InthispaperweillustratehowtheknowledgedrivenBehaviourAnal- ysis, which has been used in the KITE.it process management framework, can support the evolution of analytics from descriptive to predictive. We describe how the methodology uses an iterative three-step process: first the descriptive knowledge is collected, querying the knowledge base, then the prescriptive and predictive knowledge phases allow us to evaluate business rules and objectives, extract unexpected business patterns, and screen exceptions. The procedure is iterative since this novel knowledge drives the definition of new descriptive an- alytics that can be combined with business rules and objectives to increase our level of knowledge on the combination between process behaviour and contex- tual information.
Knowledge Driven Behavioural Analysis in Process Intelligence / A. Azzini, P. Ceravolo, E. Damiani, F. Zavatarelli - In: Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data / [a cura di] W. van der Aalst, R. Bergenthum, J. Carmona. - [s.l] : CEUR Workshop Proceedings Vol-1371 at CEUR-WS.org, 2015 Jun. (( convegno Workshop on Algorithms & Theories for the Analysis of Event Data Satellite event of the conferences: 36th International Conference on Application and Theory of Petri Nets and Concurrency Petri Nets 2015 and 15th International Conference on Application of Concurrency to System Design ACSD 2015 tenutosi a Brussels nel 2015.
Knowledge Driven Behavioural Analysis in Process Intelligence
A. AzziniPrimo
;P. CeravoloSecondo
;E. DamianiPenultimo
;F. ZavatarelliUltimo
2015
Abstract
InthispaperweillustratehowtheknowledgedrivenBehaviourAnal- ysis, which has been used in the KITE.it process management framework, can support the evolution of analytics from descriptive to predictive. We describe how the methodology uses an iterative three-step process: first the descriptive knowledge is collected, querying the knowledge base, then the prescriptive and predictive knowledge phases allow us to evaluate business rules and objectives, extract unexpected business patterns, and screen exceptions. The procedure is iterative since this novel knowledge drives the definition of new descriptive an- alytics that can be combined with business rules and objectives to increase our level of knowledge on the combination between process behaviour and contex- tual information.File | Dimensione | Formato | |
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