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Physical Data Warehouse Design Using Neural Network

International Journal of Computer Applications
© 2010 by IJCA Journal.
Number 3 - Article 15
Year of Publication: 2010
Mayank Sharma
Navin Rajpal

Mayank Sharma, Navin Rajpal and B.V.R.Reddy. Article: Physical Data Warehouse Design using Neural Network. International Journal of Computer Applications 1(3):86–94, February 2010. Published By Foundation of Computer Science. BibTeX

	author = {Mayank Sharma and Navin Rajpal and B.V.R.Reddy},
	title = {Article: Physical Data Warehouse Design using Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {1},
	number = {3},
	pages = {86--94},
	month = {February},
	note = {Published By Foundation of Computer Science}


Performance of the data warehouse depends on physical design. Index selection and storage of multidimensional data bases are important activities of physical designing process. Conventional indexing techniques such as bitmaps, B-trees and hash based indexing systems need large storage space for storing indexes along with data itself. Spelling variants, misspellings and transliteration differences are source of uncertainty in data with in the databases. Misspelled and distorted key values are also hard to map in present indexing systems. In this paper neural network based physical design is suggested, a class of artificial neural network known as self-organizing net is used for indexing data warehouse at physical level. Indexes of active neurons will be used for generating indexes for the data values. In conventional indexing techniques every key value is mapped to a specific point in space, while in neural network based database indexing system, every key value is mapped to a region in space. This region is a class to which the key values of similar type belong. Indexes generated through this method used optimal space for storage, as only final weight matrices after training of neurons are stored. Self-organizing net based indexing is very robust as distorted key values get indexed to right classes. Accuracy of our self-organizing net based indexing system in mapping key values with distorted keys is found to be high.


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