Business Intelligence: Data Mining, IoT, and EmIoT

This article discusses the concepts of data mining, IoT, and EmIoT in business intelligence. It explores their applications, advantages, and disadvantages. The article covers topics such as data mining techniques, IoT analytics, and EmIoT devices. It also highlights the potential risks and ethical concerns associated with these technologies.

Running head: BUSINESS INTELLIGENCE
Business Intelligence
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Table of Contents
1. Mining....................................................................................................................................2
Data Mining...........................................................................................................................2
Text Mining............................................................................................................................2
Web Mining...........................................................................................................................3
Social Mining.........................................................................................................................3
2. Internet of Things (IoT).........................................................................................................4
3. EmIoT and IoT.......................................................................................................................6

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1. Mining
Data Mining
The process of identifying patterns from large sets of data is commonly known as data
mining. Data mining can be conducted with all methods at the interaction of database
systems, machine learning and statistics (Witten, 2016). Some of the most notable uses of
data mining techniques are as follows:
 Business-market analysis: This is widely used in the e-commerce applications to
analyse customer purchase trends (Lee & Lee, 2016).
 Bio Informatics: The mining of biological data helps to extract crucial knowledge in
the field of biology, which can be applied in the fields of neuroscience, genetic
research and medicine.
 Customer-Relationship Management: CRM uses data mining facilities to collect and
analyse customer information. Data mining helps to analyse the gathered information
and concentrate on the appropriate factors that will help the concerned organization to
retain customers.
Text Mining
This is the process where massive chunks of unstructured text data is is explored and
analysed. Software are used to identify recognizable topics, patterns and concepts or
attributes in the respective collectives. This is commonly used in the following fields:
 Customer Care service: Text analysis techniques in text mining helps in the rapid and
automated response generation to customers, by gathering and reading through the
patterns of past customer-operator text chat responses. Automated text-message-

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support curbs off the burden from the call-center operators, in providing hassle-free
solutions to the client.
 Spam Filtering: Spamming has been quite a crucial issue in the modern days. Text
mining methods are applied to improve the effectiveness of various types of
statistical-based email or message filtering methods. This technique is widely used by
of the greatest names in the business like Google, Facebook and so on (Aggarwal &
Zhai, 2012).
Web Mining
Web mining allows the pattern recognition techniques with the help of content and
structure mining abilities. This data mining application aids in the process of discovering
necessary data patterns from the WWW or World Wide Web. Some of its renowned use cases
are as follows:
 Search Engine analytics: Google Analytics is one of the most important examples of
the usage of the web mining technology. It uses this technology to gather visitor
response information of various websites and provides analytical reports. This also
allow them to meaningfully extract the best outputs on search queries.
 Advertising performance analysis: Web mining allows to collect data about the user
or web-visitor interaction on the ads that are displayed on various websites. This
method helps to create meaningful statistical reports on the reach and popularity of
such e-adverts.
Social Mining
Social mining or social media mining is one of the most common mining trends of the
modern era. The process of analysing, representing, and retrieving meaningful patterns from
data that is collected from social media that is a product of several user interactions of posts

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and information of the respective platforms. This involves the collection and analysis of data
collected from the perspective of user’s emotional attachment. This is in turn used in the
process of content management. Highlighting likewise content and adverts is an important
use case of this process.
2. Internet of Things (IoT)
Internet of Technology, which is most commonly known as IoT can be defined as a
network of several devices or appliances that are connected to a control system. This allows
them to be remotely operated or work on their own, based on AI or Artificial Intelligence
aids. These IoT devices can be so operated to work on their own, interact with each other and
exchange data as necessary. Some of the major analytics techniques that are applied in IoT
are as follows:
 Application Analytics: This helps to gain fair knowledge about the usage of the IoT
devices based on the data collected from the applications that are used by the users to
operate these devices.
 Social Analytics: This helps to gather data about the user interaction with the various
IoT devices. Artificial Intelligence means are availed to make the devices to work
based on the data analysed. This helps to make the devices work according user’s
preferences.
 Real-Time visualization analytics: Data collected is often required to be analysed and
prompt solutions are to be delivered. Such types of analytic techniques help in this
process. Smart cars are the best example in this field. They collect data based on
sensor inputs and the device needs to make accurate decisions based on the real time
updates in the data or environment.

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 Data mining: Big data helps to collect and focus on the most important portion of data
that are collected. Data analytic techniques allow these devices to formulate
appropriate reports for solutions to long-term or real-time problems.
The advantages of IoT are as follows:
 It is highly cost and effort effective. The transferring of data from one device to
another is easy. They are internally shared over the networks, hence saving up lots
of charges and time of the user.
 Ease of information access is another crucial addition to the list of advantages that
comes with the implementation of IoT devices. Also, massive chunks of data can
be collected and retrieved as and when necessary. This helps in the proper
management of these devices (Formisano et al., 2015).
 Automation is the key aspect. The reduction of human interaction or involvement
in data collection techniques is another positive point that can be noted in the
favour of IoT. This allows the devices to collect and produce flawless data
outputs.
There arises various complexities with IoT. Some commonly mentioned
disadvantages are as follows:
 Privacy and security is the most effective concern in the usage of IoT. These being
few of the major devices in a household, if compromised, will lead to a massive
breakout. This leads to the leakage of crucial and sensitive information. In addition,
the control of these devices into the hands of anti-socials will lead to a vital unsecured
situation (Zhang et al., 2014).

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 With the increase of automation, there will be less works for humans to do. All crucial
data collection and or analysis can be done by these devices and human value will
decrease.
 Incorrect ethical analysis is a huge drawback in this technology. However smart a
device can be designed to get, they can never match the intuition level of human
beings. Hence, in crucial ethical situations, the smart device may fail to make a proper
decision. An example of this can be the ethical consideration of a smart car. In case
the car is faced with a situation where it has a pedestrian appearing suddenly in front
of it and it cannot turn as it will else hit a tree, hence harm its own passengers. The car
faces an ethical dilemma and will generally jeopardise the life of its passengers to
save the pedestrians (He, Yan & Da Xu, 2014). A human driver could have handled
such situations in a more matured way.
3. EmIoT and IoT
EmIoT or Emotional Internet of Things is the technology that allows the IoT devices
to interact on a greater aspect with the users’ emotional factors to create a better data set for
analytics. The data that are collected from this technology helps in the emotional analysis
process. It records data that are related to the deepest personal factors of the inheritants. This
includes data like how a user reacts to certain situations or how the user interacts with the IoT
devices. They gather information that consist of user’s voice pitch level or sensor data about
the user’s catabolism, depending on various familiar situations that the device can simulate or
note. IoT on the other hand, is designed to only collect, share and analyse the data that they
are meant to handle. It is the EmIoT that aids the integrated IoT devices with data feedbacks.
Important examples of EmIoT devices are the EmoSPARK and the Feel Wristband.
The EmoSPARK is a smart home device that creates an emotional profile of the inhabitants
based on their vocal characteristics, choice of words and also facial recognition. This device

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can alter the IoT devices’’ operation based on the real-time collected emotional data. This
includes changing the music or video playlist based on the user’s mood or general daily
habits.
The Feel Wristband uses sensors to read pulse, body temperature and skin responses.
These are useful in health assistance purposes (Mano et al., 2016). The collection of these
data helps the device to create a real-time emotional profile of the user and hence instruct the
connected IoT devices to operate properly. For example, if the band reads that the user has a
hot skin temperature and a pounding heart, it may advice the mobile application that it is
connected to, to call the doctor or it may even bring down the temperature of the AC if
connected (Kelly, Suryadevara & Mukhopadhyay, 2013).
EmIoT is developed and designed to frame the feelings of the users in a special
manner so as to add a deeper layer understanding of the data that they gather. While, IoT
devices only face the threat of security and excess human dependence, EmIoT creates a fine
line of competition between humans and the robots. Cui (2016), says that robots are getting
smarter with time and a time will come when these will take over almost every network
accessible device usage duties from the control human beings.

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