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Data Warehousing within Intranet: Prototype of a Web-based Executive Information System

  • A. Kurz
  • , A Min Tjoa

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

A Data Warehouse (DWH) contains a large amount of aggregated data, collected from the various operational, enterprise-wide data sources. The DWH will be locigal modeled as a virtual n-dimensional data-cube. Analyses against this n-dim data-cube allow the decision makers (e.g. executives, middle management, controllers, etc.) to view their enterprise in various different ways. This paper describes our ongoing 'WWW-EIS-DWH' research project of the development of a simple to use Executive Information System (EIS), which is complete embedded in the Web , res. an enterprise-wide network (Intranet), and based on a multidimensional medeled Data Warehouse. To accomplish our goals we implemented a generic R-OLAP (relational Online Analytical Processing) engine to extract the raw data from the given multidimensional OLAP data cubes. The 'Information Server' (IS) is responsible for the vizualisation of the retrieved OLAP data cubes in an easy understandable manner. Our user interface uses commonly used Web technology like Java, JavaScript, HTML 3.2.
OriginalspracheEnglisch
TitelProc of the 8th Int. Workshop on Database and Expert System Application 1997
Herausgeber*innen Roland Wagner
VerlagIEEE Computer Press
Seiten627-632
Seitenumfang6
ISBN (Print)0-8186-8147-0
PublikationsstatusVeröffentlicht - Sep. 1997

Wissenschaftszweige

  • 102001 Artificial Intelligence
  • 102006 Computer Supported Cooperative Work (CSCW)
  • 102010 Datenbanksysteme
  • 102014 Informationsdesign
  • 102015 Informationssysteme
  • 102016 IT-Sicherheit
  • 102028 Knowledge Engineering
  • 102019 Machine Learning
  • 102022 Softwareentwicklung
  • 102025 Verteilte Systeme
  • 502007 E-Commerce
  • 505002 Datenschutz
  • 506002 E-Government
  • 509018 Wissensmanagement
  • 202007 Computer Integrated Manufacturing (CIM)
  • 102033 Data Mining
  • 102035 Data Science

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