Abstract
In this paper we describe how we use the BIER data model to design the governmental office information system KIS (Kanzlei-Informations-System). KIS has been designed and implemented on our institute in cooperation with Nixdorf Austria. During our work we soon find out that the behavior (state transition) respectively the historical states of the document can hardly be modelled with a relational data model. Therefore we have used our BIER-Model, a framework of modelling static components and dynamic processes in an information system. To model the static component we use an extendet Entity-Relationship diagram and for the dynamic model we use a Petri net based graph representation.
The experiences with the BIER-Model show that the design of a governmental office information system can be supported in a very efficient manner and, because of the integrity management facilities, the schema created are always tested about the correctness. Furthermore the graphic method allows a step-wise extension of defined schemas.
| Original language | German (Austria) |
|---|---|
| Title of host publication | Governmental and Municipal Information Systems, II |
| Editors | Roland Traunmüller |
| Publisher | Elsevier Science Publishers B.V. (North Holland), IFIP |
| Pages | 57-71 |
| Number of pages | 15 |
| ISBN (Print) | 0-444-89470-5 |
| Publication status | Published - 1991 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 16 Peace, Justice and Strong Institutions
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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