Social Media Market Trender with Dache Manager Using Hadoop and Visualization Tools

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1320
SOCIAL MEDIA MARKET TRENDER WITH DACHE MANAGER USING
HADOOP AND VISUALIZATION TOOLS
Raveena Rana [1], Tejas Shah[2], Jitanksha Shrimanker[3]
1-3BE Student, Information Technology Department
K. J. Somaiya Institute of Engineering & Information Technology, Mumbai, Maharashtra, India
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Abstract- The modern business and advertising strategies
all involve market analysis and study of the trends going on
in the world. Social media is the main advertising and
analysis market in today’s world. The current Analytics tools
and models that are available in the market are very costly,
unable to handle Big Data and less secure. The traditional
Analytics systems takes a long time to come up with results,
so it is not beneficial to use for Real Time Analytics. So, the
proposed work resolves all these problems by combining the
Apache Open Source platform which solves the issues of Real
Time Analytics using HADOOP. It also provides scalability
and reduced cost over analytics by using open Source
Software. [1]
Key Words: Map Reduce (MR), Hadoop, Java , Flume ,
Sqoop, Caching.
I. INTRODUCTION
The world is getting smaller and smaller day by day
through internet and usage of social sites. Data is growing
exponentially as the number of users and activity over the
web is increasing rapidly. The consumer market is
becoming increasingly competitive and the need to keep a
tab on trends and consumers’ opinions is a must. [3]
Social Media is being extensively used by people
belonging to all age groups’ for expressing their reviews
and suggestions whenever they buy a new product or a
new product is launched. Also it is used for
recommendations to friends and family of a particular
product or service. Social media sites like Facebook and
Twitter generate huge amounts of data on a daily basis.
This data is directly from the end user or consumer and as
a result is of great value to the organizations and
companies. [1]
This data is still scattered and not in a suitable
format that may yield any results. Also the data is so huge
that the buzz-word Big Data is the only one that can
represent such an amount of data. Our Project is based on
Hadoop framework that can
manage and handle Big Data efficiently. Main reasons for
choosing Hadoop are:
1. The data generated by Social sites are too large
for traditional databases and analysis tools.
2. Hadoop being a framework gives more flexibility
in implementing ideas.
3. Scalability is a major advantage of using Hadoop
over other platforms.
Application developers specify the computation in
terms of a map and a reduce function, and the underlying
MR Job scheduling system automatically parallelizes the
computation across a cluster of machines. MR gains
popularity for its simple programming interface and
excellent performance when implementing a large
spectrum of applications. Since most such applications
take a large amount of input data, they are nicknamed
“Big-data applications”. Hadoop is an open-source
implementation of the Google MR programming model.
Hadoop consists of the Hadoop Common, which provides
access to the file systems supported by Hadoop. MR
provides a standardized framework for implementing

large-scale distributed computation, namely, the big-data
applications. [5][6]
The work proposes to combine the Apache Open
Source Modules and configure them to get the required
result. Hadoop is flexible and scalable architecture. Google
MR is a programming model and a software framework for
large-scale distributed computing on large amounts of
data [7][8].
. We propose Dache, a data-aware cache framework
for big-data applications. In Dache, tasks submit their
intermediate results to the cache manager. A task queries
the cache manager before executing the actual computing
work. We implement Dache by extending Hadoop. [2]
In the proposed work, we would be grabbing data from
Twitter and analyzing it through visualization techniques.
[4]
The project aims at finding out the trends of the
market according to the social sites such as Twitter and
visualizing and presenting it in a simple format such as a
graph or chart that can be understood by anyone and a
clear conclusion can be drawn out of it. It also aims at
tackling data size issues by using Hadoop and MR
frameworks that are open-source. It also tries to improve
the processing speed by introducing a cache concept for
distributed computing named Dache.[9]
II. DRAWBACKS OF EXISTING SYSTEM
The current Analytics tools and models that are
available in the market are very costly, unable to handle
Big Data and less secure. The traditional Analytics systems
takes a long time to come up with results, so it is not
beneficial to use for Real Time Analytics.
Google’s MR and Apache’s Hadoop are the defacto
software systems for big-data applications. An observation
of the MR framework is that the framework generates a
large amount of intermediate data. Such abundant
information is thrown away after the tasks finish, because
MR is unable to utilize them. Also the MR operates on huge
amount of data and as a result the processing takes usually
a longer period of time.
A drawback of Hadoop operation is that MR, its
processing part creates a lot of intermediate data during
operations such as word-count on a particular file. But in
the end we get only the desired output file and the rest of
the intermediate data generated during the processing is
thrown away. This data can be stored separately and can
be used in similar future operations to speed up the
process. Dache, a concept of cache will be introduced in
the project so as to boost the operation speed of MR.
The current Analytics tools and models that are
available in the market are very costly (SocialBro,
TweetStats, Twentyfeet,) unable to handle Big Data and
less secure. Hence the existing system lags during
functioning. The traditional Analytics systems takes a long
time to come up with results, so it is not beneficial to use
for Real Time Analytics. Real time analytics require high
speed and less time for performing it.
Querying Twitter data in a traditional RDBMS is
inefficient. There are many Twitter API which provide
streaming of twitter data. Streamed data is not preferred.
Traditional Hadoop MR does not use the concept of Cache.
Dache is a caching technique for Big Data. It boosts the
speed of the analysis. This will help to cut short the time
required for the entire process.[10]
III. PROPOSED METHODOLGY
We propose to grab data from Twitter and store
in HDFS. The data will be converted into formats such as
CSV and will be visualized via different tools. Dache is a
data-aware cache framework for big-data applications. In
Dache, tasks submit their intermediate results to the cache
manager. A task queries the cache manager before
executing the actual computing work. We implement
Dache by extending Hadoop. Advantages of proposed
System are as follows:
 Efficiency and Reliability of handling Big Data
increases with Hadoop. The system can be scaled

as per the user requirement. Storing of grabbed
data in HDFS will increase the speed, reliability
and efficiency of data & results.
 The CSV used is better for understanding. The CSV
will be exported to database for backup purposes.
Graphs and charts will provide a clear picture of
the latest trends.
 The operation speed will be boosted by Dache.
There would be no cost of data grabbing and
handling as all the tools used are open source
softwares. Dache will make the MR operations
faster.
The Proposed System will show us the trends in
market that are most talked about on social media. In the
end, we can compare various brands, topics and also the
traditional Hadoop MR operations vs Hadoop MR
operations using Dache.
A front end will be created wherein a user can sign up
for an account. After sign up, he can login the account.
There will be various options for grabbing data, analysis of
the data, graphs of analysis etc. At the backend, the
grabbed data will be stored in HDFS. It will be in the form
of JSON. This data will be converted into CSV. Using
Visualization tools, reports will be generated in the form of
graphs.
Dache helps in speeding up the Big Data whenever the
same data is accessed again, just like cache functions in
other applications. This will increase the speed of
processes and decrease the time required for accessing the
same data otherwise.
Figure 1: Proposed system architecture
Figure 1 shows the block diagram of the proposed system.
The project aims to provide the end user recent Market
Trends. The proposed system is very cost effective as most
of the work is done on free open source softwares. The
drawback of cost in existing system is overcome.
The proposed system architecture involves the following
modules:
1. Module 1: Grab Data From Social Media
Description: In this module, the data will be
grabbed from social site on the basis of the
keywords entered by the user. The grabbed data
will be stored in Hadoop distributed file system
(HDFS).
Input: The user will enter the four token keys and
keywords for grabbing the data. On
the basis of keywords the data will be grabbed.
Output: Output will be the grabbed data files on
the basis of keywords. The format of the files will
be JSON and will be stored in the HDFS or other
specified location.
2. Module 2: Process Data using Hadoop
Description: The Grabbed Data which is in the
form of JSON will be converted into
Comma Separated Value (CSV) format with
MapReduce operations. In MapReduce there will
be five stages viz Input Phase, Mapper Phase, Sort
and Shuffle Phase, Reducer Phase and Output
Phase. Different Functions will be carried out on
the CSV data and visualized graphically.
Input: Grabbed data in the form of JSON
Output: The data in CSV form, Graphs of the
analyzed data.
3. Module 3: Display Result
Description: This module will be used to give the
results to the user in the form of graphs and
keywords entered.
Input: Request for results.

Output: Graphs of the analyzed data.
IV. IMPLEMENTATION
Initially, softwares like Apache Flume, Sqoop,
Twitter, Hadoop, VM Ware, Tableau, Pentaho, and Putty
have to be downloaded. Along with these, Flume settings
for Twitter are also required. Setup of Flume has to be
done first. All the services have to be started. A Twitter
account has to be made. In the Flume settings downloaded
for Twitter, desired keywords are entered. The keywords
are the topics for analysis. The data will be grabbed as per
the keywords entered. The data will be in millions. Hence
it is known as Big Data. The analyzed data will be stored in
HDFS in the form of JSON. This data will be converted to
CSV with the help of Sqoop. Dache impleme1ntation is the
next step. A Dache manager is created for speeding up the
process. The data will be processed by the visualization
tools which have already been downloaded. The output of
the analysis will be in the form of graphs. Studying of
graphical data is much simpler as compared to Big Data.
[1]
Hadoop framework is used to facilitate operations
on BigData. Hadoop allows us to install various softwares
and work with them. By installing Flume and running it,
we will be grabbing data from social sites and storing it
directly in Hadoop Distributed File System (HDFS). HDFS
is the storage part of Hadoop.
MapReduce will be used to perform operations such
as Conversion of Data, Word-count, etc and Dache will be
used to optimise and increase the speed of operations.
MapReduce is the processing part of Hadoop. Visualization
tools will be used to show the data graphically. Java will be
used to write MapReduce operations.
Technique for Dache Implementation:
Input Map Intermediate Reducer Output
Phase phase phase phase phase
Fig 4: High level description of architecture of Dache
We propose a novel cache description scheme. A
high-level description is presented in Fig. 4.3. This scheme
identifies the source input from which a cache item is
obtained, and the operations applied on the input, so that
a cache item produced by the workers in the map phase is
indexed properly. In the reduce phase, we devise a
mechanism to take into consideration the partition
operations applied on the output in the map phase. We
also present a method for reducers to utilize the cached
results in the map phase to accelerate the execution of the
MapReduce job. We implement Dache in the Hadoop
project by extending the relevant components.
Algorithm for MapReduce operations (Wordcount)
 Map()
- Input <filename, file text>
- Parses file and emits <word, count> pairs
 Reduce()
- Sums Values for the same keys and emits
<word, TotalCount>
The mapper emits an intermediate key-value pair for each
word in a document. The reducer sums up all counts for
each word.
class Mapper
method Map(docid a, doc d)
for all term t ∈ doc d do
Emit(term t, count 1)
class Reducer

method Reduce(term t, counts [c1, c2, . . .])
sum ← 0
for all count c ∈ counts [c1, c2, . . .] do
sum ← sum + c 6: Emit(term t, count sum)
Figure 2 shows a screenshot of grabbed data in
the form of CSV.[1]
Figure 2: Screenshot of Grabbed Data
V. CONCLUSION
As compared to the existing system, our proposed
system is cost effective. Requirements of this proposed
system are easily available. All the softwares required are
free open source softwares which cost minimal. Time
saved in the entire process is cut down. Great speed is
provided as compared to the existing system. We have
already grabbed data for some keywords. We are working
on the Dache implementation. We have stated the
advantages of the existing system. Finally, we have
explored a cost effective way to analyze social media
market trends and generate a graphical view of the
analysis.
REFERENCES
[1] Raveena Rana, Tejas Shah and Jitanksha Shrimanker
“Social Media Market Trender with Dache Manager using
Hadoop and Visualization Tools”, “International Research
Journal of Engineering and Technology”, Volume: 03,
Issue: 02, February 2016.
[2]Hadoop: The Definitive Guide by Tom White.
[3] Yaxiong Zhao, Jie Wu, Cong Liu, “Dache: A Data Aware
Caching for Big-Data Applications Using the MapReduce
Framework” ,TSINGHUA SCIENCE AND TECHNOLOGY
ISSNl 1007-0214l l05/10l lpp39-50 Volume 19, Number 1,
February 2014.
[4] Kaveh Ketabchi Khonsari, Zahra Amin Nayeri, Ali
Fathalian, Leila Fathalian, “Social Network Analysis of
Iran’s Green Movement Opposition Groups using Twitter”,
2010 International Conference on Advances in Social
Networks Analysis and Mining.
[5] Karen Stepanyan, Kerstin Borau, Carsten Ullrich, “A
Social Network Analysis Perspective on Student
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on Advanced Learning Technologies.
[6] Guojun Liu, Ming Zhang, Fei Yan, “Large-Scale Social
Network Analysis based on MapReduce”,2010
International Conference on Computational Aspects of
Social Networks568.
[7] Kai Shuang, Yin Yang, Bin Cai, Zhe Xiang, “ X-RIME:
Hadoop-based Large-Scale Social Network Analysis”,
Proceedings of IC-BNMT20 10.
[8] Hyeokju Lee, Joon Her, Sung-Ryul Kim,
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International Conference on Computers, Networks,
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[9] Ge Song, Zide Meng, Fabrice Huet, Frederick Magoules,
Lei Yu, Xuelian Lin, “A Hadoop MapReduce Performance
Prediction Method”, 2013 IEEE International Conference
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[10] Gaurav D Rajurkar, Rajeshwari M Goudar, “ A speedy
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