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El autor Flavia Santoro ha publicado 2 artículo(s):

1 - Modelling Citizen Letters for Public Services automation

The publication and, when it is possible, automation of public services on Internet provides advantages for citizens and governance. The former because promotes the transparency and control over governance actions and avoids unneeded presencial inquiries and the latter because information systems help to decrease human resources costs. A number of efforts have been performed by public administrations to provide precise service information online. As this service information is incrementally published, manual interaction to navigate and query these services becomes a difficult task that automated mechanisms could support based on service catalogs. In this paper we introduce an ongoing work proposing the use of ontologies to enable the automated processing -i.e. search and validation- of these service catalogs.

Autores: Antonio Manuel Gutiérrez / Fernanda Massena / Claudia Cappelli / Manuel Resinas / Flavia Santoro / Antonio Ruiz-Cortés / 
Palabras Clave:

2 - Context-Aware Process Performance Indicator Prediction

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phaseguides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.This paper was published in IEEE Access, 2020, Vol. 8, pp. 222050 – 222063, doi: 10.1109/ACCESS.2020.3044670

Autores: Alfonso E. Márquez-Chamorro / Kate Revoredo / Manuel Resinas / Adela Del-Río-Ortega / Flavia Santoro / Antonio Ruiz-Cortés / 
Palabras Clave: Business Process Management - context-awareness - predictive monitoring - process indicator prediction - Process Mining