Coupled Transformations of Graph Structures applied to Model Migration

Model-Driven Engineering (MDE) is a relatively new paradigm in software engineering that pursues the goal to master the increased complexity of modern software products. While software applications have been developed for a specific platform in the past, today they are targeting various platforms and devices from classical desktop PCs to smart phones. In addition, they interact with other applications. To easier cope with these new requirements, software applications are specified in MDE at a high abstraction level in so called models prior to their implementation. Afterward, model transformations are used to automate recurring development tasks as well as to generate software artifacts for different runtime environments. Thereby, software artifacts are not necessarily files containing program code, they can also cover configuration files as well as machine readable input for model checking tools. However, MDE does not only address software engineering problems, it also raises new challenges. One of these new challenges is connected to the specification of modeling languages, which are used to create models. The creation of a modeling language is a creative process that requires several iterations similar to the creation of models. New requirements as well as a better understanding of the application domain result in an evolution of modeling languages over time. Models developed in an earlier version of a modeling language often needs to be co-adopted (migrated) to language changes. This migration should be automated, as migrating models manually is time consuming and error-prone. While application modelers use ad-hoc solutions to migrate their models, there is still a lack of theory to ensure well-defined migration results. This work contributes to a formalization of modeling language evolution with corresponding model migration on the basis of algebraic graph transformations that have successfully been used earlier as theoretical foundations of model transformation. The goal of this research is to develop a theory that considers the problem of modeling language evolution with corresponding model migration on a conceptual level, independent of a specific modeling framework

Well-formed Model Co-evolution with Customizable Model Migration

Model-driven engineering (MDE) is a software engineering discipline which focuses on models as the primary artifact of the software development process while programs are mainly generated by means of model-to-code transformations. In particular, modeling languages tailored to specific domains promise to increase the productivity and quality of software. Nevertheless due to e.g. evolving requirements, modeling languages evolve and existing models have to be migrated. Corresponding manual model migration is tedious and error-prone, therefore tools have been developed to (partly) automate this process. We follow the idea of considering such modeling language and model co-evolutions as related graph transformations ensuring a correct and unique typing of migrated models. In this paper, we present a general and formal construction of well-formed model migration schemes that are able to co-adapt any model of a given modeling language to a performed meta-model change. We show how appropriate model migration schemes can be constructed and discuss how they may be customized

Enforcement of Patterns by Constraint-Aware Model Transformations

Patterns are descriptions and solutions for recurring problems in software design and implementation. In this paper, some ideas towards a formal approach to the specification of patterns in model-driven engineering (MDE) is presented. The approach is based on the Diagram Predicate Framework which provides a formal approach to (meta)modelling, model transformation and model management in MDE. In particular, patterns are defined as diagrammatic specifications and constraint-aware model transformations are adapted to enforce patterns. Moreover, running examples are used to illustrate the facade design pattern in structural models

Co-Transformation of Type and Instance Graphs Supporting Merging of Types and Retyping

Algebraic graph transformation is a well-known rule-based approach to manipulate graphs that can be applied in several contexts. In this paper we use it in the context of model-driven engineering. Graph transformation rules usually specify changes to only one graph per application, however there are use cases such as model co-evolution where not only a single graph should be manipulated but also related ones. The co-transformation of type graphs together with their instance graphs has shown to be a promising approach to formalize model and meta-model co-evolution. In this paper, we extend our earlier work on co-evolution by allowing transformation rules that have less restrictions so that graph manipulations such as merging of types and retyping of graph elements are allowed

A combined measurement of cosmic growth and expansion from clusters of galaxies, the CMB and galaxy clustering

Combining galaxy cluster data from the ROSAT All-Sky Survey and the Chandra X-ray Observatory, cosmic microwave background data from the Wilkinson Microwave Anisotropy Probe, and galaxy clustering data from the WiggleZ Dark Energy Survey, the 6-degree Field Galaxy Survey and the Sloan Digital Sky Survey III, we test for consistency the cosmic growth of structure predicted by General Relativity (GR) and the cosmic expansion history predicted by the cosmological constant plus cold dark matter paradigm (LCDM). The combination of these three independent, well studied measurements of the evolution of the mean energy density and its fluctuations is able to break strong degeneracies between model parameters. We model the key properties of cosmic growth with the normalization of the matter power spectrum, sigma_8, and the cosmic growth index, gamma, and those of cosmic expansion with the mean matter density, Omega_m, the Hubble constant, H_0, and a kinematical parameter equivalent to that for the dark energy equation of state, w. For a spatially flat geometry, w=-1, and allowing for systematic uncertainties, we obtain sigma_8=0.785+-0.019 and gamma=0.570+0.064-0.063 (at the 68.3 per cent confidence level). Allowing both w and gamma to vary we find w=-0.950+0.069-0.070 and gamma=0.533+-0.080. To further tighten the constraints on the expansion parameters, we also include supernova, Cepheid variable and baryon acoustic oscillation data. For w=-1, we have gamma=0.616+-0.061. For our most general model with a free w, we measure Omega_m=0.278+0.012-0.011, H_0=70.0+-1.3 km s^-1 Mpc^-1 and w=-0.987+0.054-0.053 for the expansion parameters, and sigma_8=0.789+-0.019 and gamma=0.604+-0.078 for the growth parameters. These results are in excellent agreement with GR+LCDM (gamma~0.55; w=-1) and represent the tightest and most robust simultaneous constraint on cosmic growth and expansion to date.Comment: 14 pages, 5 figures, 1 table. Matches the accepted version for MNRAS. New sections 3 and 6 added, containing 2 new figures. Table extended. The results including BAO data have been slightly modified due to an updated BAO analysis. Conclusions unchange

The XXL Survey V: Detection of the Sunyaev-Zel'dovich effect of the Redshift 1.9 Galaxy Cluster XLSSU J021744.1-034536 with CARMA

We report the detection of the Sunyaev-Zel'dovich (SZ) effect of galaxy cluster XLSSU J021744.1-034536, using 30 GHz CARMA data. This cluster was discovered via its extended X-ray emission in the XMM-Newton Large Scale Structure survey, the precursor to the XXL survey. It has a photometrically determined redshift $z=1.91^{+0.19}_{-0.21}$, making it among the most distant clusters known, and nominally the most distant for which the SZ effect has been measured. The spherically integrated Comptonization is $Y_{500}=(3.0\pm0.4)\times 10^{-12}$, a measurement which is relatively insensitive to assumptions regarding the size and redshift of the cluster, as well as the background cosmology. Using a variety of locally calibrated cluster scaling relations extrapolated to z~2, we estimate a mass $M_{500} \sim (1$-$2)\times 10^{14}M_{sun}$ from the X-ray flux and SZ signal. The measured properties of this cluster are in good agreement with the extrapolation of an X-ray luminosity-SZ effect scaling relation calibrated from clusters discovered by the South Pole Telescope at higher masses and lower redshifts. The full XXL-CARMA sample will provide a more complete, multi-wavelength census of distant clusters in order to robustly extend the calibration of cluster scaling relations to these high redshifts.Comment: ApJ, in press. 9 pages, 4 figures, 4 table

The 6dF Galaxy Survey: z \approx 0 measurement of the growth rate and sigma_8

We present a detailed analysis of redshift-space distortions in the two-point correlation function of the 6dF Galaxy Survey (6dFGS). The K-band selected sub-sample which we employ in this study contains 81971 galaxies distributed over 17000deg^2 with an effective redshift z = 0.067. By modelling the 2D galaxy correlation function, xi(r_p,pi), we measure the parameter combination f(z)sigma_8(z) = 0.423 +/- 0.055. Alternatively, by assuming standard gravity we can break the degeneracy between sigma_8 and the galaxy bias parameter, b. Combining our data with the Hubble constant prior from Riess et al (2011), we measure sigma_8 = 0.76 +/- 0.11 and Omega_m = 0.250 +/- 0.022, consistent with constraints from other galaxy surveys and the Cosmic Microwave Background data from WMAP7. Combining our measurement of fsigma_8 with WMAP7 allows us to test the relationship between matter and gravity on cosmic scales by constraining the growth index of density fluctuations, gamma. Using only 6dFGS and WMAP7 data we find gamma = 0.547 +/- 0.088, consistent with the prediction of General Relativity. We note that because of the low effective redshift of 6dFGS our measurement of the growth rate is independent of the fiducial cosmological model (Alcock-Paczynski effect). We also show that our conclusions are not sensitive to the model adopted for non-linear redshift-space distortions. Using a Fisher matrix analysis we report predictions for constraints on fsigma_8 for the WALLABY survey and the proposed TAIPAN survey. The WALLABY survey will be able to measure fsigma_8 with a precision of 4-10%, depending on the modelling of non-linear structure formation. This is comparable to the predicted precision for the best redshift bins of the Baryon Oscillation Spectroscopic Survey (BOSS), demonstrating that low-redshift surveys have a significant role to play in future tests of dark energy and modified gravity.Comment: 17 pages, 13 figures, 1 tabl

Coupled Transformations of Graph Structures applied to Model Migration

Model-Driven Engineering (MDE) is a relatively new paradigm in software engineering that pursues the goal to master the increased complexity of modern software products. While software applications have been developed for a specific platform in the past, today they are targeting various platforms and devices from classical desktop PCs to smart phones. In addition, they interact with other applications. To easier cope with these new requirements, software applications are specified in MDE at a high abstraction level in so called models prior to their implementation. Afterward, model transformations are used to automate recurring development tasks as well as to generate software artifacts for different runtime environments. Thereby, software artifacts are not necessarily files containing program code, they can also cover configuration files as well as machine readable input for model checking tools. However, MDE does not only address software engineering problems, it also raises new challenges. One of these new challenges is connected to the specification of modeling languages, which are used to create models. The creation of a modeling language is a creative process that requires several iterations similar to the creation of models. New requirements as well as a better understanding of the application domain result in an evolution of modeling languages over time. Models developed in an earlier version of a modeling language often needs to be co-adopted (migrated) to language changes. This migration should be automated, as migrating models manually is time consuming and error-prone. While application modelers use ad-hoc solutions to migrate their models, there is still a lack of theory to ensure well-defined migration results. This work contributes to a formalization of modeling language evolution with corresponding model migration on the basis of algebraic graph transformations that have successfully been used earlier as theoretical foundations of model transformation. The goal of this research is to develop a theory that considers the problem of modeling language evolution with corresponding model migration on a conceptual level, independent of a specific modeling framework

Co-Transformation of Type and Instance Graphs Supporting Merging of Types with Retyping-Long Version- ⋆

Abstract. Algebraic graph transformation is a well known rule-based approach to manipulate graphs that can be applied in many contexts. In this paper we use it in the context of model-driven engineering (MDE). Graph transformation rules usually only describing changes of one graph, however there are use cases such as model co-evolution where not only a single graphshouldbemanipulatedbutrelatedones.Theco-transformation of type graphs together with their instance graphs has shown to be a promising approach to formalize model and meta-model co-evolution. In this paper, we extend our earlier work on co-evolution by allowing transformation rules that have less restrictions so that graph manipulations such as merging and retyping of graph elements become possible.