One major task in business process management is that of aligning real process execution traces to a process model by (minimally) introducing and eliminating steps. Here, we look at declarative process specifications expressed in Linear Temporal Logic on finite traces (LTLf). We provide a sound and complete technique to synthesize the alignment instructions relying on finite automata theoretic manipulations. Such a technique can be effectively implemented by using planning technology. Notably, the resulting planning-based alignment system significantly outperforms all current state-of-the-art ad-hoc alignment systems. We report an in-depth experimental study that supports this claim.

On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment / DE GIACOMO, Giuseppe; Maggi, Fabrizio M.; Marrella, Andrea; Patrizi, Fabio. - 5:(2017), pp. 3555-3561. (Intervento presentato al convegno 31st AAAI Conference on Artificial Intelligence (AAAI-17) tenutosi a San Francisco, California, USA nel 4-9 February 2017).

On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment

Giuseppe De Giacomo;Fabrizio M. Maggi;ANDREA MARRELLA
;
Fabio Patrizi
2017

Abstract

One major task in business process management is that of aligning real process execution traces to a process model by (minimally) introducing and eliminating steps. Here, we look at declarative process specifications expressed in Linear Temporal Logic on finite traces (LTLf). We provide a sound and complete technique to synthesize the alignment instructions relying on finite automata theoretic manipulations. Such a technique can be effectively implemented by using planning technology. Notably, the resulting planning-based alignment system significantly outperforms all current state-of-the-art ad-hoc alignment systems. We report an in-depth experimental study that supports this claim.
2017
31st AAAI Conference on Artificial Intelligence (AAAI-17)
Business Processes; Trace Alignment; Linear Time Temporal Logic on Finite Traces; Automated Planning; Declare
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment / DE GIACOMO, Giuseppe; Maggi, Fabrizio M.; Marrella, Andrea; Patrizi, Fabio. - 5:(2017), pp. 3555-3561. (Intervento presentato al convegno 31st AAAI Conference on Artificial Intelligence (AAAI-17) tenutosi a San Francisco, California, USA nel 4-9 February 2017).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/965532
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