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author:

Chen, X. (Chen, X..) [1] | Guo, C. (Guo, C..) [2]

Indexed by:

Scopus

Abstract:

This paper employs Data Warehouse theory, theory of Decision Support System, and the ASP.net technology to implement the Power Marketing DSS (Decision-making Support System) and put for ward the realization of entire system architecture. In the data layer, various data from Applications which are running for the Power Business are integrated to establish the data warehouse, and then we build the multi-dimensional data cube to provide online analytical processing. In application layer, the Webservice technology as well as its Registration Model and query technology based on UDDI registry will be introduced to Power consumption for ecasting module. The for ecasting models include Quadric Exponential Smoothing prediction model, Curve Fitting, Multiple Linear Regression Model and the Gray Model, each model will be packaged into a Web service, so the models can be called through the network for Distributed Collaborative for ecasting. Through these technologies, a complete and wellestablished Power Marketing DSS was created with following functions: on-line analysis, report service, distributed collaborative power for ecasting models work in parallel, etc. © 2009 IEEE.

Keyword:

DSS; Multi-dimensional data cube; Power Consumption for ecasting model; WebService

Community:

  • [ 1 ] [Chen, X.]Department of Computer Science and Technology, Fuzhou University, Fuzhou, Fujian, 350002, China
  • [ 2 ] [Guo, C.]Department of Computer Science and Technology, Fuzhou University, Fuzhou, Fujian, 350002, China

Reprint 's Address:

  • [Guo, C.]Department of Computer Science and Technology, Fuzhou University, Fuzhou, Fujian, 350002, China

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Source :

Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2009

Year: 2009

Page: 319-324

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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