0000000001059279
AUTHOR
Giovanni Aiello
Inferring Business Rules from Natural Language Expressions
This paper proposes a mapping technique for automatically translating rules expressed in a format based on natural language, i.e. Semantics of Business Vocabulary and Business Rules (SBVR) standard, into production rules that can be executed by a computer (i.e. Rule engine). The proposed approach achieves a twofold purpose: on the one hand non IT skilled people (i.e. Domain expert) can effectively focus on business rules definition by using statements in natural language, and on the other hand the IT staff will have to manage business rules in a format ready to be executed by a rule engine. The main goal is to overcome some weaknesses in the software development process that could produce i…
The Random Neural Network Model for the On-line Multicast Problem
In this paper we propose the adoption of the Random Neural Network Model for the solution of the dynamic version of the Steiner Tree Problem in Networks (SPN). The Random Neural Network (RNN) is adopted as a heuristic capable of improving solutions achieved by previously proposed dynamic algorithms. We adapt the RNN model in order to map the network characteristics during a multicast transmission. The proposed methodology is validated by means of extensive experiments.