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Fuzzy-Rule Based Adaptive Data Warehouse: An Extension of Data Warehouse as Knowledge Warehouse

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  • Rajdev Tiwari

    (ABES Institute of Technology, India)

  • Anubhav Tiwari

    (Amity University, India)

  • Manu Pratap Singh

    (Dr. B. R. Ambedkar University, India)

Abstract

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

Suggested Citation

  • Rajdev Tiwari & Anubhav Tiwari & Manu Pratap Singh, 2012. "Fuzzy-Rule Based Adaptive Data Warehouse: An Extension of Data Warehouse as Knowledge Warehouse," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 3(1), pages 47-65, January.
    • Handle: RePEc:igg:jaec00:v:3:y:2012:i:1:p:47-65
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