Model-Driven Engineering for Constraint Database Query Evaluation

Data used in applications such as CAD, CAM or GIS are complex, but the techniques developed for their treatment and stor age are not adapted enough to their needs. Examples of these types of data are spatiotemporal, scientific, economic or industrial information, in which data has not a single value because is defined by parameters, variables, functions, equations . . .. These complex data cannot be repre sented nor evaluated with the relational algebra types, then a new, more complex, data type is needed, the Constraint type. Constraint Databases were defined and implemented in order to handle this type of constraint data. When a Constraint Database is implemented, different configura tion parameters can be set up, depending on which database manager is going to be used, which constraint programming tool is going to solve the query evaluation, or which type of constraints can be involved. When some of these parameters are changed, the implementation that supports the evaluation of queries over constraints have to be changed too. For this reason, we propose the use of Model-Driven Engineering to model the queries over Constraint Databases in an independent way of the im plementation and the techniques used to evaluate the queries.Junta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-13714Ministerio de Ciencia y Tecnología TIN2010- 21744-C02-0

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance

Transformation As Search

In model-driven engineering, model transformations are con- sidered a key element to generate and maintain consistency between re- lated models. Rule-based approaches have become a mature technology and are widely used in different application domains. However, in var- ious scenarios, these solutions still suffer from a number of limitations that stem from their injective and deterministic nature. This article pro- poses an original approach, based on non-deterministic constraint-based search engines, to define and execute bidirectional model transforma- tions and synchronizations from single specifications. Since these solely rely on basic existing modeling concepts, it does not require the intro- duction of a dedicated language. We first describe and formally define this model operation, called transformation as search, then describe a proof-of-concept implementation and discuss experiments on a reference use case in software engineering

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

AUTO-GENERATING MODELS FROM THEIR SEMANTICS AND CONSTRAINTS

poster abstractModel-Driven Engineering (MDE) facilitates building solutions in many en-terprise application domains through the systematic use of graphical lan-guages called domain-specific modeling languages (DSMLs). MDE tools, such as the Generic Modeling Environment (GME) and the Generic Eclipse Modeling System (GEMS), enable end-users to rapidly create such custom DSMLs. When DSMLs are coupled with constraint solvers, it is possible for DSML end-users to auto-generate solutions (i.e., models) based on the goals of the constraint solver (e.g., finding the optimal deployment for a set of software components given resource availability and resource needs). One requirement of using a constraint solver with a DSML, however, is that mod-elers have to create an initial model, also known as a “partial model”. This implies that it is the end-users responsibility to (1) understand how to use the DSML and (2) understand when they have created an appropriate partial model that is a candidate for completion using a constraint solver. Our research therefore focuses on addressing the two problems men-tioned above. We address the problems by analyzing the semantics and con-straints of the DSML (i.e., the meta-model). Based on our analysis, we then auto-generate as much of the model until we reach a point that requires us-er intervention. At that point, we present a set of operations (or moves) to the user and continue the process until the model is complete, or is solvable using a constraint solver

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation

Driven by a growing requirement during the 21st century for the integration of rigorous statistical analyses in engineering research, there has been a movement within the statistics and quality communities to evolve a unified statistical engineering body of knowledge (Hoerl & Snee, 2010). Systems Statistical Engineering research seeks to integrate causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer\u27s Viable System Model (S Beer, 1972; Stafford Beer, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. (Cotter & Quigley, 2018) set forth the Bayesian systemic hierarchical constraint propagation theoretical basis for modeling the amplification and attenuation effects of environmental constraints propagated into systemic variability and variety. In their theoretical development, they simplified the analysis to only deterministic constraints, which models only the effect of statistical risks of failure. Imprecision and uncertainty in the assessment of environmental constraints will induce additional variance components in systemic variability and variety. To make causal Bayesian hierarchical modeling more capable of capturing and representing the imprecise and uncertain nature of environments, we must incorporate rough or fuzzy functions and boundaries to model imprecision and grey boundaries to model uncertainty in constraint propagation at each system level to measure the overall impact on the organization variability and variety. This paper sets forth a proposed research method to incorporate rough, fuzzy, and Grey set theories into Systems Statistical Engineering causal Bayesian hierarchical constraints modeling

Management of conflict for preliminary engineering design tasks

Much of preliminary engineering design is a constraint-driven non-monotonic exploration process. Initial decisions are made when information is incomplete and many goals are contradictory. Such conditions are present regardless of whether one or several designers contribute to designs. This paper presents an approach for supporting decisions in situations of incomplete and conflicting knowledge. In particular, we use assumptions and conflict management to achieve efficient search in contexts where little reliable information exists. A knowledge representation, containing a semantic differentiation between two types of assumptions, is used within a computational model based on the dynamic constraint satisfaction paradigm. Conflict management strategies consist of three generic mechanisms adapted to the type of constraints involved. These strategies may be refined through consideration of variable importance, context, and design inerti