Using Strategic Business Process Architecture Models to Create a Process Architecture Reference for the Healthcare Industry

Strategic Business Process Architecture (SBPA) models identify the key elements and their relationships that can be used to document, design and improve operational processes across any process type or industry. The critical SBPA process architecture elements was previously combined with a traditional process map to develop a novel process architecture mapping tool enabling the capture of important elements needed to design streamlined processes. The goal of this research project is to apply the process architecture meta models and the process architecture mapping tool to the healthcare industry, and subsequently develop a standard healthcare process architecture reference model. The process architecture reference model can be used by healthcare organizations as a basis for process management, including to document, design and improve their processes to provide excellent patient care.https://ecommons.udayton.edu/stander_posters/2392/thumbnail.jp

Process of Mapping between User Centric Concepts and Lyee Internal Concepts

International audienceThe overall objective of the research activity of the UP1 unit is to apply a method engineering approach to the Lyee methodology. This paper presents a formalization of the Lyee Process Model using the concept of Map. It develops also two methodological guidelines supporting (i) the mapping of the Lyee user-centric requirements, which have been previously specified using Design Patterns, into Lyee software requirements and (ii) the optimization of the latter. The motivation is the search for efficiency and effectiveness in the formulation of requirements in accordance with the two levels Lyee Product Meta-Model. The pay-off will be a more productive process of requirements formulation and a better quality result

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

An implementation of the behavior annex in the AADL-toolset Osate2

AADL is a modeling language to design and analyze High-Integrity Distributed and Real-time systems. Embedded sub-languages published as AADL annexes extend an AADL model to enhance analysis. The behavior annex specifies the behavior of an AADL application model. An implantation of this annex allows to perform behavior analysis. In addition, as there are several AADL annexes, the implementation of generic mechanisms to support each one of them is challenging. The behavior annex is a valid candidate to illustrate these challenges by combining several sub-languages. In this paper we expose our experiment to support the behavior annex in the reference AADL toolset OSATE2. This one, supports the AADL version 2 by providing a front-end and a set of analysis plug-ins to analyze an AADL model

ProspectBa - Platform for Collaborative Exploration of Product Innovation Opportunities

This paper presents the design research based development of the ProspectBa platform, consisting of method and related tools for identifying future product innovation opportunities in an unfamiliar domain. The ProspectBa platform is a resource for exploring future business areas. It is also a solution to the common issue of distributed cognition in cross-organization innovation networks. The diverse body of knowledge about a domain, produced with the platform, has versatile application in the Front-End of product innovation and serves in the renewal of core capabilities. The three main platform components are the Prospect Mapping method, the Prospect Map system model and tool, and the online ProspectBa Studio blog. The Prospect Mapping method combines systematic design methods with a systems approach and scenario building for designing alternative systems of solutions to potential future needs in a domain. The Prospect Map system model defines design parameters and variables for creating a domain system image used in prospecting future needs and composing design briefs. The system model functions as a shared conceptualization of the domain; in this capacity it supports an emergent common understanding of opportunities among platform users. Common understanding is further supported by the systematic externalization of domain parameters and variables to the Prospect Map tool. The ProspectBa Studio blog augments the Prospect Map tool, enabling archiving, annotating and sharing its contents online. The adaptive design-based Prospect Mapping process produces meta-foresight about the impact of trends, macro-foresight about the impact of future concepts and micro-foresight about how to exploit identified opportunities. These outcomes are founded on the domain system image, which systematizes the monitoring of their relevance and simplifies new knowledge-creation. Ultimately, the platform is a potential unifying solution to the heterogeneous Front-End of product innovation

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

SiSeRHMap v1.0: A simulator for mapped seismic response using a hybrid model

SiSeRHMap is a computerized methodology capable of drawing up prediction maps of seismic response. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches 5 and models are organized in a code-architecture composed of five interdependent modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A metamodeling process confers a hybrid nature to the methodology. In this process, the one-dimensional linear 10 equivalent analysis produces acceleration response spectra of shear wave velocitythickness profiles, defined as trainers, which are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm (EA) and the Levenberg–Marquardt Algorithm (LMA) as the final optimizer. In the fi15 nal step, the GCM Maps Executor module produces a serial map-set of a stratigraphic seismic response at different periods, grid-solving the calibrated Spectra model. In addition, the spectra topographic amplification is also computed by means of a numerical prediction model. This latter is built to match the results of the numerical simulations related to isolate reliefs using GIS topographic attributes. In this way, different sets 20 of seismic response maps are developed, on which, also maps of seismic design response spectra are defined by means of an enveloping technique

A critical rationalist approach to organizational learning: testing the theories held by managers

The common wisdom is that Popper's critical rationalism, a method aimed at knowledge validation through falsification of theories, is inadequate for managers in organizations. This study falsifies this argument in three phases: first, it specifies the obstructers that prevent the method from being employed; second, the critical rationalist method is adapted for strategic management purposes; last, the method and the hypotheses are tested via action research. Conclusions are that once the obstructers are omitted the method is applicable and effective

Enhancing geographical information systems capabilities with multi-criteria evaluation functions

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