NEXT: Generating tailored ERP applications from ontological enterprise models

Tailoring Enterprise Resource Planning (ERP) software to the needs of the enterprise still is a technical endeavor, often requiring the (de)activation of modules, modification of configuration files or even execution of database queries. Considering the large body of work on Enterprise Modeling and Model-Driven Software Engineering, this is remarkable: Ideally, one models one’s own enterprise and, at the press of a button, ERP software tailored to the needs of the modeled enterprise is generated. In this paper, we introduce NEXT, a novel model-driven software generation approach being developed with precisely this goal in mind. It uses the expressive power of ontological enterprise models (OEMs) to generate ERP cloud applications. An OEM only describes the real-world phenomena essential to the enterprise, using terms and customizations specific to the enterprise. We present our considerations during development of the OEM modeling language, which is designed to capture the specifics of enterprise phenomena in a way that technical details can be derived from it. We expect NEXT to drastically shorten the time-to-market of ERP software, from months–years to hours–days

NEXT: Generating Tailored ERP Applications from Ontological Enterprise Models

Part 1: Regular PapersInternational audienceTailoring Enterprise Resource Planning (ERP) software to the needs of the enterprise still is a technical endeavor, often requiring the (de)activation of modules, modification of configuration files or even execution of database queries. Considering the large body of work on Enterprise Modeling and Model-Driven Software Engineering, this is remarkable: Ideally, one models one’s own enterprise and, at the press of a button, ERP software tailored to the needs of the modeled enterprise is generated. In this paper, we introduce NEXT, a novel model-driven software generation approach being developed with precisely this goal in mind. It uses the expressive power of ontological enterprise models (OEMs) to generate ERP cloud applications. An OEM only describes the real-world phenomena essential to the enterprise, using terms and customizations specific to the enterprise. We present our considerations during development of the OEM modeling language, which is designed to capture the specifics of enterprise phenomena in a way that technical details can be derived from it. We expect NEXT to drastically shorten the time-to-market of ERP software, from months–years to hours–days

Examples of subcortical and global brain segmentation.

<p>An example of subcortical brain segmentation of the thalamus in red in coronal (A), sagittal (B), and transversal plane (C), and an example of voxel-based segmentation of an anatomical image (D) in a gray matter image (E) and a white matter image (F).</p

Flow chart of the study.

<p>Flow chart of the study.</p

Examples of spectra, background images, and ¹H Magnetic Resonance Spectroscopic Imaging grid.

<p>An example of a Hanning filtered spectrum in the occipital cortex (A) and in the hippocampus (B) and the accompanying background image plus ¹H Magnetic Resonance Spectroscopic Imaging grid (respectively C and D).</p

NEXT: Generating tailored ERP applications from ontological enterprise models

Tailoring Enterprise Resource Planning (ERP) software to the needs of the enterprise still is a technical endeavor, often requiring the (de)activation of modules, modification of configuration files or even execution of database queries. Considering the large body of work on Enterprise Modeling and Model-Driven Software Engineering, this is remarkable: Ideally, one models one’s own enterprise and, at the press of a button, ERP software tailored to the needs of the modeled enterprise is generated. In this paper, we introduce NEXT, a novel model-driven software generation approach being developed with precisely this goal in mind. It uses the expressive power of ontological enterprise models (OEMs) to generate ERP cloud applications. An OEM only describes the real-world phenomena essential to the enterprise, using terms and customizations specific to the enterprise. We present our considerations during development of the OEM modeling language, which is designed to capture the specifics of enterprise phenomena in a way that technical details can be derived from it. We expect NEXT to drastically shorten the time-to-market of ERP software, from months–years to hours–days

Magnetic resonance spectroscopic imaging and volumetric measurements of the brain in patients with postcancer fatigue: a randomized controlled trial

Contains fulltext : 127313.pdf (publisher's version ) (Open Access)Background Postcancer fatigue is a frequently occurring problem, impairing quality of life. Until now, little is known about (neuro) physiological factors determining postcancer fatigue. For non-cancer patients with chronic fatigue syndrome, certain characteristics of brain morphology and metabolism have been identified in previous studies. We investigated whether these volumetric and metabolic traits are a reflection of fatigue in general and thus also of importance for postcancer fatigue. Methods Fatigued patients were randomly assigned to either the intervention condition (cognitive behavior therapy) or the waiting list condition. Twenty-five patients in the intervention condition and fourteen patients in the waiting list condition were assessed twice, at baseline and six months later. Baseline measurements of 20 fatigued patients were compared with 20 matched non-fatigued controls. All participants had completed treatment of a malignant, solid tumor minimal one year earlier. Global brain volumes, subcortical brain volumes, metabolite tissue concentrations, and metabolite ratios were primary outcome measures. Results Volumetric and metabolic parameters were not significantly different between fatigued and non-fatigued patients. Change scores of volumetric and metabolic parameters from baseline to follow-up were not significantly different between patients in the therapy and the waiting list group. Patients in the therapy group reported a significant larger decrease in fatigue scores than patients in the waiting list group. Conclusions No relation was found between postcancer fatigue and the studied volumetric and metabolic markers. This may suggest that, although postcancer fatigue and chronic fatigue syndrome show strong resemblances as a clinical syndrome, the underlying physiology is different