Adaptive Process Management in Highly Dynamic and Pervasive Scenarios

Process Management Systems (PMSs) are currently more and more used as a supporting tool for cooperative processes in pervasive and highly dynamic situations, such as emergency situations, pervasive healthcare or domotics/home automation. But in all such situations, designed processes can be easily invalidated since the execution environment may change continuously due to frequent unforeseeable events. This paper aims at illustrating the theoretical framework and the concrete implementation of SmartPM, a PMS that features a set of sound and complete techniques to automatically cope with unplanned exceptions. PMS SmartPM is based on a general framework which adopts the Situation Calculus and Indigolog

Direito Civil: obrigações

- Divulgação dos SUMÁRIOS das obras recentemente incorporadas ao acervo da Biblioteca Ministro Oscar Saraiva do STJ. Em respeito à Lei de Direitos Autorais, não disponibilizamos a obra na íntegra.- Localização na estante: 347(81) O48c- A 1a. edição foi publicada sob o título: Curso de direito civil. Volume II, Direito das obrigações e atos unilaterais

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

Swimmers in thin films: from swarming to hydrodynamic instabilities

We investigate theoretically the collective dynamics of a suspension of low Reynolds number swimmers that are confined to two dimensions by a thin fluid film. Our model swimmer is characterized by internal degrees of freedom which locally exert active stresses (force dipoles or quadrupoles) on the fluid. We find that hydrodynamic interactions mediated by the film can give rise to spontaneous continuous symmetry breaking (swarming), to states with either polar or nematic homogeneous order. For dipolar swimmers, the stroke averaged dynamics are enough to determine the leading contributions to the collective behaviour. In contrast, for quadrupolar swimmers, our analysis shows that detailed features of the internal dynamics play an important role in determining the bulk behaviour. In the broken symmetry phases, we investigate fluctuations of hydrodynamic variables of the system and find that these destabilize order. Interestingly, this instability is not generic and depends on length-scale.Comment: 4 pages, 2 figures, references added, typos corrected, new introductio

History-based construction of log-process alignments for conformance checking : discovering what really went wrong

Alignments provide a robust approach for conformance checking which has been largely applied in various contexts such as auditing and performance analysis. Alignment-based conformance checking techniques pinpoint the deviations causing nonconformity based on a cost function. However, such a cost function is often manually defined on the basis of human judgment and thus error-prone, leading to alignments that do not provide the most probable explanations of nonconformity. This paper proposes an approach to automatically define the cost function based on information extracted from the past process executions. The cost function only relies on objective factors and thus enables the construction of the most probable alignments, i.e. alignments that provide the most probable explanations of nonconformity. Our approach has been implemented in ProM and assessed using both synthetic and real-life data. Keywords: Conformance checking, alignments, cost function

The FeaturePrediction package in ProM : correlating business process characteristics

In Process Mining, often one is not only interested in learning process models but also in answering questions such as "What do the cases that are late have in common?", "What characterizes the workers that skip this check activity?" and "Do people work faster if they have more work?". Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for specific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. This paper reports on a tool, implemented as plug-in for ProM, that unifies these ideas and provide a general framework for deriving and correlating process characteristics. To demonstrate the maturity of the tool, we show the steps with the tool to answer one correlation question related to a health-care process. The answer to a second question is shown in the screencast accompanying this paper

A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing

Thanks to the advances in the Internet of Things (IoT), Condition-based Maintenance (CBM) has progressively become one of the most renowned strategies to mitigate the risk arising from failures. Within any CBM framework, non-linear correlation among data and variability of condition monitoring data sources are among the main reasons that lead to a complex estimation of Reliability Indicators (RIs). Indeed, most classic approaches fail to fully consider these aspects. This work presents a novel methodology that employs Accelerated Life Testing (ALT) as multiple sources of data to define the impact of relevant PVs on RIs, and subsequently, plan maintenance actions through an online reliability estimation. For this purpose, a Generalized Linear Model (GLM) is exploited to model the relationship between PVs and an RI, while a Hierarchical Bayesian Regression (HBR) is implemented to estimate the parameters of the GLM. The HBR can deal with the aforementioned uncertainties, allowing to get a better explanation of the correlation of PVs. We considered a numerical example that exploits five distinct operating conditions for ALT as a case study. The developed methodology provides asset managers a solid tool to estimate online reliability and plan maintenance actions as soon as a given condition is reached.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Ship Design, Production and Operation

Discovering, analyzing and enhancing BPMN models using ProM

Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners