Tamás Mészáros

Supporting domain-specific model patterns with metamodeling

Metamodeling is a widely applied technique in the field of graphical languages to create highly configurable modeling environments. These environments support the rapid development of domain-specific modeling languages (DSMLs). Design patterns are efficient solutions for recurring problems. With the proliferation of DSMLs, there is a need for domain-specific design patterns to offer solutions to problems recurring in different domains. The aim of this paper is to provide theoretical and practical foundations to support domain-specific model patterns in metamodeling environments. In order to support the treatment of premature model parts, we weaken the instantiation relationship. We provide constructs relaxing the instantiation rules, and we show that these constructs are appropriate and sufficient to express patterns. We provide the necessary modifications in metamodeling tools for supporting patterns. With the contributed results, a well-founded domain-specific model pattern support can be realized in metamodeling tools.

Manual and automated performance optimization of model transformation systems

Model-based development is one of the most promising solutions for several problems of industrial software engineering. Graph transformation is a proven method for processing domain-specific models. However, in order to be used by domain experts without graph transformation experts, it must be fast even if not tweaked for speed manually based on knowledge available only to the implementers of the transformation system. In this paper, we compare the performance of such manual optimizations with a solution using automated optimization based on sharing of matches between overlapping left-hand-sides of sequentially independent rules. This yields a 11% improvement in our scenario, although our prototypical implementation only exploits overlapping between, at most, two rules, and the analyzed benchmark does not contain many cases where the optimization is applicable.

An ontology-based information retrieval system

Authors describe a general architecture and a prototype application for the concise storage and presentation of the information retrieved from a wide spectrum of information sources. The proposed architecture was influenced by particular challenges of knowledge intensive domain, mining the knowledge content of primarily unstructured textual information, demands for context driven, multi-faceted, up-to-date query and presentation of the required information, and by the intricacies of the Hungarian language, calling for special solutions to a number of linguistic problems.

Annotated Bayesian networks: a tool to integrate textual and probabilistic medical knowledge

We have previously (2000) reported on the development of Bayesian network models for the pre-operative discrimination between malignant and benign ovarian masses. The models incorporated both medical background knowledge and patient data, which required the traceability of the incorporated prior medical knowledge. For this purpose, we followed a particular annotation method for Bayesian networks using a dedicated representation. In this paper, we present the resulting annotated Bayesian network (ABN) representation that consists of a regular Bayesian network, with standard probabilistic semantics, and a corresponding semantic network, to which textual information sources are attached. We demonstrate the applicability of such a dual model to represent both the rigorous probabilistic and the unconstrained textual medical knowledge. We describe methods on how these ABN models can be used: (1) as a domain model to arrange the personal textual information of a clinician according to the semantics of the domain, (2) in decision support to provide detailed (and even personalized) explanation, and (3) to enhance the information retrieval to find new textual information more efficiently.

Towards truly parallel model transformations: A distributed pattern matching approach

In the recent years, software modeling became an essential part of designing large software systems. However, efficient model processing techniques are required, unless we use models only for documentation purposes. One way to improve efficiency is to apply model transformations in parallel. This paper introduces a truly parallel model transformation framework focusing on a new pattern matching approach. By using this new approach, the framework is now scalable and much more efficient than any parallel model transformation approach before.

Building an Information and Knowledge Fusion System

In this paper authors present a system for information and knowledge fusion that provides an integrated management of information in a particular task domain. The proposed system uses structured (database and XML-based) and unstructured (information retrieval) data acquisition techniques, various knowledge representation schemes to integrate retrieved information, and customisable reports generated based on profiles supplied by the end user. This paper concentrates on modelling the problem domain, information and knowledge fusion methods, and technology fields used in the proposed system.

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 flexible, declarative presentation framework for domain-specific modeling

Domain-Specific Modeling has gained increasing popularity in software modeling. Domain-Specific Modeling Languages can simplify the design and the implementation of systems in various domains. Consequent domain specific visualization helps to understand the models for domain specialists. However, the efficiency of domain-specific modeling is often determined by the limited capabilities -- i.e. the lack of interactive design elements, low customization facilities -- of the editor applications.n This paper introduces the Presentation Framework of Visual Modeling and Transformation System, the framework provides a flexible environment for model visualization and provides a declarative solution for appearance description as well.

Domain knowledge based information retrieval language: an application of annotated Bayesian networks in ovarian cancer domain

The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. P. Antal et al. (2001) proposed the application of so-called annotated Bayesian networks (ABNs), which are textually-enriched probabilistic domain models that help knowledge engineers and medical experts to find and organize the information that is necessary in model-building. In this paper, we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language on the one hand provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.

Will measurement instruments turn into agents

Authors investigate implications of agent based software engineering and Internet environment in measurement practice. Measurement minded expert agents and instrument-as-agent approach offer numerous advantages. However, this approach would require reconsideration of certain properties of the measuring equipment and the role of the system controller.