ABSTRACT conflict reaction occurs mostly, but sometimes

ABSTRACTThe research on this reviewpaper presents the complicated usage of prescribed drugs which perform in thezone of data mining for organizing high volume of data and usage of complexfunction for performing more refined analysis using cloud platform. The aim ofthis paper is to understand the extensive and innovative frame that uses the socialmedia to characterize drug abuse. The rough idea of thissurvey is a analytical approach to analyze social media for acquiring theemerging trends in drug abuse by applying powerful techniques such as cloudcomputing and Map Reduce model.

This paper describeshow to capture important data to evaluate from networks like Twitter, Facebook,and Instagram. Big data techniques are used to mine the useful content foranalysis.1.INTRODUCTIONSocialmedia is an internet based applications which can be used for sharing informationand creative ideas through a communication network channel. Currently, socialmedia is used for enumerating the information regarding patients forunderstanding the symptoms of patient. Social media allows message sharing,collecting information and deliver to the health care space.

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Health care spaceis the one that provide the data’s of patient with their permission. The properway of accessing data and programs over the internet known as cloud. It modelthe social medias such as facebook, twitter etc using network based analysismethod. Currently, the scientificresearch often requires vast amount of estimation during simulation and data processing.The scientific problem can be solved by automatic computational throughcollection or array list which is emerged by set of sensors. Themain aim of this paper is to use the social media as an informative source foranalyzing the illicit drug activities in the society. Data mining play animportant role in all stages during the development of drug.

The use of datamining techniques during the drug development is mainly classified into twoareas:1.New Effect of Drug Identification: conflict reaction occurs mostly, butsometimes new remedial effect occur and effects in some population.2.Suitableness in drug use.Acrawler which basis in Map Reduce Model is performing the data mining task for thedistributed computation of data which is implemented in the framework ofHadoop.

Data processing consists of three stages, first and second stages arecollecting information from different media sources and filter it which resultsin small dataset with data corresponding to solve the task. On the last stagethe small dataset which are analyzed using refined models. The main advantageof this paper is that to provide knowledge about the drug usage for a group ofpeople which are observed who rarely use drugs or not addicted to drugs andanother aim is to collect the reviews of patients which cause side effects dueto the drug and can prescribe another drug through media.

Literature SurveyFromV. R. Nagarajan, et at1 social media provide information for the field ofhealth informatics which includes Bioinformatics, Image informatics, Clinicalinformatics, Public health informatics etc. In this paper they use the methodscalled SOMS ( an analysis to check the interrelationship between userposts  and positive or negative commentson drug usage) and hierarchical clustering. This paper  provide a framework which evaluate thepositive and negative symptoms of disease and also the side effects of treatmentcommon cancers lung cancers.

FromJun Huan, et al2 frequent subgraph mining is an active research topic in thedata mining community. They use graph as a general model to represent the dataad can be used in several field like bioinformatics, web indexing, etc. Theproblem of frequent sub-graph mining is to find all frequent subgraphs from agraph database. In this paper they propose a new algorithm FFSM(Fast FrequentSubgraph Mining) for the frequent sub-graph mining problem i.e., to reduce thenumber of redundant candidates proposed.FromMathew Herland, et al3  a bulk amountof data is produced within health informatics and analysis of this data is doneby big data techniques and big data allows potentially unlimited possibilitiesfor knowledge to be gained.

This information can improve health care qualityoffered to patients. A several problem will arise while managing this bulkamount of data especially how to analyze data in a reliable manner. This paperpresents big data tools and approaches for the analysis of health informaticsdata gathered at multiple levels including the molecular, tissue, patient andpopulation levels.FromDeepa Sharma, et al4 appearance of recent techniques for scientific knowledgecollection has resulted in large scale accumulation of information relatingvarious fields. Retrieval of data from huge knowledge base by typical queryways is an inadequate form.

Therefore, cluster analysis is used for analysisand k means clustering algorithm is mostly used for data mining applications. The analysis of the cancer data set with the k meanand then applying with the Som. This paper proposes a techniquefor creating knowledge retrieval more practical and efficient using SOM with Kmean clustering technique, So as to get better clustering with reduced quality.FromHari Kumar and Dr. P.

Uma Maheshwari 5 Big data is the term thatcharacterized by its increasing volume, velocity, variety and veracity. Allthese characteristics make processing on this big data a complex task. So, forprocessing such data Author need to do it differently like Map ReduceFramework. When an organization exchanges data for mining useful informationfrom this Big Data then privacy of the data becomes an important problem in theprevious years, several privacy preserving models have been given.

Anonymizingthe dataset can be done on many operations like generalization, suppression andspecialization. These algorithms are all suitable for dataset that does nothave the characteristics of the Big Data. To perpetuate the privacy of datasetan algorithm was proposed recently. An author represents how the growth of bigData characteristics, Map Reduce framework for privacy preserving in future ofour research.ConclusionThisreview paper instant approach for mining and managing data from social chainwhich depends upon combination of large amount of data through social networkswhich is based on  infrastructureparadigms and combination of big data.

Map Reduce model is useful method  to mine, store and process bulk data fromsocial network. Mined data processing is  performed by Hadoop which simplifiesdevelopment of new algorithms and provides high scalability and flexibility.The Map Reduce programming path has been successfully used by Google for manydifferent purpose. Author attributes this success for many reasons. First, themodel is  used, even for programmerwithout any experience with parallel processing and distributed system, becauseit shields the details of parallelization, fault tolerance, and load balancing.Second, a large variety of problem is easily expressible as Map Reducecomputation. For example, Map Reduce is used for the generalization of data forGoogle’s production web search service for sorting, for data mining, formachine learning and many other systems.

This paper presents development of animplementation of Map Reduce that extend to bulk storage of machines comprisingthousands of machines. The utilization makes efficient use of these machineresources is suitable for many large computational issue encountered at Google.References 1.Mr. V. R.Nagarajan, Monisha. P.

M., “Extracting Knowledge from SocialMedia to Improve Health Informatics”.2. Jun  Huan, Wei  Wang,  Jan Prins,  “Efficient  Mining of  Frequent  Subgraph in the Presence of Isomorphism”.3. MatthewHerland, “A review of data mining using big data in health informatics”.

4. Deepa Sharma, “Efficient Data Retrieval using Combine Approachof SOM and K-Mean Clustering”,   InternationalJournal of Computer Applications .5.Hari Kumar.R M.E (CSE), Dr. P.

Uma Maheshwari, Ph.d, “Literature survey on bigdata in cloud,” International Journal of Technical Research and Applications.


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