 papers) showed that there is a shift of focus from operational data
management systems, data-warehouses and business intelligent solutions (present
for instance in Finance and Insurance domain in 1990s) [336] to parallel and dis-
tributed computing [478], as well as scalable architectures [187] for storing and
processing data in the cloud (“Analytics in Cloud” [368]). Emerging paradigms
such as the Internet of Things [120,369] and blockchain additionally influence
cloud computing systems [157]. Interconnected technologies like RFID (Radio Fre-
quency IDentification) and WSAN (Wireless Sensor and Actor Networks) enabled
development of smart environments [122] that will be explored further in subse-
quent sections. Wide availability of cheap processing power and vast amounts of
data in recent years have enabled impressive breakthroughs in machine learning
[123,178,269], semantic computing [222,316], artificial neural networks and mul-
timodal affective analytics [400].
Chapter 9 Survey on Big Data Applications 153
3 Big Data Analytics in Industrial Sectors
The analysis presented in this section examines the BDA-driven applications in
sectors spanning healthcare, transport, telecommunications, energy production
and smart grids, energy consumption and home automation, finance, media, e-
Government [220] and other public utilities. The research was motivated by the
needs of the Mihajlo Pupin Institute to innovate the existing product portfolio
that is currently mainly focused on building advanced analytical services for con-
trol, monitoring and management of large facilities, for instance from the trans-
port and the energy sector.
Healthcare and Pharma
Healthcare and Data Engineering. Advances in Internet of Things (IoT) and
sensor devices have enabled integrated data processing from diverse healthcare
data sources in a real-time manner [339]. In addition to existing sources (Elec-
tronic Health Record and Clinical reports), healthcare providers can use new data
sources such as social media platforms, telematics, and wearable devices in order
to personalize treatment plans. However, healthcare organizations face unique
challenges when it comes to developing and implementing the smart health con-
cept [11] based on using a remote cloud server with powerful computing capabili-
ties. Besides taking into account the 3Vs (volume, velocity and variety) that raise
issues related to scalability, efficiency, speed, transparency, availability, reliability,
security, and others, the veracity dimension is very important because the value
of health information is directly dependent on the ability to determine the qual-
ity of the data in question (accuracy, correctness, reliability). Hence, fog-enabled
smart health solutions are proposed where fog nodes create a heterogeneous fog
network layer and complement a portion of computation and storage of the cen-
tralized cloud server [421].
Personalized medicine is an approach to the practice of medicine that uses
information about a patient’s unique genetic makeup and environment to cus-
tomize their medical care to fit their individual requirements. Recently, epigenet-
ics has grown in popularity as a new type of science that refers to the collection of
chemical modifications to the DNA and chromatin in the nucleus of a cell, which
profoundly influence the functional output of the genome. The identification of
novel individual epigenetic-sensitive trajectories at the single cell level might pro-
vide additional opportunities to establish predictive, diagnostic and prognostic
biomarkers as well as drug targets [386]. Based on emerging trends, patient care
can be improved in many ways including using:
modern healthcare applications that almost every smartphone possesses like
Apple Health5, Google Health6or Samsung Health7are used for spotting
trends and patterns;
154 V. Janev et al.
– the data obtained by wireless body area networks, implemented with ade-
quate permissions by the user (WBANs) can be integrated (with clinical tri-
als, patient records, various test results and other similar data) and analysed
in order to improve the effectiveness of medical institutions and to aid doctors
in their decision making;
advanced data management and processing (patient similarity, risk stratifica-
tion, and treatment comparison [345]) for better prescription recommendations
and optimizations of the drug supply chain, which results in cutting losses and
increasing efficiency.
Over the years, the role of Artificial Intelligence in medicine has become
increasingly important, for instance for image processing and diagnosis purposes.
Also deep-learning neural networks have proved very useful for extracting associ-
ations between a patient’s condition and possible causes. To summarize opportu-
nities and challenges of using innovative big data tools in healthcare, we point in
Table 2to the COVID-19 outbreak that occurred this year (Table 3).
Table 3. Case study: coronavirus disease 2019 (COVID-19)
Description The outbreak of the 2019 novel coronavirus disease (COVID-19) has
caused more than 5 million people to be infected and hundred of
thousands of deaths. In the fight against the disease, almost all
countries in the world have taken radical measures utilizing big data
technologies. [485]
Key challenges - Integration of heterogeneous data, which requires governments,
businesses, and academic institutions to jointly promote the
formulation of relevant policies
- Rapid collection and aggregation of multi-source big data
- GIS technologies for rapid visualization of epidemic information
- Spatial tracking of confirmed cases and estimation of population
- Prediction of regional transmission, spatial segmentation of the
epidemic risk and prevention level
- Balancing and management of the supply and demand of material
resources (checked 22/05/2020).
Pharma. New trends in pharmaceutical research (such as genomic computing
[370]) make the process of discovering disease patterns, early epidemic and pan-
demic detection and forecasting much easier. Das, Rautaray and Pandey [96] out-
line the general potential uses of big data in medicine like heart attack prediction,
brain disease prediction, diagnosis of chronic kidney disease, analysing specific dis-
ease data, tuberculosis prediction, early hearth stage detection, HIV/AIDS pre-
diction and some general aspects like disease outbreak and disease outcome pre-
diction. Lee and Yoon [275] discuss some technical aspects of big data applications
Chapter 9 Survey on Big Data Applications 155
in medicine like missing values, the effects of high dimensionality, and bias con-
trol. Ristevski and Chen [374] mention privacy and security on the topic of big
data in healthcare, while Tafti [420] offers an open source toolkit for biomedical
sentence classification. Modern concepts relating to mobile health are discussed
in [214] with Bayne [32] exploring big data in neonatal health care.
Transportation and Smart Cities
As suggested in Chap. 1, Smart Transportation is one of the key big data vertical
applications besides Healthcare, Government, Energy and Utilities, Manufactur-
ing and Natural Resources, Banking and Insurance, the Financial industry, Com-
munications and Media, Environment and Education. The collection of related
articles to this topic is possibly the largest of all applications. Zhang [483]oers
a methodology for fare reduction in modern traffic congested cities, Liu [285]dis-
cusses the Internet of Vehicles, Grant-Muller [165] talks about the impacts that
the data extracted from the transport domain has on other spheres, Torre-Bastida
[429] talks about recent advances and challenges of modern big data applications
in the transportation domain, while Imawan [211] analyses the important concept
of visualization in road traffic applications. Also related, Ghofrani [154]surveys
big data applications for railways, Gohar [158] discusses data-driven modelling in
intelligent transportation systems, and Wang [454] attempts fuzzy control appli-
cations in this domain. Herein, we will discuss route planning applications and
future challenges related to self-driving cars and user behaviour analysis.
Route Planning Applications. Using Global Positioning System (GPS) data,
for instance, a large number of smartphone users benefit from the routing system
by receiving information about the shortest or fastest route between two desired
points. Some applications like Waze rely on direct user inputs in order to locate
closed-off streets, speed traps etc. but at its most rudimentary level, this approach
can work with just raw GPS data, calculating average travel times per street seg-
ments, and thus forming a live congestion map. Of course, such a system would be
of no benefit to end users if it were not precise, but since the aggregated results that
are finally presented are obtained based on many different sources, classifying this
as a big data processing task, the data uncertainty is averaged out, an accurate
results tend to be presented. In order to provide a quick response, geo-distributed
edge devices also known as edge servers are used that can form an edge cloud for
providing computation, storage and networking resources to facilitate big data
analytics around the point of capture [91].
Self-driving cars rely on vast amounts of data that are constantly being pro-
vided by its users and used for training the algorithms governing the vehicle in
auto-pilot mode. Holding on to the automation aspect, big data processing in the
transportation domain could even be used to govern traffic light scheduling, which
would have a significant impact on this sector, at least until all vehicles become
autonomous and traffic lights are no longer required.
User Behaviour Analysis. Furthermore, the transportation domain can be
optimized using adequate planning obtained from models with data originating
156 V. Janev et al.
from user behaviour analysis. Ticketing systems in countries with high population
density or frequent travellers where reservations have to be made, sometimes, a
few months in advance, rely on machine learning algorithms for predictions gov-
erning prices and availability. Patterns discovered from toll collecting stations and
border crossings can be of huge importance when planning the duration of one’s
trip and optimizing the selected route.
Energy Production and Smart Grids
Energy Production. The energy sector has been dealing with big data for
decades, as tremendous amounts of data are collected from numerous sensors,
which are generally attached to different plant subsystems. Recently, modern
big data technologies have also been applied to plant industry such as oil and
gas plants, hydro, thermal and nuclear power plants, especially in the context of
improving operational performance. Thus, some of the applications of big data
in the oil and gas industry [311] are analyzing seismic and micro-seismic data,
improving reservoir characterization and simulation, reducing drilling time and
increasing drilling safety, optimization of the performance of production pumps,
improved petrochemical asset management, improved shipping and transporta-
tion, and improved occupational safety. Promising applications of big data tech-
nology in future nuclear fusion power plants are (1) data/plasma modeling in gen-
eral [88], (2) real-time emergency planning [276], (3) early detection of accidents in
reactors [290], etc. Related to hydro-power plants, many authors have discussed
the use of IoT applications for measuring water supply (see Koo [260], Bharat
[396]orKu[418]). Zohrevand [490] talks about the application of Hidden Markov
models for problem detection in systems for water supply.
Smart Grids. The smart grid (SG) is the next-generation power grid, which uses
two-way flows of electricity and information to create a widely distributed auto-
mated energy delivery network [155]. The goal is to optimize the generation, dis-
tribution and consumption of electricity. In general, there are three main areas
where data analytics have been applied:
Ensuring smart grid stability, load forecast and prediction of energy demand
for planning and managing energy network resources;
Improving malfunction diagnosis, either on the production side (in plant facili-
ties) or health state estimation, and identifying locations and forecasting future
line outages in order to decrease the outage costs and improve system reliabil-
Profiling user behaviours to adjust individual consumption patterns and to
design policies for specific users.
Smart metering equipment and sensors provide key insights into load distribu-
tion and profiles required by plant operators to sustain system stability. Predictive
maintenance also plays a key role in smart grid upkeep since all segments are both
critical and expensive, and any unplanned action cuts users from the electricity
Chapter 9 Survey on Big Data Applications 157
supply upon which almost all modern devices rely to function. Analytics method-
ologies or algorithms used in these cases are: 1) statistical methods; 2) signal pro-
cessing methodologies; 3) supervised regression forecasting (short and long-term
forecasts); 4) clustering algorithms; 4) dimensionality reduction techniques; and
5) feature selection and extraction. Tu [431] and Ghorbanian [155] present a long
list of various open issues and challenges in the future for smart grids such as
lack of comprehensive and general standard, specifically concentrated on big
data management in SGs;
interoperability of smart devices dealing with massive data used in the SGs;
the constraint to work with approximate analytics and data uncertainty due
to the increasing size of datasets and real-time necessity of processing [354];
security and privacy issues and the balance between easier data processing and
data access control for big data analytics, etc.
More insight into potential applications of big data-oriented tools and analyt-
ical technologies in the energy domain are given in Chap.10.
Energy Consumption and Home Automation
An unavoidable topic when discussing big data applications, in general, is home
automation. One of the challenges that the world is facing nowadays is reducing
our energy consumption and improving energy efficiency. The Internet of Things,
as a network of modern sensing equipment, plays a crucial role in home automation
solutions that based on this data are capable of processing and providing accu-
rate predictions, and energy saving recommendations. Home automation solutions
provide optimal device scheduling to maximize comfort and minimize costs, and
can even be extended from the operation aspect to planning and offering possible
home adjustments or suggesting investments in renewable sources if the location
being considered is deemed fit. Having smart appliances initially presented the
concept of human-to-machine communication but, governed by big data process-
ing, this concept has been further popularized with machine-to-machine commu-
nication where the human input is removed, resulting in less interference. Predic-
tive maintenance and automatic fault detection can also be obtained from sen-
sor data for both basic household appliances and larger mechanical systems like
cars, motors, generators, etc. IoT applications require proper cloud frameworks
[456]. Ge [151] presents a comprehensive survey of big data applications in the IoT
sphere, Martis [300] introduce machine learning to the mix. Kumari [270] gives a
survey but with the main focus on multimedia, and Kobusi´nska [248] talks about
current trends and issues.
Banking and Insurance
Business intelligence tools have been used to drive profitability, reduce risk, and
create competitive advantage since the 1990s. In the late 1990s, many banks and
insurance companies started using machine learning techniques for categorizing
and prioritizing clients, assessing the credit risk of individual clients or companies,
158 V. Janev et al.
and survival analysis, etc. As this industry generally adopts new technologies early
on, thanks to advances in cognitive computing and artificial intelligence, compa-
nies can now use sophisticated algorithms to gain insights into consumer behav-
ior. Performing inference on integrated data from internal and external sources is
nowadays the key for detecting fraud and security vulnerabilities. Furthermore,
novel approaches state that the applied machine learning can be supplemented
with semantic knowledge, thus improving the requested predictions and classifica-
tions and enriching them with reasoning explanations that pure machine learning
based deduction lacks [40]. Regarding other financial institutions, stock markets,
for instance, are also a considerable use case for big data as the sheer volume and
frequency of transactions slowly renders traditional processing solutions and com-
putation methods obsolete. Finding patterns and surveilling this fast-paced pro-
cess is key for proper optimization and scam prevention. Hasan [186] and Huang
[204] offer concrete approaches like predicting market conditions by deep learn-
ing and applying market profile theory with Tian [427] discussing latency critical
applications, Begenau [36] looking at the link between Big Data and corporate
growth, and ( ´
Oskarsd´ottir [492] placing an emphasis on data collected from social
networks and mobile phones.
Social Networks and e-Commerce
Social Networks. When considering big data applications, one cannot overlook
the massive impact that the development of social networks like YouTube, Face-
book and Twitter has had on digital media and e-commerce. Social networks pro-
vide a source of personalized big data suitable for data mining with several hun-
dreds of thousands of new posts being published every minute. They are also excel-
lent platforms for implementing big data solutions whether it be for advertising,
search suggestions, post querying or connection recommendations. The social net-
work structure has also motivated researchers to pursue alike architectures in the
big data domain. From the related literature, Saleh [381] addresses challenges in
social networks that can be solved with big data, Persico [352] gives a performance
evaluation of Lambda and Kappa architectures, and Ghani [152] classifies analyt-
ics solutions in the big data social media domain.
e-Commerce. With all services available to web users, the wide variety of online
shopping websites also presents a continuous source of huge volumes of data that
can be stored, processed, analysed and inferred to create recommendation engines
with predictive analytics. As a means to increase user engagement, multi-channel
and cross-channel marketing and analysis are performed to optimize product pres-
ence in the media fed to the user. It is no accident that a certain advertisement
starts to show right after a user has searched for that specific product category.
Examining user behaviour patterns and tendencies allows for offer categorization
in the best possible way so that the right offer is presented precisely when it needs
to be, thus maximizing sale conversions. Data received from big data analysis can
also be used to govern product campaigns and loyalty programs. However, con-
tent recommendations (inferred from big data sources) in this domain are not only
related to marketing and sales but are also used for proper display of information
Chapter 9 Survey on Big Data Applications 159
relating to the user. Some search engines companies have even publicly stated that
their infrastructure relies on big data architecture, which is not surprising consid-
ering the amount of data that needs to be processed.
Environment Monitoring
Environmental monitoring involves the collection of one or more measurements
that are used to assess the status of an environment. Advances in remote sensing
using satellite and radar technologies have created new possibilities in oceanogra-
phy, meteorology, forestry, agriculture and construction (urban planning). Envi-
ronmental remote sensing can be subdivided into three major categories based on
the distance between the sensor and the area being monitored [139]. The first cat-
egory, satellite-based measurement systems, is primarily employed to study the
Earth and its changing environment. The most valuable source of data from this
category is the Landsat, a joint satellite program of the USGS and NASA, that
has been observing the Earth continuously from 1972 through to the present day.
More than 8 million images [207] are available via the NASA website8and Google
Earth Engine Data Catalog 9. Additionally, the Earth observation mission from
the EU Copernicus Programme produces 12 terabytes of daily observations (opti-
cal imagery at high spatial resolution over land and coastal waters) each day that
can be freely accessed and analysed with DIAS, or Data and Information Access
The second major category of remote sensing encompasses aircraft-borne
instruments, for instance, the light detection and ranging (LIDAR) systems that
permit better monitoring of important atmospheric species such as ozone, carbon
monoxide, water vapor, hydrocarbons, and nitrous oxide as well as meteorological
parameters such as atmospheric density, pressure, and temperature [139].
Ground-based instruments (e.g. aerosols measurement instruments) and
Wireless Sensor Networks (WSN) [397] are the third major category for outdoor
monitoring technologies that create new opportunities to monitor farms and rain
forests, cattle, agricultural (soil moisture), water quality, volcanic eruptions and
earth-quakes, etc.
The table below points to some social-economic and natural environment
applications enabled by big data, IoT and remote sensing (Table 4).
Natural Disasters, Safety and Security
The application of big data analytics techniques is specially important for the
Safety and Security industry as it can extract hidden value (e.g. early warning,
triggers, predictions) from security-related data, derive actionable intelligence,
and propose new forms of surveillance and prevention. Additionally, the number of
connected devices is expected to rapidly increase in the coming years with the use
of AI-defined 5G networks [477]. Natural Disasters. Due to changing climatic
160 V. Janev et al.
Table 4. Environment monitoring applications (examples)
Big data research in Smart Farming is still in an early development
stage. Challenges foreseen are related both to technical and
organizational issues. Technical challenges include the automation of
the data acquisition process, the availability and quality of the data,
and the semantic integration of these data from a diversity of sources
(information on planting, spraying, materials, yields, in-season
imagery, soil types, weather, and other practices). Although, from a
business perspective, farmers are seeking ways to improve profitability
and efficiency, there are challenges related to the governance (incl.
data ownership, privacy, security) and business models for integration
of the farms in the entire food supply chain [469]
The contribution of the world’s rainforests to the reduction of the
impact of climate change is well-known to environment scientists,
therefore projects have been started to integrate various low-cost
sensors for measuring parameters such as humidity, temperature, total
solar radiation (TSR), and photosynthetically active radiation (PAR)
- Machine learning and statistical algorithms have proved to be useful
for the prediction of several numeric target attributes simultaneously,
for instance, to help natural resource managers to assess vegetation
condition and plan biodiversity conservation [249]
conditions, natural disasters such as floods, landslides, droughts, earthquakes are
nowadays becoming common events. These events create a substantial volume of
data that needs to be processed in real time and thus avoid, for instance, suffering
and/or death of the people affected. Advancements in the field of IoT, machine
learning, big data, remote sensing, mobile applications can improve the effective-
ness of disaster management strategies and facilitate implementation of evacua-
tion processes. The requirements faced by ICT developers are similar to those in
the other domains already discussed
the need to integrate multimodal data (images, audio, text from social sites
such as Twitter and Facebook);
the need to syncronize the activities of many stakeholders involved in four
aspects of emergency (preparedness, response, mitigation and recovery);
the need to install measuring devices for collecting and real-time analysis in
order to understand changes (e.g. in water level, ocean waves, ground motions,
the need to visualize information;
the need to communicate with people (first responders and/or affected people
and track their responses and behaviour) or to alert officials to initiate rescue
The global market offers a wide range of emergency solutions (in the form
of web and/or mobile solutions) with intuitive mapping, live field monitor-
Chapter 9 Survey on Big Data Applications 161
ing and multimedia data sharing, such as CommandWear11 , TRACmate12,and
Trac k2413. However, the Linked Data principles and data management techniques
discussed in the previous chapters can, to a considerable extend, facilitate inte-
gration and monitoring; see for instance the Intelligent fire risk monitor based on
Linked Open Data [442].
Safety and Security of Critical Infrastructures. Big data processing is espe-
cially important for protecting critical infrastructures like airports, railway/metro
systems, and power grids. Large infrastructures are difficult to monitor due to
their complex layout and the variety of entities that they may contain such as
rooms and halls of different sizes, restricted areas, shops, etc. In emergency situa-
tions, various control and monitoring systems, e.g. fire protection systems, heat-
ing, ventilation and air conditioning systems, evacuation and access control sys-
tems and flight information display systems among others, can send altogether
thousands of events to the control room each second [309]. By streaming these low-
level events and combining them in a meaningful way, increased situation aware-
ness can be achieved. Using big data tools, stream processing solutions, complex
event processing/event-condition-action (CEP/ECA) paradigm and combining
events, state and emergency management procedures, a wide range of emergency
scenarios and emergency procedures can be pre-defined. Besides processing the
large amount of heterogeneous data extracted from multiple sources while con-
sidering the challenges of volume, velocity and variety, what is also challenging
today is
real-time visualization and subsequent interaction with computational mod-
ules in order to improve understanding and speed-up decision making;
development of advanced semantic analytics and Machine Learning techniques
for new pattern recognition that will build upon pre-defined emergency scenar-
ios (e.g. based on rules) and generate new early warning procedures or reliable
action plans.
Following the already mentioned impact of using smart mobile phones as data
sources, the telecommunications industry must also be considered when discussing
big data. The 5th generation of cellular network (5G) that is now live in 24 markets
(GSMA predicts that it will account for 20% of global connections by 2025) will
provide real-time data collection and analysis and open possibilities for business
intelligence and artificial intelligence-based systems.
Mobile, television and internet service providers have customer retention as
their core interest in order to maintain a sustainable business. Therefore, in order
to prevent customer churn, behaviour patterns are analysed in order to provide
predictions on customers looking to switch their provider and allow the com-
pany to act in time and offer various incentives or contract benefits in due course.
162 V. Janev et al.
Also, besides this business aspect, telecommunication companies using big data
analytic solutions on data collected from mobile users can use the information gen-
erated in this way to assess problems with their network and perform optimiza-
tions, thus improving the quality of their service. Since almost all modern mobile
phones rely on wireless 4G (and 5G in the years to come) networks to communicate
when their users are not at home or work, all communication is passed through
the data provider’s services, and in processing this data still lie many useful bits of
information as only time will tell what useful applications are yet to be discovered.
Papers covering this aspect include Yazti [479] and He [191] outlining mobile big
data analytics, while Amin [15] talks about preventing and predicting the men-
tioned phenomena of customer churn, and Liu [286] talks about collecting data
from mobile (phone and wearable) devices.
Industry 4.0 is about automating processes, improving the efficiency of processes,
and introducing edge computing in a distributed and intelligent manner. As dis-
cussed previously, more complex requirements are imposed in process operations
while the process frequently forfeits robustness, complicating process optimiza-
tion. In the Industry 4.0 era, smart manufacturing services have to operate over
multiple data streams, which are usually generated by distributed sensors in
almost real-time. Similarly to other industrial sectors, transforming plants into
full digital production sites requires an efficient and flexible infrastructure for data
integration and management connected to powerful computational systems and
cognitive reasoning engines. Edge computing (distributing computing, storage,
communication and control as close as possible to the mediators and objects at the
edge) plays an important role in smart manufacturing. Data has to be transferred,
stored, processed and transferred again back (bidirectional communications from
machine to machine, machine to cloud and machine to gateway) to both users
and providers in order to transmit the inferred knowledge from sensor data. In
the layered infrastructure (see Fig.2), cognitive services have a central role and
their design (selection of algorithms/models) depends on the problem in place,
for instance
Kumar [268] proposes using the MapReduce framework for automatic pat-
tern recognition based on fault diagnosis in cloud-based manufacturing. Fault
diagnosis significantly contributes to reduce product testing cost and enhances
manufacturing quality;
Vater [443] discusses how new technologies, such as IoT, big data, data analyt-
ics and cloud computing, are changing production into the next generation of
In the smart manufacturing ecosystem, cognitive applications make use of pro-
cess data (processed on the edge) and provide high level supervisory control and
support the process operators and engineers. Data analytics and AI techniques
are combined with digital twins and real-life feedback from the shop floor or pro-
duction facility to improve the quality of products and processes. Example areas
where semantic processing and artificial intelligence can advance this sector are
Chapter 9 Survey on Big Data Applications 163
Fig. 2. Multi-layered software architecture
Human-Computer Interaction. In complex situations, operators and
machines need to quickly analyze situations, communicate and cooperate with
each other, coordinate emergency response efforts, and find reasonable solu-
tions for emerging problems. In such situations, collaborative intelligence ser-
vices are needed that require fewer human-driven decisions as well as easy-to-
use interfaces that accelerate information-seeking and human response. Inter-
pretability and explainability are crucial for achieving fair, accountable and
transparent (FAT) machine learning, complying with the needs and standards
of the business sector.
Dynamic process adaptation. Many industrial processes are hard to adapt
to changes (e.g. related to status and availability of all relevant production
resources, or in case of anomaly detection). This affects product quality and can
cause damage to equipment and production lines. Hence, a semantic framework
for storing contextual information and an explainable AI approach can be used
for fine-tuning of process parameters to optimize environmental resources, fast
reconfiguration of machines to adapt to production change, or advance fault
diagnosis and recovery.
4 Conclusions
This chapter presented applications of big data approaches in different sectors.
Research into real-time data analytics by addressing the volume and velocity
dimension of big data is a significant area in emerging smart grid technology, for
164 V. Janev et al.
instance, where different predictive models and optimization algorithms serve to
improve end-to-end performance, end-user energy efficiency and allow increasing
amounts of renewable energy sources to be embedded within the distribution net-
works (e.g. solar photovoltaic (PV), wind power plants). Next, analytics on real-
time data streams combined with GIS and weather data improves detection of
significant events, enhances situational awareness and helps identify hazardous
road conditions (e.g. snow), which may assist drivers and emergency responders
in avoiding such conditions and allow for faster emergency vehicle routing and
improved response time. Solutions that address the variety dimension, integra-
tion of heterogeneous data sources (including open and social media data) and
advanced machine learning algorithms have found application in customer rela-
tion management and fraud detection (finance, insurance, telecommunication).
For instance, the ability to cross-relate private information on consumer prefer-
ences and products with information from Facebook, tweets, blogs, product eval-
uations, and other sources opens a wide range of possibilities for organisations to
understand the needs of their customers, predict their needs and demands, and
optimise their use of resources. This chapter also discussed challenges that can be
addressed and overcome using the semantic processing approaches and knowledge
reasoning approaches discussed in this book.
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