ABSTRACT trends in drug abuse by applying powerful techniques

ABSTRACT

The research on this review
paper presents the complicated usage of prescribed drugs which perform in the
zone of data mining for organizing high volume of data and usage of complex
function for performing more refined analysis using cloud platform. The aim of
this paper is to understand the extensive and innovative frame that uses the social
media to characterize drug abuse. The
rough idea of this survey is a analytical approach to analyze social media for acquiring
the emerging trends in drug abuse by applying powerful techniques such as cloud
computing and Map Reduce model. This
paper describes how to capture important data to evaluate from networks like Twitter,
Facebook, and Instagram. Big data techniques are used to mine the useful
content for analysis.

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1. INTRODUCTION

Social
media is an internet based applications which can be used for sharing
information and creative ideas through a communication network channel.
Currently, social media is used for enumerating the information regarding
patients for understanding the symptoms of patient. Social media allows message
sharing, collecting information and deliver to the health care space. Health
care space is the one that provide the data’s of patient with their permission.
The proper way of accessing data and programs over the internet known as cloud.
It model the social medias such as facebook, twitter etc using network based
analysis method. Currently, the
scientific research often requires vast amount of estimation during simulation
and data processing. The scientific problem can be solved by automatic computational
through collection or array list which is emerged by set of sensors.
The main aim of this paper is to use the social media as an informative source
for analyzing the illicit drug activities in the society. Data mining play an
important role in all stages during the development of drug. The use of data
mining techniques during the drug development is mainly classified into two
areas:

1.
New Effect of Drug Identification: conflict reaction occurs mostly, but
sometimes new remedial effect occur and effects in some population.

2.
Suitableness in drug use.

A
crawler which basis in Map Reduce Model is performing the data mining task for the
distributed computation of data which is implemented in the framework of
Hadoop. Data processing consists of three stages, first and second stages are
collecting information from different media sources and filter it which results
in small dataset with data corresponding to solve the task. On the last stage
the small dataset which are analyzed using refined models. The main advantage
of this paper is that to provide knowledge about the drug usage for a group of
people which are observed who rarely use drugs or not addicted to drugs and
another aim is to collect the reviews of patients which cause side effects due
to the drug and can prescribe another drug 
through media.

Literature Survey

From
V. R. Nagarajan, et at1 social media provide information for the field of
health informatics which includes Bioinformatics, Image informatics, Clinical
informatics, Public health informatics etc. In this paper they use the methods
called SOMS ( an analysis to check the interrelationship between user
posts  and positive or negative comments
on drug usage) and hierarchical clustering. This paper  provide a framework which evaluate the
positive and negative symptoms of disease and also the side effects of
treatment common cancers lung cancers.

From
Jun Huan, et al2 frequent subgraph mining is an active research topic in the
data mining community. They use graph as a general model to represent the data
ad can be used in several field like bioinformatics, web indexing, etc. The
problem of frequent sub-graph mining is to find all frequent subgraphs from a
graph database. In this paper they propose a new algorithm FFSM(Fast Frequent
Subgraph Mining) for the frequent sub-graph mining problem i.e., to reduce the
number of redundant candidates proposed.

From
Mathew Herland, et al3  a bulk amount
of data is produced within health informatics and analysis of this data is done
by big data techniques and big data allows potentially unlimited possibilities
for knowledge to be gained. This information can improve health care quality
offered to patients. A several problem will arise while managing this bulk
amount of data especially how to analyze data in a reliable manner. This paper
presents big data tools and approaches for the analysis of health informatics
data gathered at multiple levels including the molecular, tissue, patient and
population levels.

From
Deepa Sharma, et al4 appearance of recent techniques for scientific knowledge
collection has resulted in large scale accumulation of information relating
various fields. Retrieval of data from huge knowledge base by typical query
ways is an inadequate form. Therefore, cluster analysis is used for analysis
and k means clustering algorithm is mostly used for data mining applications. The analysis of the cancer data set with the k mean
and then applying with the Som. This paper proposes a technique
for creating knowledge retrieval more practical and efficient using SOM with K
mean clustering technique, So as to get better clustering with reduced quality.

From
Hari Kumar and Dr. P. Uma Maheshwari 5 Big data is the term that
characterized by its increasing volume, velocity, variety and veracity. All
these characteristics make processing on this big data a complex task. So, for
processing such data Author need to do it differently like Map Reduce
Framework. When an organization exchanges data for mining useful information
from this Big Data then privacy of the data becomes an important problem in the
previous years, several privacy preserving models have been given. Anonymizing
the dataset can be done on many operations like generalization, suppression and
specialization. These algorithms are all suitable for dataset that does not
have the characteristics of the Big Data. To perpetuate the privacy of dataset
an algorithm was proposed recently. An author represents how the growth of big
Data characteristics, Map Reduce framework for privacy preserving in future of
our research.

Conclusion

This
review paper instant approach for mining and managing data from social chain
which depends upon combination of large amount of data through social networks
which is based on  infrastructure
paradigms and combination of big data. Map Reduce model is useful method  to mine, store and process bulk data from
social network. Mined data processing is  performed by Hadoop which simplifies
development of new algorithms and provides high scalability and flexibility.
The Map Reduce programming path has been successfully used by Google for many
different purpose. Author attributes this success for many reasons. First, the
model is  used, even for programmer
without any experience with parallel processing and distributed system, because
it shields the details of parallelization, fault tolerance, and load balancing.
Second, a large variety of problem is easily expressible as Map Reduce
computation. For example, Map Reduce is used for the generalization of data for
Google’s production web search service for sorting, for data mining, for
machine learning and many other systems. This paper presents development of an
implementation of Map Reduce that extend to bulk storage of machines comprising
thousands of machines. The utilization makes efficient use of these machine
resources is suitable for many large computational issue encountered at Google.

References

 

1.
Mr. V. R. Nagarajan, Monisha. P. M.,
“Extracting Knowledge from Social Media to Improve Health Informatics”.

2. Jun  Huan, 
Wei  Wang,  Jan 
Prins,  “Efficient  Mining 
of  Frequent  Subgraph in the Presence of Isomorphism”.

3.
Matthew Herland, “A review of data mining using big data in health informatics”.

4. Deepa Sharma, “Efficient Data Retrieval using Combine Approach
of SOM and K-Mean Clustering”,   International
Journal of Computer Applications .

5.
Hari Kumar.R M.E (CSE), Dr. P. Uma Maheshwari, Ph.d, “Literature survey on big
data in cloud,” International Journal of Technical Research and Applications.

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