CASE support for collaborative modelling: re-engineering conceptual modelling techniques to exploit the potential of CASE tools

1994 ◽  
Vol 9 (4) ◽  
pp. 183 ◽  
Author(s):  
Simon McGinnes
1995 ◽  
Vol 10 (3) ◽  
pp. 269-300 ◽  
Author(s):  
John K. C. Kingston ◽  
Jim G. Doheny ◽  
Ian M. Filby

AbstractThe KADS methodology and its successor, CommonKADS, have gained a reputation for being useful approaches to building knowledge-based systems in a manner which is both systematic and well documented. However, these methods require considerable effort to use them completely. It has been suggested that automated support for KADS or CommonKADS users, in the form of “knowledge engineering workbenches”, could be very useful. These tools would provide computerised assistance to knowledge engineers in organising and representing knowledge, in a similar fashion to the support which CASE tools provide for software engineers. To provide support for KADS or CommonKADS, the workbenches should provide specific support for the modelling techniques recommended by these methods, which are very detailed in the representation and analysis stages of knowledge engineering. A good knowledge engineering workbench should also be easy to use, should be robust and reliable, and should generate output in a presentable format.This paper reports on an evaluation of two commercially available workbenches for supporting the KADS approach: KADS Tool from ILOG and Open KADS Tool from Bull. This evaluation was carried out by AIAI as part of the CATALYST project, funded by the European Community's ESSI programme, which aimed to introduce CommonKADS to two technology-oriented companies. Information is also presented on two other workbenches: the CommonKADS workbench (which will soon become commercially available) and the VITAL workbench. The results show various strengths and weaknesses in each tool.


2019 ◽  
Vol 246 ◽  
pp. 27-41 ◽  
Author(s):  
Fateme Zare ◽  
Sondoss Elsawah ◽  
Ali Bagheri ◽  
Ehsan Nabavi ◽  
Anthony J. Jakeman

Author(s):  
Rohith Sothilingam ◽  
Eric Yu ◽  
Arik Senderovich

Integrating machine learning (ML) applications into business settings presents challenges for many organizations despite rapid advances in ML technologies. There is a lack of systematic guidance in integrating ML into business applications. Conceptual modelling techniques have been used widely to analyze and enhance information systems. This paper outlines how conceptual modelling can be used to help organizations design their processes in order to fit their different needs when integrating ML into business applications. We identify the characteristics and challenges of ML as well as demonstrate how conceptual modelling can be applied to enhance ML integration processes and assist them in meeting these challenges.


1992 ◽  
Vol 139 (5) ◽  
pp. 353 ◽  
Author(s):  
J. Pelayo ◽  
J. Paniello ◽  
N. Gisin ◽  
J.W. Burgmeijer ◽  
M. Blondel ◽  
...  

1999 ◽  
Vol 38 (01) ◽  
pp. 50-55 ◽  
Author(s):  
P. F. de Vries Robbé ◽  
A. L. M. Verbeek ◽  
J. L. Severens

Abstract:The problem of deciding the optimal sequence of diagnostic tests can be structured in decision trees, but unmanageable bushy decision trees result when the sequence of two or more tests is investigated. Most modelling techniques include tests on the basis of gain in certainty. The aim of this study was to explore a model for optimizing the sequence of diagnostic tests based on efficiency criteria. The probability modifying plot shows, when in a specific test sequence further testing is redundant and which costs are involved. In this way different sequences can be compared. The model is illustrated with data on urinary tract infection. The sequence of diagnostic tests was optimized on the basis of efficiency, which was either defined as the test sequence with the least number of tests or the least total cost for testing. Further research on the model is needed to handle current limitations.


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