Scalable Web mining architecture for backward induction in data warehouse environment

Author

Joo, Dongkwon ; Moon, Songchun

Author_Institution

Graduate Sch. of Manage., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea

Volume

1

fYear

2001

fDate

2001

Firstpage

8

Abstract

For Web mining, the biggest problem is the scarcity of data. To overcome the problem and prepare as much needed data as possible for business intelligent information, we propose backward induction in Web mining. Web mining itself is an iterative process where data mining techniques are used back and forth and iteratively. To support backward induction and Web mining characteristics, the scalable Web mining architecture in a data warehouse environment is proposed. The proposed Web mining architecture has three kinds of scalabilities. These are: the scalabilities of operational database, the scalabilities of data model and the scalabilities of data mining engines. By implementing the scalable Web mining architecture with three kinds of scalabilities in a data warehouse environment to support backward induction procedures, we can extract business intelligent information from Web mining

Keywords

business data processing; data mining; data models; data warehouses; information resources; backward induction procedures; business intelligent information; data mining engines; data mining techniques; data model; data warehouse environment; iterative process; operational database; scalable Web mining architecture; Data mining; Data warehouses; Databases; Moon; Scalability; Service oriented architecture; Technology management; Telephony; Web mining; Web sites;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2001. Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology

Print_ISBN

0-7803-7101-1

Type

conf

DOI

10.1109/TENCON.2001.949541

Filename

949541