Multilevel Modeling

Domain-specific modeling languages (DSMLs) promise clear advantages over general-purpose modeling languages. However, their design poses a fundamental challenge. While economies of scale advocate the development of DSMLs that can be used in a wide range of cases, modeling productivity demands more specific language concepts tuned to individual requirements. Inspired by the actual use of technical languages (German: “Fachsprachen”), this paper presents a novel multilevel modeling approach to conceptual modeling and to the design of information systems. Unlike traditional language architectures such as Meta Object Facility (MOF), it features a recursive architecture that allows for an arbitrary number of classification levels and, hence, for the design of hierarchies of DSMLs ranging from reference DSMLs to “local” DSMLs. It can not only diminish the conflict inherent in designing DSMLs, but enables the reuse and integration of software artifacts in general. It also helps reduce modeling complexity by relaxing the rigid dichotomy between specialization and instantiation. Furthermore, it integrates a meta-modeling language with a metamodel of a reflective meta-programming language, thereby allowing for executable models. The specification of the language architecture is supplemented by the description of use scenarios that illustrate the potential of multilevel modeling and a critical discussion of its peculiarities

Study protocol: SWING – social capital and well-being in neighborhoods in Ghent

Background: Investing in social capital has been put forth as a potential lever for policy action to tackle health inequity. Notwithstanding, empirical evidence that supports social capital's role in the existence of health inequity is limited and inconclusive. Furthermore, social capital literature experiences important challenges with regard to (1) the level on which social capital is measured and analyzed; (2) the measurement of the concept in line with its multidimensional nature; and (3) the cross-cultural validity of social capital measurements. The Social capital and Well-being In Neighborhoods in Ghent (SWING) study is designed to meet these challenges. The collected data can be used to investigate the distribution of health problems and the association between social capital, health and well-being, both at the individual and at the neighborhood level. The main goals of the SWING study are (1) to develop a coherent multilevel dataset of indicators on individual and neighborhood social capital and well-being that contains independent indicators of neighborhood social capital at a low level of aggregation and (2) to measure social capital as a multidimensional concept. The current article describes the background and design of the SWING study. Methods/Design: The SWING study started in 2011 and data were collected in three cross-sectional waves: the first in 2011, the second in 2012, and the third in 2013. Data collection took place in 142 neighborhoods (census tract level) in the city of Ghent (Flanders, Belgium). Multiple methods of data collection were used within each wave, including: (1) a standardized questionnaire, largely administered face-to-face interviews for neighborhood inhabitants (N = 2,730); (2) face-to-face interviews with key informants using a standardized questionnaire (N = 2,531); and (3) an observation checklist completed by the interviewers (N = 2,730 in total). The gathered data are complemented by data available within administrative data services. Discussion: The opportunities and ambitions of the SWING study are discussed, together with the limitations of the database

Workplace design

Purpose: Although both the job and its broader context are likely to drive motivation, little is known about the specific workplace characteristics that are important for motivation. We present the Workplace Characteristics Model, which describes the workplace characteristics that can foster motivation, and the corresponding multilevel Workplace Design Questionnaire. Design/methodology/approach: The model is configured as nine workplace attributes describing climate for motivation at two levels, psychological and organizational. The multilevel multi-time questionnaire was validated with data from 4287 individuals and 212 workplaces and integrated regulation as the criterion outcome. Findings: Multilevel factor analysis and regression indicated good internal reliability, construct validity, and stability over time, and excellent concurrent and predictive validity of the questionnaire. Research/Practical implications: The model could help to optimize job and workplace design by contextualizing motivation. The questionnaire offers advancement over single-level climate measures as it is validated simultaneously at two levels. Further research should focus on overcoming the low response rate typical for online surveys, on need fulfillment as the mediating variable, and on the joint influence of job and workplace characteristics on organizational behavior. Originality/value: This work responds to calls to incorporate context in research into organizational behavior and job design. An understanding of the workplace is a first step in this direction. The questionnaire is the first to be validated at multiple levels of analysis. Ultimately, workplace design could support job design and the development of inherently motivating workplaces

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Extensibility of Enterprise Modelling Languages

Die Arbeit adressiert insgesamt drei Forschungsschwerpunkte. Der erste Schwerpunkt setzt sich mit zu entwickelnden BPMN-Erweiterungen auseinander und stellt deren methodische Implikationen im Rahmen der bestehenden Sprachstandards dar. Dies umfasst zum einen ganz konkrete Spracherweiterungen wie z. B. BPMN4CP, eine BPMN-Erweiterung zur multi-perspektivischen Modellierung von klinischen Behandlungspfaden. Zum anderen betrifft dieser Teil auch modellierungsmethodische Konsequenzen, um parallel sowohl die zugrunde liegende Sprache (d. h. das BPMN-Metamodell) als auch die Methode zur Erweiterungsentwicklung zu verbessern und somit den festgestellten Unzulänglichkeiten zu begegnen. Der zweite Schwerpunkt adressiert die Untersuchung von sprachunabhängigen Fragen der Erweiterbarkeit, welche sich entweder während der Bearbeitung des ersten Teils ergeben haben oder aus dessen Ergebnissen induktiv geschlossen wurden. Der Forschungsschwerpunkt fokussiert dabei insbesondere eine Konsolidierung bestehender Terminologien, die Beschreibung generisch anwendbarer Erweiterungsmechanismen sowie die nutzerorientierte Analyse eines potentiellen Erweiterungsbedarfs. Dieser Teil bereitet somit die Entwicklung einer generischen Erweiterungsmethode grundlegend vor. Hierzu zählt auch die fundamentale Auseinandersetzung mit Unternehmensmodellierungssprachen generell, da nur eine ganzheitliche, widerspruchsfreie und integrierte Sprachdefinition Erweiterungen überhaupt ermöglichen und gelingen lassen kann. Dies betrifft beispielsweise die Spezifikation der intendierten Semantik einer Sprache

Towards a Security, Privacy, Dependability, Interoperability Framework for the Internet of Things

A popular application of ambient intelligence systems constitutes of assisting living services on smart buildings. As intelligence is imported in embedded equipment, the system becomes able to provide smart services (e.g. control lights, airconditioning, provide energy management services etc.). IoT is the main enabler of such environments. However, the interconnection of these cyber-physical systems and the processing of personal data raise serious security and privacy issues. In this paper we present a framework that can guarantee Security, Privacy, Dependability and Interoperability (SPDI) in IoT. Taking advantage of the underlying IoT deployment, the proposed framework not only implements the requested smart functionality but also provide modelling and administration that can guarantee those SPDI properties. Moreover, we provide an application example of the framework in a smart building scenario

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology