Content uploaded by C K Gomathy
Author content
All content in this area was uploaded by C K Gomathy on Feb 17, 2023
Content may be subject to copyright.
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 1
DATA MINING PREPARATION: PROCESS, TECHNIQUES AND MAJOR ISSUES IN DATA
ANALYSIS
Dr C K Gomathy-Assistant Professor, Department of CSE, SCSVMV Deemed to be University, India
Mr.P.Gopala Krishna Reddy, Mr.Sai Srinivas, Mr.T.Karthikeya, Mr.R.Uday Kumar
UG Scholars, Department of CSE, SCSVMV Deemed to be University, India
Abstract:
Data preparation is an iterative-agile process for exploring,
combining, cleaning and transforming raw data into curated datasets
for self-service data integration, data science, data discovery, and
BI/analytics. To perform data preparation, data preparation tools are
used by analysts, citizen data scientists and data scientists for self-
service. The tools are also used by citizen integrators and data
engineers for data enablement to reduce the time and complexity of
interactively accessing, cataloging, harmonizing, transforming and
modeling data for analytics in an agile manner with metadata and
lineage support. These tools can provide data access for use in
mostly analytical tasks that include storage, logical and physical
data modeling, and data manipulation for data visualization, data
integration and analytics. Some tools support machine-learning
algorithms that can recommend or even automate actions to
augment and accelerate data preparation.
Keywords: Data Mining, Data preparation, Data Analysis, Big Data
Analytics
1.Introduction:
Data preparation is the sorting, cleaning, and formatting of raw data
so that it can be better used in business intelligence, analytics,
and machine learning applications.
Data comes in many formats, but for the purpose of this guide we’re
going to focus on data preparation for the two most common types
of data: numeric and textual.
Numeric data preparation is a common form of data
standardization. A good example would be if you had customer data
coming in and the percentages are being submitted as both
percentages (70%, 95%) and decimal amounts (.7, .95) – smart data
prep, much like a smart mathematician, would be able to tell that
these numbers are expressing the same thing, and would standardize
them to one format.
Textual data preparation addresses a number of grammatical and
context-specific text inconsistencies so that large archives of text
can be better tabulated and mined for useful insights.Text tends to
be noisy as sentences, and the words they are made up of, vary with
language, context and format (an email vs a chat log vs an online
review). So, when preparing our text data, it is useful to ‘clean’ our
text by removing repetitive words and standardizing meaning.For
example, if you receive a text input of:‘My vacuum’s battery died
earlier than I expected this Saturday morning A very basic text
preparation algorithm would omit the unnecessary and repetitive
words leaving you with: ‘Vacuum’s’ [subject] died [active verb]
earlier [problem] Saturday morning [time]’ This stripped down
sentence format is now primed to be much easier to be tabulated
analytically – an AI analysis bot could now, for instance, easily
calculate the number of text responses complaining of ‘early’
‘battery’ -related failures. Furthermore, this could be routed to a
relevant support team that can help the customer. Another easy
application is the removal of text noise, which, when dealing with
customer service, can take the form of URLs. URLs can trip up
machine analysis because each is a unique word and near impossible
to group. So, an intelligent AI text prep bot can be trained to
recognize the format of a URL and be programmed to turn all
instances in the simple, recognizable format of ‘--url--’. It’s even
more important for your system to use text analysis to detect user
input variation. Just for starters, we want to make sure personal
information that our users send us for support needs (phone
numbers, email addresses) aren’t included in our larger analytics
approach – to ensure data privacy and to simplify our data.
For example, customers may write their address and apartment in
the same line, versus two separate lines. If your data prep doesn’t
take this into account, you’ll end up with a ton of confusing entries
that will completely scramble results. Because of its detail-oriented
nature and perceived heavy manpower requirements, data prep is
menial work, but it’s benefits are obvious. Monkeylearn’s founder
and CEO Raúl Garreta agrees that it can be tedious:
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 2
“I mean, it's not the most interesting thing to do, but if you are a
data practitioner you find the importance of doing it and, as a
result, you appreciate it,” he said. Properly prepared, clean data, can
make or break proper analysis and thus hamper or help entire
business strategies. Let’s get into the benefits of well-prepped data.
Figure 1 Process of Data Mining Preparation
Data mining is the central part of the method of knowledge
detection. KDP is a process of seeking knowledge in data, using
data mining methods (algorithms) to extract challenging knowledge
from large quantities of information. The process of information
exploration will consist of the following steps: 1- Data Source: In
the context of computer science and computer applications, the
source of the data is where the data used comes from. The source of
data may be a database, a static file, live measurements on the
physical system, abstract web data, or one of the myriad data
services that stream and static over the internet. The primary
concern for information accuracy is the data source. In such cases,
to help companies and institutions operate more efficiently.
Identifying data sources is the first step in any data storage project
because you cannot do anything without the data. After setting up
the right plan to obtain accurate information (data), the next step is
to know how to store it consistently and in the same format so that
when you run the reports, you can get the right results for decision
making. Ultimately, data sources aim to help users and applications
connect with and move data where it needs to be. 2- Data Cleaning:
Data cleaning is the mechanism by which wrong, corrupt,
misformatted, duplicate or incomplete data is corrected or deleted
within a dataset. 3- Data Integration: Describes how data from
multiple sources are integrated into usable and meaningful
knowledge using a mix of techniques and business processes. 4-
Data Selection: The term data selection seeks to choose data that
should be preserved or shared/archived when the project is finished
during the data collection. 5- Data Transformation: The
transformation of data is the process of transferring data from one
format or structure to another. For activities such as data integration
and data management, data transformation is important. 6- Pattern
Evaluation: The pattern evaluation describes fascinating patterns of
information based on various types of interesting steps. A pattern is
seen as appealing because it is potentially useful, readily understood
by humans, and uses summary and viewing to make data
understandable. 7- Knowledge Presentation: The representation of
knowledge is a show of knowledge to the user in terms of trees,
tables, rules, graphs, charts, matrices, etc. Represented one Methods
for understanding the ins and outs of preparing data.
analysis should be submitted and therefore the data should be
converted to fulfill these demands. Moreover, the selection of
specific data to be analyzed can greatly influence the models
learned. It is often the most time-consuming part of any data mining
project. Many researchers have recognized high-end data extraction
expertise and information as essential. research subject in machine
learning and the database system and in many industrial enterprises
as an interesting field with the potential of generating substantial
revenues. Data, information, or knowledge have an important role in
human activities. Data mining has significance in finding patterns,
forecasting and discovering knowledge, etc. in various business
fields. Data mining is used in the Medical Sciences, the detection of
malicious executables, statistical techniques, identifying patterns,
sales forecasting, basket analysis, mathematical organizations, and
the presentation of information in a way that can be easily
interpreted by people. This allows businesses and organizations to
concentrate their stored data on the most relevant information.The
massive increase in data in recent years has led to this, prompting
recording, processing, and analyzing these records. Research
Contributions To automate data preparation, several challenges must
be addressed, including: (1) accommodating several different
components of the same task, Establish alternative approaches; (2)
Coordination of the various components of data preparation that rely
on different evidence about the problem to be solved; (3) Identify
the evidence that enables us to perform steps on the data; (Iv)
Creating several options between candidate and alternative
solutions, in light of multiple requirements; (5) Linking multiple
components for the purpose of forming transactions.
2.Literature Survey and Proposed System:
Data mining is important in pattern finding, prediction, discovery of
knowledge, etc. in different fields of industry. Data mining
applications use a range of data types, from text to photos,
warehouses, and different databases and data structures. Different
data mining techniques for extracting patterns and hence knowledge
from these various databases. Data collection and methods Data
mining is an essential activity and domain awareness is crucial in
this process. A range of data needs to be collected in the particular
problem area to collect data, pick data from the identified data for
data mining, clean and process data, extract patterns to generate
inforsmation and finally interpret pattern and generate knowledge.
Data mining is used in medical sciences, malicious executables
tracking, sports associations, trend recognition, sales forecasting,
basket analysis etc. There were still numerous unresolved security
problems, social concerns, user interface issues, performance issues,
and so on before data mining became a conventional, mature and
trusted file. While data mining is very efficient, during its
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 3
implementation it faces numerous challenges. Problems and
challenges related to data mining may be efficiency, data,
techniques, etc. When challenges or problems are correctly
identified and sorted properly, data mining is successful. We would
like to propose potential recommendations for data creation,
including the development of efficient and successful data prepared
algorithms and systems for single and multiple data sources, taking
all internal data into account. And external knowledge. Create the
environment for immersive and automated data extraction.
3. Methodology:
Data mining is the process by which useful data, patterns and trends
from several data are collected, using techniques like clustering,
classification, regression and correlation. Data, information, and
knowledge are the exciting roles of human life. Massive data
warehouses with the rapid development of file technologies require
big data analysis and modelling to predict future information trends.
Data mining is called a technique by which the necessary
information in databases can be extracted from raw information.
Using the data mining prediction analysis methodology, future
scenarios can be predicted with regard to current knowledge.
Forecast analysis is a combination of classification and aggregation.
Data mining is used for data extraction from a great deal of
information. Data mining is made up of two predictive and
descriptive models. Data management aims to collect data in
stratification files with either the ultimate aim of learning new
effects or seeing new areas. Data analysis techniques were applied
to the higher education institution's educational data. The analytical
data included event records which extracted hidden information
from the data by performing pattern recognition and forecast
modelling tasks. Solve many complex problems, including energy
efficiency and energy use, structural analysis, building materials,
smart cities, design and optimization, technology forecasts, soil
engineering and construction engineering. Accurate forecasting of
traffic information, predicting the occurrence of COVID-19, the
user must use the information published effectively through
prediction models. In this section, the applied methods used in our
approaches, such as decision support systems, data mining, and
correct data preparation and extraction, have an effective role in
showing the right results through their application to data mining
techniques. The results differed between accurate and imprecise due
to their reliance on the techniques and algorithms used to show
results on prepared data. Therefore, the main features of the
conceptual data management platform must be followed to prepare
the data to offer more accurate results before implementing it, which
are: 1. Taking the initial data and preparing it for analysis.
2. Apply algorithms to raw data to reveal new insights.
3. Create database categories to place raw data.
4. Data collection and classification.
5. Collect data from multiple sources and collect them
together.
6. Converting unstructured data into data ready for analysis.
7. Merging different types of data into a unified system.
8. Converting data from one type to data of another kind.
2. Data Mining Implementation Process Many different sectors are
leveraging data mining to enhance their businesses' efficiency,
including manufacturing, banking, marketing, aerospace, education,
health, etc. Therefore, the need for the traditional data extraction
process has effectively improved. Data mining techniques should be
reliable and reproducible by company personnel with little or no
knowledge of the data mining context. Several steps determine the
classic presentation (see Table 3). First, The problem has to be
identified in terms of work or academic objectives and converted
into concrete data mining and analytical objectives. The second
stage is the data step, the discovery, fusion and transformation of
primary data sources to be used for the related data mining mission.
This is typically the longest step unless the process is completely
automated. The third step is step modelling. Algorithms are used to
extract real data patterns, to predict or to calculate my metadata. In
the fourth stage these patterns and models are evaluated in a quality
and content format. The extracted forms will be added to the new
data during the final publication stage and the findings are combined
with other details for effective action.
3. Major Issues in Data Mining With the increasing growth of data
in any application, data mining satisfies an imminent need for
accurate, scalable, and flexible data analysis in our society Data
mining can be seen as a natural IT creation and a convergence of
several disciplines and associated fields of application. Although
data mining is very effective, it faces numerous challenges
throughout its implementation. Data mining issues and challenges
can involve performance, data, techniques, etc. Data mining is
successful if issues or problems are correctly detected and properly
sorted. Data can be collected on any data as long as data is essential
for the target app, such as database data, data warehouse data,
transaction information, and advanced data forms. Figure 2
demonstrates the data mining problems.
Figure 2 Issues in data mining
4. Data mining techniques Organizations and institutions today have
more access than ever to data. However, it can be very difficult to
grasp large volumes of structured and unstructured data to
strengthen the organization and at other levels, due to the sheer
volume of information. If not properly treated, this challenge will
lessen the benefits of all data. Data mining is how businesses
discover data trends to obtain knowledge that is important to their
business needs. For both business intelligence and data science, it is
important. There are many techniques for data mining that
organizations can use to convert raw information into actionable
insights. This encompasses everything from advanced artificial
intelligence to fundamental data planning, which are the secret to
optimizing the value of data investment.
4.1. Classification
It's a task of data analysis, i.e. the process of finding a model that
describes and differentiates data
classes and concepts. This data extraction approach helps to classify
data into different categories.
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 4
4.2. Clustering
Cluster analysis is a form of data extraction to classify related data.
This method helps to consider
gaps between data and similarities.
4.3. Regression
Regression analysis is a data extraction process in which the
relationship between variables is defined
and analyzed. It is used to evaluate a given variable 's likelihood,
since there are other variables.
4.4. Association rules
This data extraction technique helps to find the connection between
two or more objects. Detects a
pattern in the dataset.
4.5. Outer detection
This type of data extraction technique refers to the observation of
data elements that do not fit the
predicted behavior pattern in the data collection. This technology
can be used in different areas
including intrusion, tracking, fraud, detection of bugs, etc. Offshore
analysis or offshore mining is also
known as external detection.
4.6. Sequential patterns
This technique of data collection helps to detect or discover similar
patterns or trends in transaction
data over a given timeframe.
4.7. Prediction
The predictions used numerous other techniques for data mining,
such as patterns, sequences,
clustering, grouping, etc. It analyses past events or circumstances in
the right order to predict a future
occurrence.
4 .Implementation & Algorithm Techniques:
Access
There are many sources of business data within any organization.
Examples include endpoint data, customer data, marketing data, and
all their associated repositories. This first essential data preparation
step involves identifying the necessary data and its repositories. This
is not simply identifying all possible data sources and repositories,
but identifying all that are applicable to the desired analysis. This
means that there must first be a plan that includes the specific
questions to be answered by the data analysis.
Ingest
Once the data is identified, it needs to be brought into the analysis
tools. The data will likely be some combination of structured and
semi-structured data in different types of repositories. Importing it
all into a common repository is necessary for the subsequent steps in
the pipeline. Access and ingest tend to be manual processes with
significant variations in exactly what needs to be done. Both data
preparation steps require a combination of business and IT expertise
and are therefore best done by a small team. This step is also the
first opportunity for data validation.
Cleanse
Cleansing the data ensures that the data set can provide valid
answers when the data is analyzed. This step could be done
manually for small data sets but requires automation for most
realistically sized data sets. There are software tools available for
this processing. If custom processing is needed, many data engineers
rely on applications coded in Python. There are many different
problems possible with the ingested data. There could be missing
values, out-of-range values, nulls, and whitespaces that obfuscate
values, as well as outlier values that could skew analysis results.
Outliers are particularly challenging when they are the result of
combining two or more variables in the data set. Data engineers
need to plan carefully for how they are going to cleanse their data.
Format
Once the data set has been cleansed; it needs to be formatted. This
step includes resolving issues like multiple date formats in the data
or inconsistent abbreviations. It is also possible that some data
variables are not needed for the analysis and should therefore be
deleted from the analysis data set. This is another data preparation
step that will benefit from automation. Cleansing and formatting
steps should be saved into a repeatable recipe data scientists or
engineers can apply to similar data sets in the future. For example, a
monthly analysis of sales and support data would likely have the
same sources that need the same cleansing and formatting steps each
month.
Combine
When the data set has been cleansed and formatted, it may be
transformed by merging, splitting, or joining the input sets. Once the
combining step is complete, the data is ready to be moved to the
data warehouse staging area. Once data is loaded into the staging
area, there is a second opportunity for validation.
Analyze
Once the analysis has begun, changes to the data set should only be
made with careful consideration. During analysis, algorithms are
often adjusted and compared to other results. Changes to the data
can skew analysis results and make it impossible to determine
whether the different results are caused by changes to the data or the
algorithms.
5.CONCLUSION:
In this chapter, we have discussed several methods and techniques
for data preprocessing in the
context of Big Data. We have reported experiences and first insights
about the preprocessing of a
real world dataset in a petro-chemical production setting. Overall,
the principle “privacy by design” is extremely important in a big
data environment in order to protect personal data of individuals
from further analytics. We also identified two types of tasks that
have to be addressed separately in order to create an assistant system
for early warnings. Our experiences show that unstructured data can
be found in various places in a production environment containing
shift reports, alarm data and even some error codes in the sensor
data. For structured data, always the relation between filtering and
information loss needs to be balanced. Furthermore, one simple, but
important preprocessing techniques for the analysis of natural
language text is the mapping of different notations of the same word
to a common form, i.e., to a common terminology so that the
different entities can be correctly resolved. Furthermore, we have
found that the conversion of file formats can lead to further
preprocessing steps, especially when the data is fragmented. Finally,
it is important to consider that data is not always coming from one
system, making it necessary to check for time offsets and the
reasons
behind it, connecting that to the business and data understanding
phases of CRISP-DM.
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 5
6.Results:
One key aspect prior to an analysis is the anonymization of the data.
In order to protect personal data of individuals from further
analytics, person names should be made unrecognizable. One way to
achieve this is to simply remove those names from the document.
This follows the principle “privacy by design” which means that
anonymization should be performed as early as possible, so that
person names cannot be the subject of further analytics any more.
When it comes to automated anonymization, it is also a requirement
to convert the files from binary to text formats. One should take into
consideration that such a conversion could cause a loss of
information. For example, in the case of graphical documents, it is
obvious that the visual information cannot be captured by a text
format. Such documents have to be manually anonymized by hand
(e.g., by blackening person names). For text processing documents
(e.g., word format) most of the information can be preserved by
choosing an html format over a plain text format. This way, the
document structure (e.g., headlines, bold words) is still available for
analytical processing such as generating warnings for abnormal
situations. An assistant system that generates early warnings for
abnormal situations actually has to solve two types of tasks. At first,
the system has to identify events based on the long history of data.
A burst in the frequency of the alarm logs, for example, could be an
indicator for an unexpected situation, which corresponds to an
event. Secondly, after identifying events, the system needs to extract
features that help to predict this type of event as early as possible.
Coming back to the frequency of alarm logs example, small
fluctuations in the frequency distribution could be an indicator for a
specific event characterized by a burst in the alarm log frequency. In
our analysis, we focused on unstructured data, i.e., free text entered
by operators into the system, but we found that text data could also
be part of the sensor data as well. For example, a sensor value is
only recorded when it is within a specific range of electricity
current, e.g. 24mA. An electricity current above the threshold
cannot be recorded and will result in a specific error code translated
to a label, e.g. the character string “bad value”. There exists a
variety of error codes for sensor data and one has to consider how to
deal with these error values. When analyzing free text entered into
the system by an operator one has also to consider two types of
situations. Text messages that are really typed by the operator, e.g.
because the text is about irregular situation in the production
facility, and text messages that are just copied from a template, e.g.
the text is about a standard procedure like weekly maintenance
work. Both situations show different characteristics and should be
considered separately. In the former case, the free text is prone to
typos and different spellings of the words, e.g. abbreviations and
acronyms. In an industrial environment, there also exist some
special wordings and a domainspecific vocabulary that has to be
taken into account. One of the key challenges when working with
industrial text data is to define a semantical relatedness between the
words due to the lack of domain-specific concept hierarchies. In the
FEE project, we tried to overcome this problem by using two
approaches. First, we used stemming (see section “Preprocessing of
Unstructured Data”) in order to find semantically related words by
removing of prefixes and suffixes. Furthermore, we used a micro-
worker approach, where ordinary persons should mark similar
words in order to improve the set of related words. We found that
the micro-worker approach was difficult due to the lack of
domainspecific knowledge by the micro-workers. With stemming,
there is also the problem of over- and under-stemming making it
necessary to manually inspect stemming results. Concerning
numerical data, methods like outlier detection can help to check the
data quality and to ensure meaningful episodes, e.g., in the case of
time series. In addition, value imputation methods can be helpful,
e.g., in the case of missing values. Advanced methods for data
preprocessing, like filter approaches of course can result in a loss of
information and should therefore always be targeted with respect to
the analytical goals. Having different data sources in FEE project,
we also had to deal with different file formats. All the binary
formats were converted to text formats in order to be able to further
process the files with standard command line tools. For Excel
documents, we choose the CSV format and Word / PDF documents
were converted to HTML format in order to keep as much
information about the formatting as possible. With the PDF
documents, we found that text spanning multiple lines is divided by
line when converted to a text format. This could lead to artefacts
that have to be further processed, e.g. by dense-based clustering, to
connect associated character strings again. We also like point out,
that when dealing with data from different data sources one has to
consider the time dependency of the data. In a production
environment, there are multiple production applications that have
been introduced and expanded over multiple years. These systems
are typically not synchronized by a local time server making it
necessary to inspect time offsets between different data sources. We
found that log entries in the operation journals had an offset of
approximately one hour when compared to events in the sensor data
(see Figure 2). Reasons for time offsets can be found in differing
time zones as well as data types not capable of dealing with daylight
saving time. Furthermore operators do not have time for
documentation when a critical situation is about to happen, because
they have to react to bring the system to a stable state again.
Therefore, most of the documentation for critical situations is done
after the event has happened.
7.References:
1.DR.C.K.Gomathy , V.Geetha , S.Madhumitha , S.Sangeetha ,
R.Vishnupriya Article: A Secure With Efficient Data Transaction In
Cloud Service, Published by International Journal of Advanced
Research in Computer Engineering & Technology (IJARCET)
Volume 5 Issue 4, March 2016, ISSN: 2278 – 1323.
2.Dr.C.K.Gomathy,C K Hemalatha, Article: A Study On Employee
Safety And Health Management International Research Journal Of
Engineering And Technology (Irjet)- Volume: 08 Issue: 04 | Apr
2021
3. Dr.C K Gomathy, Article: A Study on the Effect of Digital
Literacy and information Management, IAETSD Journal For
Advanced Research In Applied Sciences, Volume 7 Issue 3, P.No-
51-57, ISSN NO: 2279-543X,Mar/2018
4. Dr.C K Gomathy, Article: An Effective Innovation Technology
In Enhancing Teaching And Learning Of Knowledge Using Ict
Methods, International Journal Of Contemporary Research In
Computer Science And Technology (Ijcrcst) E-Issn: 2395-5325
Volume3, Issue 4,P.No-10-13, April ’2017
5.Dr.C K Gomathy, Article: Supply chain-Impact of importance and
Technology in Software Release Management, International Journal
of Scientific Research in Computer Science Engineering and
International Journal of Scientific Research in Engineering and Management (IJSREM)
Volume: 06 Issue: 11 | November - 2022 Impact Factor: 7.185 ISSN: 2582-3930
© 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM16833 | Page 6
Information Technology ( IJSRCSEIT ) Volume 3 | Issue 6 | ISSN :
2456-3307, P.No:1-4, July-2018.
6.C K Gomathy and V Geetha. Article: A Real Time Analysis of
Service based using Mobile Phone Controlled Vehicle using DTMF
for Accident Prevention. International Journal of Computer
Applications 138(2):11-13, March 2016. Published by Foundation
of Computer Science (FCS), NY, USA,ISSN No: 0975-8887
7.C K Gomathy and V Geetha. Article: Evaluation on Ethernet
based Passive Optical Network Service Enhancement through
Splitting of Architecture. International Journal of Computer
Applications 138(2):14-17, March 2016. Published by Foundation
of Computer Science (FCS), NY, USA, ISSN No: 0975-8887
8.C.K.Gomathy and Dr.S.Rajalakshmi.(2014), "A Software Design
Pattern for Bank Service Oriented Architecture", International
Journal of Advanced Research in Computer Engineering and
Technology(IJARCET), Volume 3,Issue IV, April 2014,P.No:1302-
1306, ,ISSN:2278-1323.
9.C. K. Gomathy and S. Rajalakshmi, "A software quality metric
performance of professional management in service oriented
architecture," Second International Conference on Current Trends in
Engineering and Technology - ICCTET 2014, 2014, pp. 41-47, doi:
10.1109/ICCTET.2014.6966260.
[11] Dr.C K Gomathy, V Geetha ,T N V Siddartha, M Sandeep , B
Srinivasa Srujay Article: Web Service Composition In A Digitalized
Health Care Environment For Effective Communications, Published
by International Journal of Advanced Research in Computer
Engineering & Technology (IJARCET) Volume 5 Issue 4, April
2016, ISSN: 2278 – 1323.
[12]C.K.Gomathy.(2010),"Cloud Computing: Business
Management for Effective Service Oriented Architecture"
International Journal of Power Control Signal and Computation
(IJPCSC), Volume 1, Issue IV, Oct - Dec 2010, P.No:22-27, ISSN:
0976-268X .
[13]Dr.C K Gomathy, Article: A Study on the recent Advancements
in Online Surveying , International Journal of Emerging
technologies and Innovative Research ( JETIR ) Volume 5 | Issue 11
| ISSN : 2349-5162, P.No:327-331, Nov-2018
[14]Dr.C.K.Gomathy,C K Hemalatha, Article: A Study On
Employee Safety And Health Management International Research
Journal Of Engineering And Technology (Irjet)- Volume: 08 Issue:
04 | Apr 2021
[15] Dr.C K Gomathy, V Geetha , T.Jayanthi, M.Bhargavi, P.Sai
Haritha Article: A Medical Information Security Using
Cryptosystem For Wireless Sensor Networks, International Journal
Of Contemporary Research In Computer Science And Technology
(Ijcrcst) E-Issn: 2395-5325 Volume3, Issue 4, P.No-1-5,April ’2017
[16] C.K.Gomathy and Dr.S.Rajalakshmi.(2014), "Service Oriented
Architecture to improve Quality of Software System in Public
Sector Organization with Improved Progress Ability", Proceedings
of ERCICA-2014, organized by Nitte Meenakshi Institute of
Technology, Bangalore. Archived in Elsevier Xplore Digital
Library, August 2014, ISBN:978-9-3510-7216-4.
[17] Parameshwari, R. & Gomathy, C K. (2015). A Novel Approach
to Identify Sullied Terms in Service Level Agreement. International
Journal of Computer Applications. 115. 16-20. 10.5120/20163-
2253.
[18] C.K.Gomathy and Dr.S.Rajalakshmi.(2014),"A Software
Quality Metric Performance of Professional Management in Service
Oriented Architecture", Proceedings of ICCTET’14, organized by
Akshaya College of Engineering, Coimbatore. Archived in IEEE
Xplore Digital Library, July 2014,ISBN:978-1-4799-7986-8.
[19] C.K.Gomathy and Dr.S.Rajalakshmi.(2011), "Business Process
Development In Service Oriented Architecture", International
Journal of Research in Computer Application and Management
(IJRCM) ,Volume 1,Issue IV, August 2011,P.No:50-53,ISSN :
2231-1009
8. Authors Profiles
1.P.Gopala Krishna Reddy Student , B.E. Computer Science and
Engineering, Sri Chandrasekharendra SaraswathiViswa
Mahavidyalaya deemed to be university, Enathur, Kanchipuram,
India. His Area of Interest in Big Data Analytics.
2.P.Sai Srinivas Student, B.E. Computer Science and
Engineering, Sri Chandrasekharendra SaraswathiViswa
Mahavidyalaya deemed to be university, Enathur, Kanchipuram,
India. His Area of Interest in Big Data Analytics.
3.T.Karthikeya Student, B.E. Computer Science and Engineering,
Sri Chandrasekharendra SaraswathiViswa Mahavidyalaya
deemed to be university, Enathur, Kanchipuram, India. His Area
of Interest in Big Data Analytics.
4.R.Uday Kumar Student, B.E. Computer Science and
Engineering, Sri Chandrasekharendra SaraswathiViswa
Mahavidyalaya deemed to be university, Enathur, Kanchipuram,
India. His Area of Interest in Big Data Analytics.
5. Dr. C.K. Gomathy is Assistant Professor in Computer Science
and Engineering at Sri Chandrasekharendra Saraswathi Viswa
Mahavidyala, Enathur, Kanchipuram, India. Her area of interest:
Software Engineering, Web Services, Knowledge Management
and IoT
