Use of Grammars and Machine Learning in ETL Systems That Control Load Balancing Process

Author

Gorawski, M. ; Gorawski, M. ; Dyduch, Stanislaw

Author_Institution

Inst. of Comput. Sci., Silesian Univ. of Technol., Gliwice, Poland

fYear

2013

fDate

13-15 Nov. 2013

Firstpage

1709

Lastpage

1714

Abstract

The following paper introduces the performance evaluation of effective components in queries analysis and classification modules, as a part of the ETL management system. The main research focuses on usage of context-free grammars while analyzing queries arriving to the system. Also in the paper we present use of several methods of machine learning in a query processing time prediction. In previous research classifiers like Ridor were used, while query analysis was based on regular expression. Obtained results were quite encouraging and led to solutions presented in the paper.

Keywords

context-free grammars; database management systems; learning (artificial intelligence); pattern classification; query processing; resource allocation; ETL management system; ETL systems; classification modules; component performance evaluation; context-free grammars; extraction-transformation-load process; load balancing process control; machine learning; query analysis; query processing time prediction; regular expression; Algorithm design and analysis; Classification algorithms; Data warehouses; Grammar; Load management; Query processing; Syntactics; Lemat system; load balancing; performance evaluation; query processing; query processing prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), 2013 IEEE 10th International Conference on

Conference_Location

Zhangjiajie

Type

conf

DOI

10.1109/HPCC.and.EUC.2013.243

Filename

6832125