Automating Business Modelling Automating Business Modelling
Advanced Information and Knowledge Processing

Automating Business Modelling

A Guide to Using Logic to Represent Informal Methods and Support Reasoning

    • USD 149.99
    • USD 149.99

Descripción editorial

Enterprise Modelling (EM) methods are frequently used by entrepreneurs as an analysis tool for describing and redesigning their businesses. The resulting product, an enterprise model, is commonly used as a blueprint for reconstructing organizations and such effort is often a part of business process re-engineering and improvement initiatives.

Automating Business Modelling describes different techniques of providing automated support for enterprise modelling methods and introduces universally used approaches. A running example of a business modelling method is included; providing a framework and detailed explanation as to how to construct automated support for modelling, allowing readers to follow the method to create similar support.

Suitable for senior undergraduates and postgraduates of Business Studies, Computer Science and Artificial Intelligence, practitioners in the fields of Knowledge Management, Enterprise Modelling and Software Engineering, this book offers insight and know-how to both student and professional.

GÉNERO
Negocios y finanzas personales
PUBLICADO
2006
30 de marzo
IDIOMA
EN
Inglés
EXTENSIÓN
339
Páginas
EDITORIAL
Springer London
VENDEDOR
Springer Nature B.V.
TAMAÑO
4.2
MB
Seriation in Combinatorial and Statistical Data Analysis Seriation in Combinatorial and Statistical Data Analysis
2022
Provenance in Data Science Provenance in Data Science
2021
Smart Systems for E-Health Smart Systems for E-Health
2021
Artificial Intelligence in Economics and Finance Theories Artificial Intelligence in Economics and Finance Theories
2020
Mining Software Engineering Data for Software Reuse Mining Software Engineering Data for Software Reuse
2020
Adaptive Resonance Theory in Social Media Data Clustering Adaptive Resonance Theory in Social Media Data Clustering
2019