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tools, knowledge engineering techniques, high-performance multi-
core processors, gigabit Ethernet solutions, and inexpensive storage
options including object storage to extract and extrapolate knowledge.
In summary, as the size of edge data is growing significantly,
there is a bigger challenge to information management professionals
to evolve a pragmatic strategy for effectively leveraging all sorts of
edge data for the well-being of their organizations. With the faster
maturity and stability of data analytics platforms, knowledge-enabled
everyday systems are bound to flourish.
4.6.1 Edge Data for Smarter Cities
According to Intel, the smart city concept mandates the use of smart-
grid infrastructures to improve environmental sustainability, manage
energy consumption, better coordinate public resources, and protect
the quality of life for urban and metropolitan residents and plan for
sustainable growth. e edge data here play a very incredible role.
For example, utility companies and governments are using data from
the smart grid to understand the complex relationships among gen-
eration, transmission, distribution, and consumption with the goal of
delivering reliable energy and reducing operating costs. Consumers
are also empowered with insights from the smart grid to better man-
age their personal energy requirements. For example, a “not-home”
setting might turn off lights, shut down unused equipment, and adjust
home temperature. Utility meter readings and grid data are brought
into centralized analytical systems to bring forth timely insights.
us edge devices collaboratively contribute immensely to arriv-
ing at better decisions than only at a centralized control center.
Communication between devices helps determine when, where,
and how much energy should be produced and consumers can use
home management tools to monitor and adjust energy consumption
accordingly.
4.6.2 Edge Data for Smart Retailers
It is well known that supermarkets and hypermarkets across the globe
duly collect a great deal of data every day. If the data are properly col-
lected, cleansed, and categorized, retailers can substantially enhance
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their influence on customers and their buying patterns. is incred-
ible knowledge of customers prepares retail stores to think big and to
bring forth scores of premium services in time to retain and delight
their loyal customers as well as to attract new customers. e endur-
ing challenges brought on by the hugeness in the data being captured
and processed are being tackled through the highly versatile Hadoop
framework that can totally change the retail economics by radically
lowering the cost of data storage and processing, bringing in new
flexibilities to gain new insights, automated replenishment, and more
accurately market to individuals rather than a demographic.
Retailers are using a variety of intelligent systems that gather data
and provide immediate feedback to help them to engage shoppers
fruitfully. Well-known data-generation systems include digital sign-
age, point of sale systems, vending machines, transaction, in-store
cameras, dispensing kiosks, and so on. e ability to gain reliable
insights from the data shared by these systems makes it possible to
provide customer-centric “connected stores.” Context awareness is the
main theme of these connected machines to understand the customer
situation precisely and perfectly. e context information then greatly
differentiates in showering customers with a host of unique services.
In short, the insights-driven shopping experience is enabling custom-
ers to purchase items for the best price. Based on the edge data, retail-
ers can integrate their supply chain activities intelligently. Further,
retailers can provide their customers with opportunities to engage
with their preferred brands in more meaningful ways to cement cus-
tomer loyalty.
4.6.3 Edge Data for Smart Automobiles
e number of digital electronics and other automation elements in
a vehicle is steadily on the climb for providing different kinds of ser-
vices to drivers and the occupants. e convenience, care, choice, and
comfort brought about through these connected devices are undeni-
ably awesome. Sensors are being attached in every critical component
in a car to preemptively get to know the component’s status and this
reading provides some leeway for drivers to ponder the next course of
action. Another interesting and involving module is the in-vehicle info-
tainment system, which is emerging as the core and central gateway
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for securing and strengthening the connectivity outside for a range
of use cases. All kinds of communication, computing, and entertain-
ment systems inside vehicles will have a seamless connection with the
outside world through the well-defined in-vehicle system so that the
occupants can enjoy their travels in a fruitful manner. GPS devices
and smart meters of cars generate a great deal of data to be captured
and analyzed.
Sensors provide information to automated parking systems to lessen
the driver’s workload substantially. ere are sensor-enabled driver
assistance systems for automobiles. Location data could be combined
with road work and other traffic information to help commuters
avoid congestion or take a faster route. Digital signage, cameras, and
other infrastructures on the roadsides in synchronization with the in-
vehicle infotainment module (V2I) aid drivers to give a pleasant travel
experience for all. Vehicles today talk to other vehicles (V2V) on the
road and interact with remote cloud services and applications (V2C).
Vehicles share their data to the remotely held databases to facilitate
the different aspects of vehicle analytics. Maps are the other salivating
tool for reaching out the destination in a cool and controlled manner.
Detecting real-time traffic flow from each direction and automati-
cally changing traffic signals will improve flow. Edge data further
enable automated, intelligent, and real-time decisions to optimize
travel across the transportation infrastructure as cars become capable
of connecting to the roadway, safety systems, and one another.
4.6.4 Edge Data for Smart Manufacturing
Every tangible machine and tool in manufacturing floors and produc-
tion facilities is being filled with manifold of smart sensors, communi-
cation modules, and so on. As such, today’s devices are instrumented
to interoperate and be intelligent in their operations and obligations.
Machines are networked not only with others in the neighborhood but
also with remote cloud environments. Today all the production-related
data are being shared to centralized systems in the form of Excel sheets
at the end of the day through emails. But new-generation machines
are capable of integrating with cloud-enabled software applications and
cloud storages instantly and insightfully. at is, machines transfer
all the ground-level information to the cyber-level transaction and
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analytical systems then and there. is technology-inspired real-time
connectivity facilitates a number of fresh possibilities and opportuni-
ties for corporations in visualizing hitherto unforeseen competencies.
In addition, chief executives and other decision makers, who are on
the move in a faraway land, can be provided with decision-enabling
productivity details through a real-time notification capability to ini-
tiate any course-correction if necessary, commit something solid to
their customers with full confidence and clarity, ponder new offer-
ings, bring operational efficiencies, explore newer avenues for fresh
revenues, and so on.
at is, smart factories connect the boardroom, the factory floor,
and the supply chain for higher levels of manufacturing control and
efficiency. Sensors and actuators in devices such as cameras, robotic
machines, and motion-control equipment generate and use data to
provide real-time diagnosis and predictive maintenance, increased
process visibility, and improved factory uptime and flexibility. us
edge data lay a sparkling foundation for smart manufacturing.
4.6.5 Edge Data for Facilities and Asset Management
e big data generated by increasingly instrumented, interconnected,
and intelligent facilities and assets is useful only if transactional sys-
tems could extract applicable information and act on it as needed. e
appropriate and real-time usage of this big data is to help improve
decisions or generate corrective actions that can create measurable
benefits for an organization. BDA can help generate revenue by pro-
viding contextual understanding of information that the business can
then employ to its fullest advantage. For example, geographic infor-
mation systems (GISs) can help location-sensitive organizations such
as retailers, telecommunications, and energy companies determine
the most advantageous geographies for their business operations. e
world’s largest wind energy producer has achieved success using a big
data modeling solution to harvest insights from an expanded set of
location-dependent factors including historical and actual weather
to help optimize wind turbine placement and performance. Exactly
pinpointing the optimal locations for wind turbines enables energy
producers to maximize power generation and reduce energy costs as
well as to provide its customers with greater business-case certainty,
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quicker results, and increased predictability and reliability in wind
power generation.
An effective facilities and asset management solution has to leverage
BDA to enable organizations to proactively maintain facilities equip-
ment, identify emerging problems to prevent breakdowns, lower mainte-
nance and operations costs, and extend asset life through condition-based
maintenance and automated issue notification [7]. To help mitigate risks
to facilities and assets, predictive analytics can detect even minor anoma-
lies and failure patterns to determine the assets that are at the greatest
risk of failure. Predictive maintenance analytics can access multiple data
sources in real time to predict equipment failure which helps organiza-
tions avoid costly downtime and reduce maintenance costs. Sensors could
capture the operating conditions of critical equipment such as vibrations
from ship engines and communicate the captured data in real time to
the company’s command center for commencing failure analysis and pre-
dictive maintenance. Similarly the careful analysis of environmental and
weather-pattern data in real time is another way to mitigate any kind
of visible or invisible risks. Organizations can receive alerts of potential
weather impacts in time to shut down facilities operations or pre-locate
emergency response teams to minimize business disruption in case of any
advancing storms.
Big data is admirably advantageous when applied to the management
of facilities and assets (everything from office buildings to oil-drilling
platforms to fleets of ships). is is due to the increased instrumentation
of facilities and assets, where the digital and physical worlds have syn-
chronized to generate massive volumes of data. erefore considering the
mammoth volume of data, tools-supported analysis of big data can lead to
bountiful benefits such as increased revenue, lowered operating expenses,
enhanced service availability, and reduced risk. Sathyan Munirathinam
[8] clearly describes how big data predictive analytics ensures proactive
semiconductor equipment maintenance. In a nutshell, edge data is a
ground-breaking phenomenon for all kinds of industrial sectors to zoom
ahead with all the required conviction.
4.7 Integrated BDA Platforms
Integrated platforms are essential to automate several tasks enshrined
in the data capture, analysis, and knowledge discovery processes.
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