Effective data mining: a data warehouse-backboned architecture

Authors:
Khalil M. Ahmed;Nagwa M. El-Makky;Yousry Taha
Affiliations:
Computer Science Dept., Faculty of Engineering, Alexandria University, Alex., Egypt;Computer Science Dept., Faculty of Engineering, Alexandria University, Alex., Egypt;Computer Science Dept., Faculty of Engineering, Alexandria University, Alex., Egypt
Venue:
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
Year:
1998

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Abstract

An effective Data Mining (DM) system for mining multiple-level knowledge from Data Warehouse (DW), DB and flat files of raw data is proposed. The DW represents the backbone of the proposed architecture. Intermediate, as well as final results of mining are incorporated into the DW for efficient processing of further queries. A Markov Chain mathematical model is developed for managing data dependency and consistency in the DW. An adaptive hybrid view technique is introduced to manage storage space. DM and OLAP technologies are closely integrated. The mining and OLAP kernel includes generic analysis modules for performing a wide spectrum of applications. Active data mining is adopted to support knowledge-driven business processes. Continuously gathered business data is partitioned according to application-dependent time periods. Active mining uses these partitioned data sets to discover rules and key business indicators for each time period.