Data Mining Techniques for Socially Aware Computing Systems Analysis

This report provides an overview of data mining based on intelligent systems for socially aware computing. It begins with an introduction to data mining, emphasizing its role in identifying patterns and anomalies within large datasets, and the integration of intelligent systems, including artificial intelligence and computational intelligence. The report then delves into socially aware computing, which combines various technologies to create integrated experiences. It explores intelligent data mining, the methods and techniques employed, and the applications within socially aware computing. The report also outlines the steps of the data mining process, benefits such as automated decision-making and accurate forecasting, and challenges including overfitting and privacy concerns. The conclusion highlights the importance of data mining in extracting relevant information to increase organizational revenue. The report includes several references to support the findings.

D ATA M I N I N G B A S E D O N
I N T E L L I G E N T S Y S T E M S
F O R S O C I A L LY A W A R E
C O M P U T I N G
N A M E O F T H E S T U D E N T
N A M E O F T H E U N I V E R S I T Y

INTRODUCTION:
• The data mining is basically a procedure that is used
for finding anomalies, correlation and patterns within
a huge data sets for predicting the outcomes. The
integration of the intelligent system basically
includes the applications that are related to the
intelligent technology like computational intelligence
method and artificial intelligence that can be utilized
in different levels of the system. The socially aware
computing is the combination of distributed content
sharing, sensor networks, social networks, ambient
technologies and pervasive computing. It is used for
providing an integrated experience.

INTELLIGENT DATA MINING:
• From the last decade, the
advances in speed and
processing power have
enabled the people for
moving tedious, manual as
well as practices that are
time consuming for easy,
quick and automated data
analysis [1]. The intelligent
data mining is a cornerstone
for the data analysis as well
as it can help people for
developing the models which

SOCIALLY AWARE
COMPUTING:
The social awareness and system can help the people
for understanding the present situation, improvement is
the social communication skills as well as facilitating
the social interactions that are efficient. The socially
aware computing can emphasize the intelligent
assistance as well as it is able to support the social
interaction and human behavior from the society and
individual perspectives respectively. The socially aware
computing is actually oriented to the continuous,
dynamic, real tome sensory and large scale data for
recognizing the individual behaviors, supporting human
communication, discovering group interaction patterns

THE CONCEPT OF SOCIALLY AWARE
COMPUTING:

DATA MINING TECHNIQUES
FOR SOCIALLY AWARE
COMPUTING:• The methods of data mining which are used for data
analysis are as per following:
Regression
Association Rule Discovery (Dependency Model)
Classification
Clustering [2]
Anomaly detection
Summarization.

DATA MINING MODELS:

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:
Business understanding
Data understanding
Data preparation
Data modelling [3]
Evaluation
Deployment

THE BENEFITS OF DATA
MINING IN SOCIALLY AWARE
COMPUTING:• The data mining is a procedure
which is utilized by an
organisation for turning the raw
data into useful data. The
benefits of data mining are as
follows:
Automated decision making
Accurate forecasting and prediction
Customer insights

THE CHALLENGES OF DATA
MINING FOR SOCIALLY
AWARE COMPUTING:• The challenges of data mining for socially aware computing:
• 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 [4].
• 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.

CONCLUSION:
• Thus, it can be concluded that the data mining is hindered
by the complexity of big data and increasing of big data.
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.

REFERENCES:
• [1] Moro, S., Rita, P. and Vala, B., “Predicting social media performance metrics and
evaluation of the impact on brand building: A data mining approach.” Journal of Business
Research, 69(9), pp.3341-3351, 2016.
• [2] S. Salloum, M. Al-Emran, A. Monem and K. Shaalan, "A Survey of Text Mining in Social
Media: Facebook and Twitter Perspectives", Advances in Science, Technology and Engineering
Systems Journal, vol. 2, no. 1, pp. 127-133, 2017. Available: 10.25046/aj020115.
• [3] Injadat, M., Salo, F. and Nassif, A.B., “Data mining techniques in social media: A
survey.” Neurocomputing, 214, pp.654-670, 2016.
• [4] Liu, L., Chang, Z. and Guo, X., “Socially aware dynamic computation offloading
scheme for fog computing system with energy harvesting devices.” IEEE Internet of Things
Journal, 5(3), pp.1869-1879, 2018.