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El autor Manuel Resinas, ha publicado 8 artículo(s):

1 - Monitorización predictiva de procesos de negocio basada en modelos de predicción actualizables

La monitorización predictiva de instancias de procesos de negocio en ejecución propociona acciones proactivas y correctivas para mejorar el rendimiento de los procesos y mitigar los posibles riesgos en tiempo real. Dicha monitorización permite la predicción de métricas de evaluación o indicadores del rendimiento de un proceso en ejecución. En este contexto, este trabajo define una arquitectura para el proceso de predicción de indicadores que, asimismo, contempla la posibilidad de la actualización del modelo predictivo a lo largo del tiempo.

Autores: Alfonso E. Márquez-Chamorro / Manuel Resinas, / Antonio Ruiz-Cortés / 
Palabras Clave: minería de procesos - monitorización predictiva - predicción de indicadores. - Procesos de Negocio

2 - On the feasibility of measuring performance using PPINOT in CMMN

Monitoring and measuring the performance of business processes are valuable tasks that facilitate the identification of possible improvement areas within the organisation according to the fulfillment of its strategic and business goals. A large number of techniques and tools have been developed with the aim of measuring process performance, but most of those processes are structured processes, usually defined using BPMN. The object of this paper is to identify and to analyse the feasibility of using an existing mechanism for the definition and modelling of process performance indicators (PPINOT) in a different context to structured BPMN processes; such as Cases, usually modelled using CMMN. This analysis is based on the similarities between CMMN and BPMN, and on characteristics and attributes used by PPINOT to get values from the process.

Autores: Bedilia Estrada-Torres / Adela del-Río-Ortega / Manuel Resinas, / Antonio Ruiz-Cortés / 
Palabras Clave: business processes - CMMN - performance indicators

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6 - VISUAL PPINOT: A Graphical Notation for Process Performance Indicators (Summary)

Summary of the contribution

Process performance indicators (PPIs) allow the quantitative evaluation of business processes (BPs), providing essential information for decision making. However, PPI management is not only restricted to the evaluation phase of the BPM lifecycle, but also includes a number of steps that must be carried out throughout the whole lifecycle. PPIs need to be defined, the corresponding BPs must be instrumented, PPI values have to be computed, then they can be monitored and analysed using techniques such as business activity monitoring or process mining, and finally, a PPI redefinition can be required in case of the evolution of either the associated BPs or the PPIs themselves. It is common practice today that BPs and PPIs are usually modelled separately using graphical notations for the former and natural language for the latter. This approach makes PPI definitions simple to read and write, but it hinders maintenance consistency between BPs and PPIs. It also requires their manual translation into lower–level implementation languages for their operationalisation, which is a time–consuming, error– prone task because of the ambiguities inherent to natural language definitions. In this article we present Visual ppinot, a graphical notation for defining PPIs together with BP models aimed at facilitating and automating PPI management. This is mainly achieved by means of the following features. First, Visual ppinot is based on the ppinot metamodel, which provides a precise and unambiguous definition of PPIs, thus allowing their automated processing in the different ac- tivities of the lifecycle. Second, Visual ppinot provides traceability by design between PPIs and BPs because PPIs must be explicitly connected to BP elements, thus avoiding inconsistencies and promoting their co–evolution. Finally, Visual ppinot enables a definition of PPIs that is independent of the platforms used to support the PPIs in the BP lifecycle, which reduces vendor lock–in and allows definitions of PPIs encompassing several information systems. In addition, it improves current state–of–the–art proposals in terms of expressiveness and of providing an explicit visualisation of the link between PPIs and BPs. The reference implementation, developed as a complete tool suite, has allowed its validation in a multiple-case study, in which five dimensions were studied: expressiveness, precision, automation, understandability, and traceability.

Autores: Adela del-Río-Ortega / Manuel Resinas, / Amador Durán / Beatriz Bernárdez / Antonio Ruiz-Cortés / Miguel Toro / 
Palabras Clave:

7 - Un Recorrido por los Principales Proveedores de Servicios de Machine Learning y Predicción en la Nube

Los medios tecnológicos para el consumo, producción e intercambio de información no hacen más que aumentar cada día que pasa. Nos encontramos envueltos en el fenómeno Big Data, donde ser capaces de analizar esta informa- ción con el objetivo de poder inferir situaciones del futuro basándonos en datos del pasado y del presente, nos puede reportar una ventaja competitiva que nos distinga claramente de otras opciones. Dentro de las múltiples disciplinas exis- tentes para el análisis de grandes cantidades información encontramos el Ma- chine Learning y, a su vez, dentro de este podemos destacar la capacidad predic- tiva que nos proporcionan muchas de las opciones existentes actualmente en el mercado. En este trabajo realizamos un análisis de estas principales opciones de APIs predictivas en la nube, las comparamos entre sí, y finalmente llevamos a cabo una experimentación con datos reales de la Red de Vigilancia y Control de la Calidad del Aire de la Junta de Andalucía. Los resultados demuestran que estas herramientas son una opción muy interesante a considerar a la hora de tratar de predecir valores de contaminantes que pueden afectar a nuestra salud seriamente, pudiéndose llevar a cabo acciones preventivas sobre la población afectada.

Autores: David Corral-Plaza / Juan Boubeta-Puig / Manuel Resinas, / 
Palabras Clave: API - big data - Cloud - Machine Learning - Predicción - Software as a Service

8 - A Template-based Approach for Responsibility Management in Executable Business Processes (Summary)

Summary of the Contribution

Process-oriented organisations need to manage the different types of responsi- bilities their employees may have w.r.t. the activities involved in their business processes. Despite several approaches provide support for responsibility modelling, in current Business Process Management Systems (BPMS) the only responsibility considered at run time is the one related to performing the work required for activity completion. Others like accountability or consultation must be implemented by manually adding activities in the executable process model, which is time-consuming and error-prone. This paper addresses this limitation by enabling current BPMS to execute processes in which people with different responsibilities interact to complete the activities. A metamodel based on Responsibility Assignment Matrices (RAM) is designed to model the responsibility assignment for each activity, and a template- based mechanism that automatically transforms such information into BPMN elements is developed. The approach is independent of the platform and hence, the output models can be interpreted and executed by BPMS that support BPMN. Furthermore, the original structure of the process model remains unchanged, as the templates for modelling responsibilities are defined at subprocess level. This provides transparency and does not affect the readability of the original model. As our approach does not enforce any specific behaviour but new templates can be modelled to specify the interaction that best suits the activity requirements, there is high flexibility and generalisability. Moreover, template libraries can be created and reused in different processes. We provide a reference implementation and build a library of templates for a well-known set of responsibilities.

Autores: Cristina Cabanillas / Manuel Resinas, / Antonio Ruiz-Cortés / 
Palabras Clave: