Publication Type:

Conference Paper

Source:

Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, Institute of Electrical and Electronics Engineers Inc., p.661-666 (2017)

ISBN:

9781509052554

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020008364&doi=10.1109%2fIC3I.2016.7918045&partnerID=40&md5=7b2f333ada1e6abf57896a7d812a775a

Keywords:

Analytical queries, Classification algorithm, Data mining, Database systems, Feature engineerings, large scale systems, Logistic regressions, Mining projects, Query processing, Real data sets, Regression analysis, Statistical packages, User Defined Functions

Abstract:

The context of this paper is to come up with an analytical query model for data categorization within DBMS. DBMS being the asset for most of the organizations, classification can help in getting better insight and control over the data. Conventionally, classification algorithms like logistic regression, KNN, etc. are applied after exporting the data out of DBMS, using non DBMS tools like R, matrix packages, generic data mining programs or large scale systems like Hadoop and Spark. However, this leads to I/O overhead since the data within DBMS is updated quite frequently and usually cannot be accommodated in the main memory. This paper proposes an alternative strategy, based on SQL and UDFs, to integrate the logistic regression for data categorization as well as prediction query processing within DBMS. A comparison of SQL with user defined functions (UDFs) as well as with statistical packages like R is presented, by experimentation on real datasets. The empirical results show the viability and validity of this approach for predicting the class of a given query. © 2016 IEEE.

Notes:

cited By 0; Conference of 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 ; Conference Date: 14 December 2016 Through 17 December 2016; Conference Code:127565

Cite this Research Publication

J. Isaac and S.a Harikumar, “Logistic regression within DBMS”, in Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, 2017, pp. 661-666.

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