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Astract: Ontology and schema matching are well established techniques, which have been applied in various integration scenarios, e.g., web service composition and database integration. Consequently, matching tools enabling automatic matching of various kinds of schemas with various matching techniques are available. In the field of model-driven engineering, in contrast to schema and ontology integration, the in- tegration of modeling languages relies on manual tasks such as writing model trans- formation code, which is tedious and error-prone. Therefore, we propose the applica- tion of ontology and schema matching techniques for automatically exploring seman- tic correspondences between metamodels, which are currently the modeling language definitions of choice. The main focus of this paper is on reporting preliminary results and lessons learned by evaluating currently available ontology matching tools for their metamodel matching potential.

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Astract: Current model-driven Web Engineering approaches (such as OO-H, UWE or WebML) provide a set of methods and supporting tools for a systematic design and development of Web applications. Each method addresses different concerns using separate models (content, navigation, presentation, business logic, etc.), and provide model compilers that produce most of the logic and Web pages of the application from these models. However, these proposals also have some limitations, especially for exchanging models or representing further modeling concerns, such as architectural styles, technology independence, or distribution. A possible solution to these issues is provided by making model-driven Web Engineering proposals interoperate, being able to complement each other, and to exchange models between the different tools. MDWEnet is a recent initiative started by a small group of researchers working on model-driven Web Engineering (MDWE). Its goal is to improve current practices and tools for the model-driven development of Web applications for better interoperability. The proposal is based on the strengths of current model-driven Web Engineering methods, and the existing experience and knowledge in the field. This paper presents the background, motivation, scope, and objectives of MDWEnet. Furthermore, it reports on the MDWEnet results and achievements so far, and its future plan of actions.

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Astract: The definition of modeling languages is a key-prerequisite for model-driven engineering (MDE). In this respect, domain-specific languages (DSL) defined in terms of metamodels and UML profiles are often considered as two alternatives. For interoperability reasons, however, the need arises to bridge modeling languages originally defined as DSLs to UML profiles by defining (1) a specific UML profile to represent the domain-specific modeling concepts in UML and (2) model transformations for transforming DSL models to UML models and vice versa. A manual definition of a UML profile typically is a tedious and errorprone task, but contains a high potential for automation. The contribution of this paper is to integrate the so far competing worlds of DSLs and UML. We report on our semi-automatic approach based on the manual mapping of domain-specific metamodels and UML using a dedicated bridging language as well as the automatic generation of UML profiles and model transformations. We present our ideas within a case study for bridging ComputerAssociate´s DSL of the AllFusion Gen CASE tool with IBM´s Rational Software Modeler for UML.

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Keywords: high performance computing, parallel computing, financial management, derivatives pricing, path dependent instruments, portfolio optimization, stochastic programming, asynchronous algorithms, benders decomposition, parallel programming model
Astract: High Performance Computing is useful in the field of finance for solving problems which are defined on models of financial variables in the form of sequences of scenarios along with their realization probabilities. Both the evolution of stock prices and interest rates is frequently described in this manner. This work deals with the two problem classes of determining prices of financial instruments, and of determining optimal portfolios of assets, with respect to some objective function and constraints. Dynamic optimization techniques allow for multiple planning periods, whereas stochastic dynamic optimization problems take into account also probabilities and exhibit (exponentially growing) tree structures, which can become very large. Computation times for solving these problems can extend to hours and days, hence high performance computing techniques of achieving speed up are desirable.
The major approach for performance improvement in this work is parallel computing. It includes the parallel implementation of Monte Carlo simulation techniques as well as of backward induction methods for pricing path dependent interest rate derivatives, in particular constant maturity floaters with embedded options. In the optimization part, the nested Benders decomposition method of multistage stochastic optimization has been parallelized in a synchronous as well as in an asynchronous version. The parallel implementations obtain speedups ranging from reasonable to excellent and demonstrate the potential of high performance computing for financial applications. In addition, they served as case studies in the development of software tools for high performance computing within the framework of the Special Research Program No. F011 AURORA "Advanced Models, Applications and Software Systems for High Performance Computing" of the Austrian Science Fund (FWF).
The data parallel programming language HPF+, with extensions for clusters of SMPs, has been successfully employed in the implementation of pricing algorithms. A path notation has been specified as an extension to Fortran 95, allowing for the high level formulation of parallel algorithms operating on lattice structures. The parallel programming model of a distributed active tree has been designed and implemented on top of Java's threads and RMI. Parallel implementations of the nested Benders decomposition algorithm in Java demonstrate that this is a suitable language for high performance computing. The OpusJava component framework, as well as the JavaSymphony class library, and the distributed active tree model proved their usefulness as programming support environments in the implementation of parallel tree structured algorithms.
In addition to the parallelization of sequential existing algorithms, the improvement of known parallelization approaches, and the use of specialized parallel programming languages and programming models, an increase in performance has been achieved by algorithmic developments.
The generalization of the classical backward induction method allows for the faster calculation, i.e., in linear instead of exponential time, of prices of a class of instruments exhibiting "limited" path dependence, demonstrating that highly effective approaches of performance improvement combine the levels of algorithms and parallel implementation.