DocumentCode :
3080309
Title :
Modified and improved conceptual framework to organize large volume of data
Author :
Anusha, R. ; Krishnan, Nikhil
Author_Institution :
Centre for Inf. Technol. & Eng., Manonmaniam Sundaranar Univ., Tirunelveli, India
fYear :
2013
fDate :
26-28 Dec. 2013
Firstpage :
1
Lastpage :
5
Abstract :
A Modified improved conceptual framework is proposed in this paper for organizing the enormous volume of data having business information using data mining techniques to retrieve information and knowledge useful in supporting complex decision-making processes. In this article Using JSON because faster than XML. A heuristic approach for organizing business data is adopted, which allows us to create, confirm, or contradict a hypothesis. This is accomplished through the use of intelligent agents that act as conceptual “Data Crowed-Puller” (DCP). These DCPs attract fundamental pieces of business information. The essential part of the design fully support queries, both ad-hoc and long standing, which also act as DCP attracting the relevant information that a human analyst needs to estimate the validity of the hypothesis.
Keywords :
XML; business data processing; data mining; organisational aspects; DCP; JSON; XML; business data organisation; business information; data crowed puller; data mining techniques; data volume; decision-making processes; improved conceptual framework; intelligent agents; modified conceptual framework; Artificial intelligence; Business; Data mining; Distributed databases; Intelligent agents; Organizing; XML; Business Intelligence; Data mining; Hypothesis; Intelligent Agents; Modified improved conceptual frame work;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on
Conference_Location :
Enathi
Print_ISBN :
978-1-4799-1594-1
Type :
conf
DOI :
10.1109/ICCIC.2013.6724293
Filename :
6724293
Link To Document :
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