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Data Retrieval and Gaining Optimistic Nepotism Samples

ISSN-2223-4985
Journal from gdlhub / 2012-06-22 14:44:26
Oleh : SarathChand P.V , Balraju M , Bhukya Shankar Nayak , Rambabu pemula , Suryaprakash G, International Journal of Information and Communication Technology Research
Dibuat : 2012-06-22, dengan 1 file

Keyword : Biased Samples, hits, Randomization, Data assets, Customization, Streams
Url : http://esjournals.org/journaloftechnology/archive/vol1no3/vol1no3_4.pdf
Sumber pengambilan dokumen : Internet

Data ware house mostly concentrates on transactional processing and operational systems. Data mining tools helps in

automating day to day operations of organizations where data is locked up with in the each transactional system for

inaccessible of the required data sample. In accessing these situations, sometimes data mining tools may be disconnected to

the data ware house. The data is locked up with in the each transaction processing system and could not be used effectively

for organization wide information retrieval and decision support functions. To avoid these problems a new algorithm was

discovered whereby the organizations extract the data samples from their informational assets through the use of spatial

stores called Data ware houses. The algorithm which is described in this paper is a subject oriented, integrated, time variant

and non-volatile. It is created by both the internal systems and external sources of data.

Deskripsi Alternatif :

Data ware house mostly concentrates on transactional processing and operational systems. Data mining tools helps in

automating day to day operations of organizations where data is locked up with in the each transactional system for

inaccessible of the required data sample. In accessing these situations, sometimes data mining tools may be disconnected to

the data ware house. The data is locked up with in the each transaction processing system and could not be used effectively

for organization wide information retrieval and decision support functions. To avoid these problems a new algorithm was

discovered whereby the organizations extract the data samples from their informational assets through the use of spatial

stores called Data ware houses. The algorithm which is described in this paper is a subject oriented, integrated, time variant

and non-volatile. It is created by both the internal systems and external sources of data.

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