IRJET- Providing In-Database Analytic Functionalities to Mysql : A Proposed System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 8063
Providing in-Database Analytic Functionalities to MySQL: A Proposed
System
Deeksha M Kumar1, Harshitha M1, Muhammed Faheem E1 , Nesar N1,Suhaas KP2
1Dept. of ISE, The National Institute of Engineering, Mysore
2Asst.Professor, Dept. of ISE, The National Institute of Engineering, Mysore, Karnataka, India
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Abstract -Data analytics hastakenthetechworldbystorm.
Given the explosion of data in today’s world, analytics is
needed now more than ever in order for businesses to plan
their next move. Conventional Analytic frameworks involve
data migration to and from the data storeandananalytictool
which proves time consuming with the bulk of data. Our
proposed system intends to implement analytic functions
within MySQL in order to cut down on the time consumed in
the analytics process.
Key Words: Database, analytics, Machine Learning,
MySQL and UDF.
1. INTRODUCTION
Quoting Christopher Ré et al in[1],the question"Isthere
anything fundamentally different about building database
system that use machine learning or are designedtosupport
machine learning?" makes you think about how one can go
about achieving a database with machine learning like
functionalities or analytic functionalities. A traditional
method of data analytics involves movement of pre-
processed data from the data store, generally a database, to
analytic software. The software then performs analytic
functionalities following which, data is transferred back to
the data store [2]. One can go about their task with this
method if he were dealing with a small amountofdata.Given
the increasing volume of data in today’s world, the
transportation of data from the data store to the analytic
software seems to be one of the biggest challenges as it is
time consuming and puts a strain in the network being used
to transfer the data.
1.1 MySQL
MySQL is one of the most widely used RDBMS’s in the
world. It is known as a fully featured RDBMS and it is stable.
It is also a multithreaded based server which means it is
extremely fast and can support multiple users at once. Each
user gets their own thread when they establish a connection
with the server.MySQLisalsoplatformindependent.Itworks
on almost all platforms. MySQL provides security to data at
business level. Data is stored in tables. MySQL also includes
Application Programming Interfaces or APIsforPerl,Python,
Java. SQL is easy to pick up and use. The SQL commands are
pretty concise and easy to learn.
1.2 Machine learning and analytics
Machine learning is a sub-fieldofArtificial intelligence.It
concerns enabling computers to learn without being
expressly customized. Throughout the years, Artificial
intelligences’ ubiquityandrequesthaspositivelybeenon the
ascent. Machine Learning employs algorithmsandstatistical
models in order to build a mathematical model of data. This
data is called training data and is used to make predictions.
The types of machine learning are supervised learning,
unsupervised learning, and reinforcement learning.
The supervised learning basically has two types:
regression and classification. Regression algorithms are
employed to predict continuous numeric values. Some
common regression algorithms include linear regression, k
nearest neighbors.
Machine learning consists of 3 steps preprocessing,
where raw data is normalized and all inconsistencies are
corrected; training and verification, where cleaned data is
trained; classification, where the trained model is used to
classify data. Generally pre-processing is done within the
database following which the data is migratedtotheanalytic
tool over a network where ML algorithms are applied and
analytics is performed. Data is then migrated back to the
database [3].
2. EXISTING SYSTEM
Conventional data analytics involves the transferofpre-
processed data from the data store to the analytic tool. The
data may be stored in relational databases like MySQL,
columnar databases like MongoDB, CSV files or XML files.
The Traditional data processing for analytics or prediction
requires the user to have the knowledge of programming
languages like Python and R. Apart from that, users need to
write an external application using programming language
and load the data into the application from database.
Analytic functions are then applied to the data by
the tool following which data is transferred back to the data
store. Transferring data from the data store to the analytic
tool is not much of a hassle with small data volumes. But
when it comes to large data volumes, we are posed with the
problem of increase in network traffic. This load on the

network could minimise network performanceandlengthen
the overall time period of the process.
Fig 1: Existing System
Third party applications offer little to no data security.
With the vastness of the volume of data being analysed,
access to such data could prove disastrous if the data was
intended to be confidential. If at all it provides
confidentiality, there will be overhead of encrypting and
decrypting the data during the data transfer.
3. PROPOSED SYSTEM
The main objective of this system is to eliminate the
need of the external applications whichimportdata fromthe
database through socket connections and using MySQL
connectors. This system integrates Machine Learning
algorithms like linear regression withintheMySQL database
server itself so that there will be no need for data migration
to and from application and database server. Thus it
eliminates the risk of data leakage or data loss [4].
As MySQL is relational database management system,
the RDBMS can prevent data corruption through ACID or
Atomicity, properties; it can automatically manage data
storage for the user and make data easier to reason about by
enforcing a rigid schema. In addition, the RDBMS can
perform efficient execution on larger-than-memory data by
only loading required columns.Thissystemusesin-database
processing methods, which does the computationwherethe
data resides.
Fig 2: Comparison of Existing and Proposed System
In our proposed system, the best way to plug the
functionalities or the algorithms of ML is by using User
Defined Functions (UDF) because of its own advantages.
UDFs are much easier to develop than is hacking raw code
into the MySQL server. If our function were hacked into the
server, we would need to change the MySQL source every
time we upgraded, which is never easy. MySQL code base
evolves quite rapidly andtoimplementthesamefunction we
would need different code changes in every new version.
They will not even need to be recompiled when the server is
upgraded. And other reason to use UDF is that they are
designed for development speed, the API or Application
Program Interface is easy to access, and compilationismuch
quicker than rebuilding the entire server just to add a tiny
function.
Since it is in-database processing, it reduces the network
load and traffic, which can be effective in cloud data centres
where thousands of rows of data should be moved from one
geographical location to another and eliminatingtheneedto
encrypt and decrypt the data during transmission, which is
the overhead in traditional data processing or existing
systems.
It also eliminates the need to know python and R for
doing predictive analysis in the application and provides
these features in Structured Query Language itself, which is
widely known by the people. And UDF can be installed
whenever it is needed or can be dropped when it is not
needed thus providing the opportunitytoupgradeormodify
the functions as needed by the user, by linking the binaries
to MySQL server even during run time.
This system is expected to give considerableadvantages
over existing systems and provide users with easy and
simple way to do predictive analytics.

3.1 Linear Regression
To achieve simple predictive analysis, the linear
regression with one variable algorithmis used.Simplelinear
regression is a statistical method that allows us to
summarize and study relationshipsbetweentwocontinuous
(quantitative) variables, with one variable, x, regarded as
the predictor, explanatory, or independent variable. The
other variable y, is regarded as the response, outcome,
or dependent variable.
The formula used for predicting the value of outcome
variable is Y=mX+C ,where m is the slope of the line and C is
constant value, Y is dependent variable andXisindependent
variable.
3.2 User-Defined Functions –MySQL
Fig 3: Flowchart of Working of Proposed System
User-Defined functions are the best way and the easiest
way to plug the new functions to existing MySQL server. It
works faster than the stored procedures [5] ,which is other
way of plugging the new functions to MySQL server. User-
Defined functions can be dynamically installed by the
CREATE FUNCTION command and uninstalled by DROP
FUNCTION command.Toimplementthefunction, we needto
design the aggregate type of UDF and the linear regression
with one variable algorithm into the UDF methods [3].
The Linear_reg_init() method is used to check for
metadata, verify the required number of arguments and
allocate the memory if needed by the function.
Linear_reg_deinit() method is used to free the memory used
by function after the execution of the query.
Linear_reg_add() and Linear_reg_clear methods are used to
add up each row in the database and clear the values after
each group of values mentioned in the query. Linear_reg()
method does the actual work of the function and linear
regression with one variable algorithm is implemented in
this method.
4. CONCLUSIONS
Data migration and network trafficaretwoofthebiggest
concerns and reasons for inefficiency in conventional
analytic workflows. This system proposes to cut down on
data migration and network traffic and reduce them by
implementing analytic functionalities within MySQL itself.
Knowledge of SQL commands is only necessary in order to
operate the system. As MySQL is a widely used RDBMS, our
system could be utilised by many in the IT sphere.
The system will consume a smaller amount of time
overall in comparison with existing systems. The ease of
utility guaranteed by UDFs is also an added advantage.
ACKNOWLEDGEMENT
We are proud and grateful to have been given an
opportunity to present an idea suchasours.Toourprincipal,
Dr. G S Ravi, we are extremely grateful for his kind
permission to carry out our work. Our sincere thanks to the
Department of Information Science and Engineering, NIE,
Mysuru.
We are indebted to Mr. Suhaas K P, our project guide for
his constant support and valuable insights to the project.
When we were lost, he helped us find a path and we will be
ever grateful.
REFERENCES
[1] C. Re, D. Agrawal, M. Balazinska, M. Cafarella, M. Jordan,
T. Kraska, R. Ramakrishnan, “Machine Learning and
Databases: The Sound of ThingstoComeora Cacophony
of Hype?” SIGMOD ’15, May 31-June 04, 2015, Victoria,
Australia. ACM 978-1-4503-2758-9/2015
[2] U. Syed, S. Vassilvitskii, “SQML: Large-scale in-database
machine learning with pure SQL” SoCC '17 Proceedings
of the 2017 Symposium on Cloud Computing, Santa
Clara, CA, USA

[3] M. Raasveldt, P. Holanda, H. Mühleisen, S. Manegold,
”Deep Integration of Machine Learning Into Column
Stores” 21st International Conference on Extending
Database Technology (EDBT), March 26-29, 2018, ISBN
978-3-89318-078-3
[4] J. Vinish D’silva, F. De Moor, B. Kemme,” AIDA -
Abstraction for Advanced In-DatabaseAnalytics”PVLDB
2018 DOI:10.14778/3236187.3236194
[5] C. Ordonez, C. Garcia-Alvarado, “A Data Mining System
Based on SQL Queries and UDFs for Relational
Databases” CIKM '11 Proceedings of the 20th ACM
international conferenceonInformationand knowledge
management, DOI 10.1145/2063576.2064008
[6] https://www.analyticsvidhya.com

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