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

  • A. Kurz
  • , A Min Tjoa

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

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.
Original languageEnglish
Title of host publicationProc of the 8th Int. Workshop on Database and Expert System Application 1997
Editors Roland Wagner
PublisherIEEE Computer Press
Pages627-632
Number of pages6
ISBN (Print)0-8186-8147-0
Publication statusPublished - Sept 1997

Fields of science

  • 102001 Artificial intelligence
  • 102006 Computer supported cooperative work (CSCW)
  • 102010 Database systems
  • 102014 Information design
  • 102015 Information systems
  • 102016 IT security
  • 102028 Knowledge engineering
  • 102019 Machine learning
  • 102022 Software development
  • 102025 Distributed systems
  • 502007 E-commerce
  • 505002 Data protection
  • 506002 E-government
  • 509018 Knowledge management
  • 202007 Computer integrated manufacturing (CIM)
  • 102033 Data mining
  • 102035 Data science

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