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Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models (Summary)

Organizations execute daily activities to meet their objectives. The performance of these activities can be fundamental for achieving a business objective, but they also imply the assumption of certain security risks that might go against a company’s security policies. A risk may be defined as the effects of uncertainty on the achievement of the goals of a company, some of which can be associated with security aspects (e.g., data corruption or data leakage). The execution of the activities can be choreographed using business processes models, in which the risk of the entire business process model derives from a combination of the single activity risks (executed in an isolated manner). In this paper, the problem of automatic security risk management in the current BPMS is addresses. First, a formalization of the risk elements according to process models is included. These elements are supported as a BPMN 2.0 extension of risk information that is analyzed to determine nonconformance regarding risk goals. In addition, a diagnosis of the risk associated with the activity responsible for the nonconformance is also carried out. To this end, the proposal applies mechanisms based on the model-based diagnosis in which activities are in nonconformance with regard to the acceptable level of risk. The automation of diagnosis is carried out using artificial intelligence techniques based on constraint programming. The proposal is supported by the implementation of a plug-in that enables the graphical specification of the extension and the automation of the verification and diagnosis process. To the best of our knowledge, this is the first published work that addresses the risk-aware design of business processes with automatic techniques.

Time Prediction on Multi-perspective Declarative Business Processes

Abstract. Process-aware information systems (PAISs) are increasingly
used to provide
flexible support for business processes. The support given
through a PAIS is greatly enhanced when it is able to provide accurate
time predictions which is typically a very challenging task. Predictions
should be (1) multi-dimensional and (2) not based on a single process in-
stance. Furthermore, the prediction system should be able to (3) adapt to
changing circumstances, and (4) deal with multi-perspective declarative
languages (e.g., models which consider time, resource, data and control
flow perspectives). In this work, a novel approach for generating time
predictions considering the aforementioned characteristics is proposed.
For this, rst, a multi-perspective constraint-based language is used to
model the scenario. Thereafter, an optimized enactment plan (represent-
ing a potential execution alternative) is generated from such a model
considering the current execution state of the process instances. Finally,
predictions are performed by evaluating a desired function over this en-
actment plan. To evaluate the applicability of our approach in practical
settings we apply it to a real process scenario. Despite the high com-
plexity of the considered problems, results indicate that our approach
produces a satisfactory number of good predictions in a reasonable time.

This research has been supported by the Spanish MINECO R&D Projects Pololas TIN2016-76956-C3-2-R and PLAN MINER TIN2015-71618-R.

Towards a new Tool for Managing Declarative Temporal Business Process Models

Business processes which require a high flexibility are com- monly specified in a declarative (e.g., constraint-based) way. In general, offering operational support (e.g., generating possible execution traces) to declarative business process models entails more complexity when compared to imperative modeling alternatives. Such support becomes even more complex in many real scenarios where the management of complex temporal relations between the process activities is crucial (i.e., the temporal perspective should be managed). Despite the needs for enabling process flexibility and dealing with temporal constraints, most existing tools are unable to manage both. In a previous work, we then proposed TConDec-R, which is a constraint-based process modeling lan- guage which allows for the specification of temporal constraints. In this paper we introduce the basis and a prototype of a constraint-based tool with a client/server architecture for providing operational support to TConDec-R process models.

Predicciones en Procesos de Negocio Declarativos

La generación de predicciones sobre instancias de procesos de negocio permite anticipar problemas, evitar el incumplimiento de restricciones de una manera proactiva, y tomar decisiones sobre prioridades y restricciones al enfrentarse a eventos inesperados, e.g., retrasos. Sin embargo, elaborar una predicción es una tarea compleja en la mayoría de los casos ya que se deben tener en cuenta múltiples instancias y recursos, es necesario adaptar dichas predicciones a circunstancias cambiantes, y hay que tener en cuenta distintas dimensiones, no sólo el tiempo. En este contexto, el presente trabajo propone una propuesta novedosa para generar predicciones sobre un conjunto de instancias en ejecución relacionadas con un modelo declarativo de un proceso de negocio. Dicha propuesta consiste en generar un plan de ejecución optimizado a partir del modelo declarativo y del estado de las instancias en ejecución. Tras ello, la predicción se genera evaluando la función que se desea predecir sobre el plan de ejecución generado. La presente propuesta ha sido evaluada utilizando un modelo de proceso de un escenario real que incluye restricciones temporales, de datos, de recursos y de control-flow que lo dotan de una alta complejidad. Los prometedores resultados obtenidos alientan a continuar los trabajos en escenarios con características diferentes que permitan extender la validez de la propuesta.