Download e-book for kindle: Advanced Information Systems Engineering: 10th International by Colette Rolland (auth.), Barbara Pernici, Costantino Thanos

By Colette Rolland (auth.), Barbara Pernici, Costantino Thanos (eds.)

This ebook constitutes the refereed court cases of the tenth overseas convention on complicated info structures Engineering, CAiSE'98, held in Pisa, Italy, in June 1998.
The e-book offers 21 revised complete papers including one invited survey; the papers have been chosen from a complete of 102 submissions. The booklet is split into sections on info structures layout, information warehouses and extensible database administration structures, workflow administration and groupware, reuse, software layout and WWW, and business experiences.

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Additional resources for Advanced Information Systems Engineering: 10th International Conference, CAiSE'98 Pisa, Italy, June 8–12, 1998 Proceedings

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Int. Symposium on Requirements Engineering, 1993. 38. , Maestro II- das intergrierte CASE-system von Softlab, CASE systeme and Werkzeuge (Ed. H. Balzert) BI Wissenschaftsverlag, pp 319-336,1991 39. R. S Michalski, A Theory and Methodology of Inductive Learning, Atificial Intelligence, Vol 20, No 2, 1983 40. M. Oivo, V. R. Basili, representing software engineering model : the TAME goal oriented approach, IEEE Transactions on Software Engineering, Vol. 18 , N° 10, 1992. 41. T. W. Olle, J. Hagelstein, I.

Ports, C. Richter, A Review of the State of the Practice in Requirements Modeling, Proc. Int. Symposium on Requirements Engineering, 1993. , Maestro II- das intergrierte CASE-system von Sofllab, CASE systeme and Werkzeuge (Ed. H. Balzert) BI Wissenschaftsverlag, pp 319-336,1991 23 Mic83 R. S Michalski, A Theory and Methodology of Inductive Learning, Atificial Intelligence, Vo120, No 2, 1983 Oiv92M. Oivo, V. R. Basili, representing software engineering model : the TAME goal oriented approach, IEEE Transactions on Software Engineering, Vol.

In order to do inductive improvement, there must exist a mapping between elements of the trace and the concepts of the process model. Deductive learning exploits Case-based reasoning. Thus, it solves new problems by adapting solutions that have been utilised to resolve past problems Rie89. Case based reasoning when applied to process performance calls for the existence of a repository of cases. Deductive process improvement aims at adding new cases in the repository by examining process performances.

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