Data Mining Based on Intelligent Systems for Socially Aware Computing

This report delves into the application of data mining within the context of socially aware computing, emphasizing the extraction of valuable information from large datasets. It begins with an introduction to data mining and its role in knowledge discovery, followed by an examination of intelligent data mining techniques. The paper then defines socially aware computing, exploring how mobile platforms and social networking have transformed human interaction. It details various data mining techniques used in socially aware computing, including regression, classification, and clustering, and discusses the importance of data warehousing. The report highlights the use of data mining in understanding and improving socially aware computing, outlining a six-step data mining process. Furthermore, it explores the benefits, such as automated decision-making, accurate forecasting, and customer insights, as well as the challenges, including overfitting models, privacy, and security. The conclusion underscores the importance of data mining in the face of big data and emphasizes its role in supporting human behavior and social interaction.

Running head: Data Mining Based on Intelligent Systems for Socially Aware Computing
Data Mining Based on Intelligent Systems for Socially Aware Computing
Name of the Student
Name of the University
Author Note

Data Mining Based on Intelligent Systems for Socially Aware Computing 1
Table of Contents
Introduction:...............................................................................................................................2
Intelligent data mining:..............................................................................................................2
Socially Aware Computing:.......................................................................................................3
Data mining techniques for socially aware computing:.............................................................4
Use of data mining in socially aware computing:......................................................................5
The benefits of data mining in socially aware computing:........................................................6
The challenges of data mining for socially aware computing:..................................................7
Conclusion:................................................................................................................................7
References:.................................................................................................................................8

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Introduction:
In the present information driven economies, the organisations may get huge benefit
from the suitable management of information. Though, the information management is not
only a technology based concept that is rather a business related practice naturally. The
purpose of this paper is to discuss about Data Mining Based on Intelligent Systems for
Socially Aware Computing. After analysing all the parts there will be a conclusion at the end
of the paper. The indispensable and even possible support of the IT related tools that are
existed in the context. The data mining is known also as Knowledge Discovery in the
Databases [1]. According to W. Frawley, G. Piatetsky-Shapiro and C. Matheus data mining
can be defined as “The nontrivial extraction of implicit, previously unknown, and potentially
useful information from data.” Statistical and visualization techniques and Machine learning
is used by it for discovering as well as presenting the knowledge in a form which is
comprehensible to the humans easily.
Intelligent data mining:
The primary objective of the data mining is to extract the important most information,
hidden or visible from the set of data which is for making decisions or for better
understanding the materials that are under construction. The information that are hidden are
indicating the weak signals which is having the ability to escape the traditional statistical
analysis. The data mining is basically a procedure that is used by the organisations for turning
the actual data in to useful information. The data mining is involving analysing and exploring
the huge blocks of information for gleaning the patterns and trends that are meaningful [2].
The data mining can be utilized in a variety of ways like as credit risk management database
marketing, spam mail filtering, fraud detection or even for discerning the opinion or
sentiment one f the user.

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Socially Aware Computing:
At present there are private and public initiatives in the country for providing
computer access universally. However most of the people still cannot make sense of the
possibilities that are brought by internet and computer. The computing devices and mobile
platforms like tablets, smartphones, and notebooks are very much essential and popular for
the interactions between the human [3]. At present the students are using the era of new
computing where the social networking and mobile computing have combined in to the social
networking that is a mean for the people for socializing as well as connecting directly by the
mobile phones of them. The students can communicate their thoughts as well as they can
share them with some of the others by using social networking sites and blogs. The programs
that are related to data mining can analyze the patterns as well as they can analyze the
relationships in the data on the basis of request of the users. As example, it can be said that an
organisation can use the data mining software for creating classes of information. The
warehouse is one of the important most aspect of the data mining. The warehousing is
basically when the organisations will centralize the data and information of them into
program or database. With the help of data warehouse, the organisation can spin off the
segments of information and data for some of the specific clients for using and analysing.

4Data Mining Based on Intelligent Systems for Socially Aware Computing
(Figure: Socially aware computing)
The social awareness and system can help the people for understanding the present
situation, improvement in the skills on the social communication as well as facilitate the
social interaction that is efficient. The socially aware computing can emphasize support for
human behaviours and intelligence assistance and social interaction from the society and
individual perspectives respectively [4]. The socially aware computing is basically oriented to
the dynamic, continuous, leverage scale as well as real time sensory data for recognising
behaviours of individual as well as for supporting human collaboration and communication.
The huge number of different types of sensing devices like ubiquitous sensors (RFID, motion
sensors, microphones, cameras, etc.) combined with web and email (social network sites,
blogs and Wiki) provide a lot of information and data to analyse human interaction and

5Data Mining Based on Intelligent Systems for Socially Aware Computing
behaviour. The mobile social networking has been becoming one of the new domain of
research for showing the power of merging mobile computing and social networking.
Data mining techniques for socially aware computing:
The extraction of the patterns that are hidden, by getting help from various methods of
data mining, might be classified in to two of the types, prediction methods and description
methods [5]. The data mining methods that are used for the data analysis are as per follows:
 Regression
 Association Rule Discovery (Dependency Model)
 Classification
 Clustering
 Anomaly detection
 Summarization.
(Figure: Data mining models)
At the time of development of the mobile services and mobile devices the
consideration of the offers that are available in the market can play an important role [6]. The
mobility strategies are very much unique for the enterprises as well as cover most of the

6Data Mining Based on Intelligent Systems for Socially Aware Computing
important issues (risk and expected benefits of mobile devices usage, Bring-YouOwn-Device
(BYOD) approach implementation). Beyond that quantifying the risks and making decisions
about the devices are very much hard that is without well investigation that is for the usage of
mobile devices in the business related organisations.
The primary step of the data mining is to gather relevant data that are very much
critical for the business. The company data can be either metadata or non operational or they
can be even transactional. The transactional data is able to deal with the day to day operations
such as inventory, sales as well as cost etc [7]. The data of socio computing is non
operational, normally the data is forecast, but the metadata is concerned with the design of
database that is logical. The relationships and patterns that are existed among the elements
are having the ability to render the information that are relevant, which are having the ability
to increase the revenue of the organisations. The organisations that are having a strong
customer focus, can deal with the techniques that are related to the data mining can provide
clear photos of the products that are sold, price, and competition and customer demographics.
The next step of data mining is to select an algorithm that is suitable, a mechanism to produce
a model related to data mining. The natural working of the algorithm are involving to identify
the trends in the set of data as well as utilizing the output for the definition of the parameter.
The popular most algorithms that can be used in this scenario are regression algorithms and
classification algorithms.
Use of data mining in socially aware computing:
The data mining can be used for better understanding for the socially aware
computing. The accepted procedure for the data mining are having 6 steps. The steps are as
follows:

7Data Mining Based on Intelligent Systems for Socially Aware Computing
Business understanding: The initial step is to establish the goals for the project as well as to
establish the process for getting help from data mining to reach the goal [8]. The plan need to
be developed for including actions, timelines as well as the role assignments.
Data understanding: The data can be collected from all of the data sources that are
applicable in this step. The tools that are for data visualization are often utilized in this stage
for exploring the properties of the data for achieving the business related goals.
Data preparation: Then the data is cleansed as well as the missing data is including for
ensuring that the data is ready for being mined. The processing of the data is able to take
enormous amounts of time that depends on the amount of information and data that are
needed to be analysed [9]. The distributed frameworks are utilized in the modern DBMS
(database management systems) for improving the speed of data mining procedure rather than
burdening a single system.
(Figure: Socially aware computing approach)
Data modelling: The mathematical related models are used then for finding the patterns that
are existed in the data utilizing the data tools that are sophisticated.

8Data Mining Based on Intelligent Systems for Socially Aware Computing
Evaluation: The findings are compared as well as evaluated to the objectives of business for
determining if they would be deployed in the organisation.
Deployment: This is the final stage. In this stage the findings of data mining are shared across
the operations of everyday business. The platform of an enterprise business can be performed
for providing the single source for the recovery of data.
The benefits of data mining in socially aware computing:
Automated decision making: The data mining can gives allowance to the organisations for
analysing the data continuously as well as automating both the critical decisions and routine
without any delay for human judgment [10]. The banks will be able to detect the fraudulent
transactions instantly, requesting verification as well as for securing the personal information
for protecting the consumers against identifying the thefts.
(Figure: Data mining)

9Data Mining Based on Intelligent Systems for Socially Aware Computing
Accurate forecasting and prediction: The planning is one of the critical most process that are
existed within the organisations. The data mining can facilitate planning as well as it can
provide reliable forecasts to the managers on the basis of past trends as well as on the basis of
present conditions.
Customer insights: The organisations can deploy models related to the data mining for
uncovering the primary characteristics as well as the differences within between the
customers. The data mining might be used for creating personas as well as for personalizing
each of the touch point for improving the experience of the customers.
The challenges of data mining for socially aware computing:
The challenges that occur in this scenario are as follows:
Over fitting models: The over fitting occurs when the model explains the errors that are
natural within the sample instead of underlying the trends of the population. The over fitted
models are very complex as well as use an excess of the variables that are independent for
generating the prediction. The risk of over fitting the is being heighted with the increase in
the variety and volume of data. the challenges is for moderating the number of variables that
are utilized in the models of data mining as well as for balancing the predictive power that
too with accuracy.

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(Figure: Challenges of Data mining)
Privacy and security: The increased requirement of the storage for data has forced many of
the organisations for turning toward the storage and cloud computing. The cloud has
empowered most of the modern advances that are included in the data mining. The nature of
the services that can create security threats and significant privacy.
Conclusion:
Thus, it can be concluded that the data mining is hindered by the complexity of big
data and increasing of big data. The Exabytes of the data that are collected by the
organisations every day, the decision makers need the ways for extracting, analysing as well
as for gaining the insight from the abundant repository of data. The socially aware computing
can emphasize support for human behaviours and intelligence assistance and social
interaction from the society and individual perspectives respectively.

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