Unstructured Datasets Analysis: Thesaurus Model

International Journal of Computer Applications Technology and Research
Volume 6–Issue 4, 209-212, 2017, ISSN:-2319–8656
www.ijcat.com 209
Unstructured Datasets Analysis: Thesaurus Model
Parvathy Gopakumar
Department of Computer
Science and Engineering
Mangalam Engineering College
Kottayam,Kerala,India
Neethu Maria John
Vinodh P Vijayan
Abstract: Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University
of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service
organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an
unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us
with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java
executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of
business analysis.
Keywords: Hadoop;MapReduce;HDFS;NoSQL;Hadoop-Streaming
1. INTRODUCTION
Data has never been more important to the business world as
it has become a vital asset as valuable as oil and just as
difficult to mine, model and manage. The volume and veracity
of the datasets that are being stored and analyzed by the
business are unforeseeable and the traditional technologies for
data management such as relational databases cannot meet the
current industry needs. Bigdata technologies play a vital role
to address this issue. Early ideas of big data came in 1999 and
at present it becomes an unavoidable phenomenon tool
through which we manage business and governance. For a
layman the idea of Bigdata may relate to images of chaotic
giant warehouses over crowded office space with numerous
staffs working through huge number of pages and come with
boring formal documents under supervision of some old
bureaucrat. On the contrary working of Bigdata is simple and
well structured, yet exciting enough to pose new challenges
and opportunities even to experts of industry. It provides
parallel processing of data in hundreds of machines that are
distributed geographically. Necessity of Bigdata arises under
the obligation of the following:
1. When existing technology is inadequate to perform
data analysis.
2. In the case of handling more than 10TB of dataset.
3. Relevant data for an analysis present across multiple
data stores which are filed in multiple formats.
4. When steaming data have to be captured, stored and
processed for the purpose of analysis.
5. When SQL is inefficient for high level querying.
In today’s data centered world Hadoop is considered as the
main agent of big data technology due to its open source
nature. However as it is a java based ecosystem, it created
hurdle for programmer from non-java background. To address
this issue it has facilitated a tool, ‘Hadoop-Streaming’ by
enabling flexibility in programming with effective parallel
computability.
2. PROBLEM STATEMENT
Why Big data analysis? Well, it helps the organization to
harness their transactional data and use it to identify new
opportunities in a cost effective and efficient manner. Primary
aim of data analysis is to glean actionable logic that helps the
business to tackle the competitive environment. This will alert
the business for their inevitable future by introducing new
products and services in favor of the customers. Unfortunately
for the matter of convenience 80% of the business oriented
data are stored in an unstructured format. Structured data
usually resides in a relational database with predefined
structures so converting the data to different models and
analyzing them seems mundane. Here the role of Hadoop-
Streaming arises which works on a Map and Reduce paradigm
by analyzing the unstructured data and presents viable
business logic.
The aim of the paper is to:
 Study existing framework employed by industry
players.
 Present a new roadmap for efficient and effective
approach to Bigdata problems: THESAURUS
MODEL
3. BACKGROUND
3.1 Structured Vs Unstructured datasets
The question that encounters a rookie is that why one uses
unstructured dataset when there is always a possibility of
using structured data. At the outset of computing, the term
storage corresponded only plain texts. Now user needs to store
richer content than plain text. Rich data type includes pictures,
movies, music, x-rays ,etc.It provides superior user experience
at the expense of storage space. Such data sets are called
unstructured because they contain data that do not fit neatly in

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a relational database. Industry came up with a third category
called semi structured data which resides in a relational
database, similar to structured data. However it does not have
some organizational property necessary to make them easy to
be analyzed.(Eg.XML doc)
3.2 NOSQL Data store
A NOSQL database [4] provides mechanism for storage and
retrieval of data which is modeled in contrast to the tabular
relations used in relational databases. It become common in
the early twenty first century when the industrial requirements
triggered a need of database structures that support query
languages other than SQL.(called “Not only SQL”, non
SQL).This is mostly used in big data and real-time
applications as it provides simpler design, horizontal
scalability and high availability. The most popular NOSQL
databases are MongoDB, Apache Cassandra [3], Datastax,
Redis.
3.3 Hadoop & Hadoop Streaming
Apache Hadoop [1] is open source software for reliable,
scalable and distributed computing. Hadoop framework
allows distributed processing of large datasets across low
level commodity hardware using simple programming
models. This framework is inspired by Google’s MapReduce
structure in which application is broken down into numerous
small parts and each part can be run in any node in the cluster.
Hadoop contains two major components - a specific file
system called Hadoop Distributed File System (HDFS) and a
Map Reduce framework. Hadoop works on divide and
conquer principle by implementing Mapper and Reducer in
the framework. Mapper function splits the data into records
and converts it into (key,value) pairs. Before feeding the
output of the Mappers to Reducer an intermediate Sort and
Shuffle phase is implemented in the MapReduce framework
to reduce the work load at Reducer machine. The sorted
(key,value)pair is given into Reducer phase. The Reducer
function does the analysis of the given input and the result
will be loaded to HDFS(eg.The maximum temperature
recorded in a year, positive and negative ratings in a business
etc.).The analyst has to develop Mapper and Reducer
functions as per the demand of the business logic.
Figure 1 Hadoop-Streaming
Hadoop Streaming (see Figure 1) is an API provided by
Hadoop which allows user to write MapReduce functions in
languages other than java[2]. Hadoop Streaming uses Unix
standard streams as the interface between Hadoop and our
MapReduce programs, so the user has the freedom to use any
languages (Eg. Python, Ruby, Perl etc.) that can read standard
input and write to standard output.
4. ANALYSING UNSTRUCTURED
DATASETS USNG HADOOP-
STREAMING
Due to the difficulties in analyzing the unstructured data
organizations have turned to a number of different software
solutions to search and extract prerequisite information.
Regardless of the platform used, the analysis must undertake
three major steps– data collection, data reduction, data
analysis [7][8][9][10](see Figure 2):
Figure 2 Analyzing Unstructured Dataset
A. Data Collection: In this stage the datasets to be analyzed
can be collected through two methods. Firstly, data can be
downloaded from different nodes containing the specified
records to HDFS. Alternatively it can be done by connecting
to the local servers containing the records. The former can be
achieved by tools such as Sqoop, Flume and the latter using
Apache Spark[6]. In a real time environment the streaming
datasets can be accessed using standard public key encryption
technique to ensure authenticity.
B. Data Reduction: Once the unstructured dataset got
available, analysis process can be launched. It involves
cleaning the data, extracting important features from data,
removing duplicate items from the datasets, converting data
formats, and many more. Huge datasets are minimized into
structural and more usable format using series of Mapper and
Reducer functions. This is done by projecting the columns of
interest and thus converting it in a format which will be
adaptable for final processing. Cleaning text is extremely easy
using R language, whereas Pig and Hive supports high level
abstraction of data preprocessing.
C. Data Analysis: Before the inception of Bigdata
technologies collecting, preprocessing and analyzing terabytes
of data was considered impossible. But due to the evolution of
Hadoop and its supporting framework the data handling and
data mining process seems not so tedious. Programmer with
the help of Hadoop Streaming API can write the code in any
language and work according to the domain of user. In this
stage the pre processed data is studied to identify the hidden
pattern. Hadoop provides a Mahout tool that implements
scalable machine learning algorithms which can be used for

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collaborative filtering, clustering and classification .The
analyzed data then can be visualized according to the
requirement of the business using Tableau, Silk, CartoDB,
Datawrapper.
Thus the whole process of analysis can be explained in a five
step workflow:
1. Collecting the data from alien environment and
keep it inside the Hadoop Distributed File System.
2. Apply set of MapReduce tasks to the step one
collected data and project the columns of interest
based on the user query.
3. Keep the preprocessed data in HDFS for further
analysis.
4. Use the preprocessed data for analyzing the pattern
of interest.
5. Store the result in HDFS so that with the help of
visualization tools user can selectively adopt the
method of presentation.
5. MODIFICATION OF EXISTING
SYSTEM: THESAURUS MODEL
The underline motivation behind this model is the lack of
knowledge base in the existing analysis framework which in
turn causes the system to follow some unnecessary repetition.
Consider an analysis problem to find the maximum recorded
temperature in last 5 years. So the analysis is done by
1. Collecting the data from National Climatic Data
Center [5] and store in HDFS.
2. Project the field which contains the temperature
data i.e. the column of interest.
3. Store the preprocessed result in HDFS.
4. Find the maximum temperature reported by
analyzing the (key, value) pair.
5. Store the final result in HDFS.
Figure 3 Thesaurus Model
So the maximum temperature of the year is accessed from the
file system and can be used for monitoring and reporting
purposes. Later if the same analyst needs to find the
maximum humidity reported, he has to go through the whole
datasets and has to bear the trouble of preprocessing and
reducing the data again. This can be avoided by using
Thesaurus model. According to this module, minable
information are logically arranged and kept in the HDFS so
that the future request for the information retrieval can be
done in no time. Once the data set is converted into a
structural format the schema of the dataset should be specified
by the preprocessing programmer so the analyst need not
come across the trouble of understanding the newly created
data set.. This preprocessed datasets can replace the old
datasets so that the unnecessary storage issue is taken care of
by the model. The working of the system is specified in two
phases, one for collection and preprocessing, and second for
analysis. In the first phase the necessary data which can be
analyzed are collected and preprocessed. This data is then
stored in the thesaurus module in HDFS and made it available
for the user to analyze based on the industry needs. Thesaurus
not only contains the structured data but also the schema of
the data storage. In phase two, the required query can be
addressed by referring the schema .Thus analyst need not
consider the problems of unstructured data collected by the
system. The Figure 3 represents the work flow of Thesaurus
model.
1. Collect the data from distributed environment and
store in HDFS.
2. Use the stored data for preprocessing.
3. Store the preprocessed data in Thesaurus with a
predefined schema. To avoid the storage bottleneck
the data that are collected on the first place can be
removed as it is no longer necessary.
6. CONCLUSION & FUTURE SCOPE
Mining the inner pattern of business invokes the related trends
and interests of the customers. This can be achieved by
analysing the streaming datasets generated by the customers
in each point of time.Hadoop provides flexible architecture
which enables industrialist and even starters to learn and
analyse this social changes.Hadoop-Streaming is widely used
for sentimental analysis using non-java executables.Also
proposed a THESARUS model which works in a time and
cost effective manner for analysing these humongous data.
Future scope is to enable the efficiency of the system by
developing a THESARUS model which is suitable to analyse
terabytes of data and returns with the relative experimental
results.
7. ACKNOWLEDGMENTS
I would like to thank Almighty, my professors, friends and
family members who helped and supported in getting this
paper work done with in time.
8. REFERENCES
[1] Apache Hadoop. [Online]. Available:
http://hadoop.apache.org

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[2] Apache Hadoop-Streaming. [Online]. : http://hadoop-
streaming.apache.org
[3] Cassandra wiki, operations. [Online]. Available:
http://wiki.apache.org/cassandra/Operations
[4] NOSQL data storage [online]: http://nosql-database.org
[5] National energy research scientific computing center.
[Online]. Available: http://www.nersc.gov
[6] Apache Hadoop [Online]: http://spark.apache.org
[7] E. Dede, B. Sendir, P. Kuzlu, J. Weachock, M.
Govindaraju, and L. Ramakrishnan, “A processing
pipeline for cassandra datasets based on Hadoop
streaming,” in Proc. IEEE Big Data Conf., Res. Track,
Anchorage, AL, USA, 2014, pp. 168–175.
[8] E. Dede, B. Sendir, P. Kuzlu, J. Weachock, M.
Govindaraju, L. Ramakrishnan, "Processing Cassandra
Datasets with Hadoop-Streaming Based Approaches",
IEEE Transactions on Services Computing, vol. 9
[9] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae,
J. Qiu, and G. Fox, “Twister: A runtime for iterative
mapreduce,” in Proc. 19th
ACMInt. Symp. High Perform.
Distrib. Comput., 2010, pp. 810–818.
[10] Z. Fadika, E. Dede, M. Govindaraju, and L.
Ramakrishnan, “MARIANE: MApReduce
implementation adapted for HPC environments,” in Proc.
12th IEEE/ACM Int. Conf. Grid Comput., 2011,vol. 0,
pp. 1–8.

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