Expressing Measurement Uncertainty in OCL/UML Datatypes

Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, di cult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations de ned for the values of these types.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Inter-modelling: From Theory to Practice

Proocedings of: ACM/IEEE 13 th International Conference on Model Driven Engineering Languages and Systems. Oslo, Norway, October 3-8, 2010.We define inter-modelling as the activity of building models that describe how modelling languages should be related. This includes many common activities in Model Driven Engineering, like the specification of model-to-model transformations, the definition of model matching and model traceability constraints, the development of inter-model consistency maintainers and exogenous model management operators. Recently, we proposed a formal approach to specify the allowed and forbidden relations between two modelling languages by means of bidirectional declarative patterns. Such specifications were used to generate graph rewriting rules able to enforce the relations in (forward and backward) model-to-model transformation scenarios. In this paper we extend the usage of patterns for two further inter-modelling scenarios &- model matching and model traceability &- and report on an EMF-based tool implementing them. The tool allows a high-level analysis of specifications based on the theory developed so far, as well as manipulation of traces by compilation of patterns into the Epsilon Object Language.Work funded by the Spanish Ministry of Science (project TIN2008-02081 and grants JC2009-00015, PR2009-0019), the R&D programme of the Madrid Region (project S2009/TIC-1650), the European Commission’s 7th Framework programme (grant #248864 (MADES)), and the Engineering and Physical Sciences Research Council (EPSRC) (grant EP/E034853/1).Publicad

Main outcomes of the Phebus FPT1 uncertainty and sensitivity analysis in the EU-MUSA project

The Management and Uncertainties of Severe Accidents (MUSA) project was funded in HORIZON 2020 and is coordinated by CIEMAT (Spain). The project aims at consolidating a harmonized approach for the analysis of uncertainties and sensitivities associated with Severe Accidents (SAs) analysis, focusing on source term figures of merit. The Application of Uncertainty Quantification (UQ) Methods against Integral Experiments (AUQMIE – Work Package 4 (WP4)), led by ENEA (Italy), was devoted to apply and test UQ methodologies adopting the internationally recognized PHEBUS FPT1 test. FPT1 was chosen to test UQ methodologies because, even though it is a simplified SA scenario, it was representative of the in-vessel phase of a severe accident initiated by a break in the cold leg of a PWR primary circuit. WP4 served as a platform to identify and discuss the issues encountered in the application of UQ methodol ogies to SA analyses (e.g. discuss the UQ methodology, perform the coupling between the SA codes and the UQ tools, define the results post-processing methods, etc.). The purpose of this paper is to describe the MUSA PHEBUS FPT1 uncertainty application exercise with the related specifications and the methodologies used by the partners to perform the UQ exercise. The main outcomes and lessons learned of the analysis are: scripting was in general needed for the SA code and uncertainty tool coupling and to have more flexibility; particular attention should be devoted to the proper choice of the input uncertain parameters; outlier values of figures of merit should be carefully analyzed; the computational time is a key element to perform UQ in SA; the large number of uncertain input parameters may complicate the interpretation of correlation or sensitivity analysis; there is the need for a statistically solid handling of failed calculations

DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies

First outcomes from the PHEBUS FPT1 uncertainty application done in the EU MUSA project

The Management and Uncertainties of Severe Accidents (MUSA) project, founded in HORIZON 2020 and coordinated by CIEMAT (Spain), aims to consolidate a harmonized approach for the analysis of uncertainties and sensitivities associated with Severe Accidents (SAs) by focusing on Source Term (ST) Figure of Merits (FOM). In this framework, among the 7 MUSA WPs the Application of Uncertainty Quantification (UQ) Methods against Integral Experiments (AUQMIE – Work Package 4 (WP4)), led by ENEA (Italy), looked at applying and testing UQ methodologies, against the internationally recognized PHEBUS FPT1 test. Considering that FPT1 is a simplified but representative SA scenario, the main target of the WP4 is to train project partners to perform UQ for SA analyses. WP4 is also a collaborative platform for highlighting and discussing results and issues arising from the application of UQ methodologies, already used for design basis accidents, and in MUSA for SA analyses. As a consequence, WP4 application creates the technical background useful for the full plant and spent fuel pool applications planned along the MUSA project, and it also gives a first contribution for MUSA best practices and lessons learned. 16 partners from different world regions are involved in the WP4 activities. The purpose of this paper is to describe the MUSA PHEBUS FPT1 uncertainty application exercise, the methodologies used by the partners to perform the UQ exercise, and the first insights coming out from the calculation phase