1,079 research outputs found
P ORTOLAN: a Model-Driven Cartography Framework
Processing large amounts of data to extract useful information is an
essential task within companies. To help in this task, visualization techniques
have been commonly used due to their capacity to present data in synthesized
views, easier to understand and manage. However, achieving the right
visualization display for a data set is a complex cartography process that
involves several transformation steps to adapt the (domain) data to the
(visualization) data format expected by visualization tools. To maximize the
benefits of visualization we propose Portolan, a generic model-driven
cartography framework that facilitates the discovery of the data to visualize,
the specification of view definitions for that data and the transformations to
bridge the gap with the visualization tools. Our approach has been implemented
on top of the Eclipse EMF modeling framework and validated on three different
use cases
ooDACE toolbox: a flexible object-oriented Kriging implementation
When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensive research and many extensions have been proposed, e.g., co-Kriging, stochastic Kriging, blind Kriging, etc. However, few Kriging implementations are publicly available and tailored towards scientists and engineers. Furthermore, no Kriging toolbox exists that unifies several Kriging flavors. This paper addresses this need by presenting an efficient object-oriented Kriging implementation and several Kriging extensions, providing a flexible and easily extendable framework to test and implement new Kriging flavors while reusing as much code as possible
Code Generation with the Model Transformation of Visual Behavior Models
There exist numerous techniques to define the abstract and the concrete syntax of metamodeled languages. However, only a few solutions are available to describe the dynamic behavior (animation) of visual languages. The aim of our research is to provide visual modeling techniques to define the dynamic behavior of the languages. Previously, we have created languages to describe animation. In this paper, we describe how these models can be processed by model transformation techniques. We elaborate the main steps of the transformation and show the details as well. We use graph rewriting-based model transformation, therefore we provide a highly generic solution which can be easily modified, and analyzed with the techniques borrowed from the field of graph rewriting. The termination analysis for the presented method is also provided
A Framework for Teaching Conceptual Modeling and Metamodeling Based on Bloomâs Revised Taxonomy of Educational Objectives
Conceptual modeling and metamodeling are vital parts in computer and information science study programs at tertiary institutions. Currently, teachers are struggling in ensuring that their teaching approach is comprehensive and in identifying application domains that motivate students, and show that the value of models exceeds pure representative means. This paper uses Bloomâs revised taxonomy of educational objectives as a foundation to define a framework for comprehensive teaching of conceptual modeling and metamodeling. The introduced framework is used to evaluate the comprehensiveness of a Smart City teaching case which has been taught at the Next-generation Enterprise: Modeling in the Digital Age Summer School. The contribution of this paper is threefold: First, a generic framework for comprehensive teaching of conceptual modeling and metamodeling is proposed; Second, a Smart City teaching case is reported; Third, the evaluation of the teaching case leads to a discussion on how to improve teaching of conceptual modeling and metamodeling in the future
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
06501 Abstracts Collection -- Practical Approaches to Multi-Objective Optimization
From 10.12.06 to 15.12.06, the Dagstuhl Seminar 06501 ``Practical Approaches to Multi-Objective Optimization\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Enriching Linked Data with Semantics from Domain-Specific Diagrammatic Models
One key driver of the Linked Data paradigm is the ability to lift data graphs from legacy systems by employing various adapters and RDFizers (e.g., D2RQ for relational databases, XLWrap for spreadsheets). Such approaches aim towards removing boundaries of enterprise data silos by opening them to cross-organizational linking within a âWeb of Dataâ. An insufficiently tapped source of machine-readable semantics is the underlying graph nature of diagrammatic conceptual models â a kind of information that is richer compared to what is typically lifted from table schemata, especially when a domain-specific modeling language is employed. The paper advocates an approach to Linked Data enrichment based on a diagrammatic model RDFizer originally developed in the context of the ComVantage FP7 research project. A minimal but illustrative example is provided from which arguments will be generalized, leading to a proposed vision of âconceptual modelâ-aware information systems
- âŠ